ALBERT

Description: 〈p〉Publication date: December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 89〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: Available online 28 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Fouad EL Ouardighi, Jeong Eun Sim, Bowon Kim〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This research investigates the impact of both horizontal and vertical competition, on the one hand, and strategy types (commitment-based 〈em〉versus〈/em〉 contingent-based equilibrium strategies), on the other hand, on the pollution accumulated by two supply chains over time. We consider a two-stage game model where two manufacturers and two retailers are involved in a wholesale price contract, in order to supply the demand over a finite time horizon. In the first stage of the game, the manufacturers set their respective optimal transfer prices. During the second stage, polluting emissions are created over time in proportion with demand, which is controlled by the retailers’ respective consumer prices. In this stage, the manufacturers are involved in emissions abatement. In this setup, we seek to identify the combination of market structure and strategy type that leads the two supply chains to generate the lowest pollution intensity and the highest level of abatement intensity.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 19 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Lili Yu, Xiuli He, Juan Zhang, Chuanyong Xu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The advertising threshold effect, which implies that advertising has little effect on sales if the level of advertising is below a certain level, has seldom been addressed in a modeling context. Inspired by the practice of horizontal cooperative advertising (HCA), this paper attempts to investigate the advertising threshold effect in a horizontal context and to explore the value of HCA programs. To investigate the advertising threshold effect in a horizontal context, we develop several advertising and pricing models that involve two horizontally related firms, where the advertising threshold effect is formulated by a two-regime sales function. We first consider the monopoly case and then extend it to a duopoly case. We further consider two HCA programs to explore the value of such programs, in which the firms share advertising expenditures to reduce their advertising cost. The findings include the following: (i) The presence of an advertising threshold can lead to sub-optimal advertising decisions. (ii) If one firm is influenced by the advertising threshold, this influence may be passed to its competitor. (iii) Although cooperative advertising is less cost effective than individual brand advertising, firms can benefit from HCA programs only when the advertising threshold is neither too high nor too low, and the synergy effect of cooperative advertising is not too low.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 16 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Chenbo Zhu, JianQiang Hu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In 2007, the Chinese aviation authority asked the five airlines that offer service between Shanghai and Beijing to form an express shuttle alliance so that tickets from one airline could be used for any flight offered by any of the other airlines. In this paper, we study the impact of such a government mandate on the competitiveness of the market and on airline operations. First we extend a competitive airline seat allocation model to include such a government mandate and provide sufficient conditions for the existence of a pure-strategy Nash equilibrium in such a model. Both our analytical and numerical results show that fewer low-fare seats would be made available in a market with the government mandate, and furthermore, revenues of individual airlines would be lower as well. We then study a seat inventory and price competition game between duopoly airlines with the government mandate and establish the existence of pure-strategy Nash equilibriums. We show that with the government mandate, the airlines would reserve more high-fare seats and raise the prices of high-fare seats. In addition, we show that the revenues of the airlines would decrease as fewer low-fare seats are offered and increase as the prices of high-fare seats are increased. We also discuss how the government mandate should be designed so that the market could operate as efficiently as possible.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 13 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Julio B. Clempner〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Airline strategic alliances result in a form of cooperation where firms can access the resources of others network members in order to create added value for their passengers. The shortcoming of this process is that each member of the network makes individual revenue management decisions to maximize its own income, resulting in a sub-optimal income for the network members.〈/p〉 〈p〉To deal with this problem, this paper suggests a resource allocation based on a transfer pricing mechanism, to cooperatively divide the revenue of a passenger between network members. The method penalizes the total time that a passenger takes for reaching the final destination. The model takes into consideration that the profit is independent of the number of available seats (with a maximum determined for each airline). The method computes the optimal transfer pricing and, at the same time, optimizes the quantity of seats (the booking limits). The solution results in a strong Nash equilibrium, which incorporate both the transfer prices and booking limits. We describe the transfer pricing process using an ergodic, finite and continuous-time Markov game model for multiple players. The revenue of each airline in the supply chain will depend on the number of flight transfers and the transit time of the passenger at the airports: the longer the time to the final destination, the lower the price. We compute a collaborative equilibrium point, useful for understanding the resulting revenue of each member of the network. For solving the game, we employ an iterative method based on a proximal approach that involves time penalization. In our final contribution, we present results from a numerical example, which validates the proposed Markov game model and measures the benefits of the transfer pricing resource allocation.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 3 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Claudia Caballini, Massimo Paolucci〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The evolving safety regulation is pushing seaports to comply with safety measures for workers performing heavy loads handling and repetitive movements. This paper proposes a risk-aware rostering approach in maritime container terminals, i.e., it addresses the rostering problem of minimizing and balancing workers’ risk in such terminals. To this end, a mixed integer mathematical programming model incorporating workforce risks is proposed, considering constraints such as the satisfaction of the workforce demand to perform the terminal operations, the worker-task compatibility and restrictions on the sequence of tasks assigned to the same worker. The model has been successfully applied to plan workforce over a six months horizon in a real container terminal located in Northern Italy, the Southern European Container Hub (SECH) in Genoa. As the workforce demand in SECH terminal is available at most two weeks in advance, a rolling horizon planning approach is devised. Experimental tests on real data provided by SECH terminal over a six months planning horizon highlight the effectiveness of the approach - the maximum monthly risk for workers is reduced by 33.9% compared to the current planning – and suitability to other container terminal contexts. Moreover, the model is applicable to a broad range of port situations, and robust enough to need little adaptation.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 3 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Ruiyue Lin, Zongxin Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Current data envelopment analysis (DEA) models with diversification cannot discriminate the performance of efficient mutual funds. Based on the directional distance function and diversification DEA models, this paper proposes two diversification super-efficiency models for discriminating the performance of efficient mutual funds on financial market. The proposed diversification super-efficiency models as well as the corresponding diversification DEA models are feasible and can deal with negative values in risk measures, transaction costs and return measures. The proposed methods generate bounded super-efficiency scores for all the funds. Under the assumption of discrete return distributions, all the models in the proposed diversification super-efficiency methods can be transformed into linear programming (LP) problems by choosing proper risk and return measures. To demonstrate the validity and practicality of the proposed diversification super-efficiency methods, we apply them to evaluate the performance of mutual funds in the American market. The empirical results show that the proposed diversification super-efficiency models can distinguish efficient funds well and the linear combination of efficient funds might be inefficient. Moreover, the backtesting results show that the proposed diversification super-efficiency models generally have a good practice value for the actual portfolio selection.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 9 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Chao Wang, Ming K Lim, Longfeng Zhao, Ming-Lang Tseng, Chen-Fu Chien, Benjamin Lev〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The aim of this study is to reflect on where 〈em〉Omega-The International Journal of Management Science〈/em〉 (〈em〉Omega〈/em〉) comes from and evaluate where 〈em〉Omega〈/em〉 currently is in a field that arguably paves the way for the challenging task of identifying future directions and trends. To achieve this goal, this study presents a bibliometric overview of 〈em〉Omega〈/em〉 over the past 40 years, from 1979 to 2018. First, this study outlines the basic bibliometric profile of 〈em〉Omega〈/em〉. Second, VOSviewer is employed to visualize the collaborative relationships that 〈em〉Omega〈/em〉 has with other journals to show how 〈em〉Omega〈/em〉 has linked to them over the past 40 years. Finally, co-occurrences of keywords are analysed and strategic diagrams are built to reveal the thematic trends in 〈em〉Omega〈/em〉. The results show that 〈em〉Omega〈/em〉 has substantial influences in the field of operational research and management science (OR&MS), with contributors from all over the world. 〈em〉Omega〈/em〉 has strong connections with other leading journals in the fields of OR&MS and engineering. The mainstream research of 〈em〉Omega〈/em〉 mainly focuses on production and manufacturing management, mathematical modelling for optimization problems, supply chain management, data envelopment analysis, multicriteria and decision making. Moreover, 〈em〉motor themes, highly developed-and-isolated themes, emerging-or-declining themes〈/em〉, and 〈em〉basic-and-transversal themes〈/em〉 are identified. The study provides strong evidence that 〈em〉Omega〈/em〉 has been a story of success, and the findings of this study will support decision making for the 〈em〉Omega〈/em〉-related scientific community.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 88〈/p〉 〈p〉Author(s): Desheng Wu, David L. Olson, Shouyang Wang〈/p〉

Description: 〈p〉Publication date: Available online 9 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Kim De Boeck, Catherine Decouttere, Nico Vandaele〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Access to immunization varies greatly across the world. In order to increase vaccine coverage, the required vaccines need to be able to reach the targeted population. However, in low- and middle-income countries, this often turns out to be a challenging task. This article provides a literature review on vaccine distribution chains in low- and middle-income countries and consists of two main parts. The first part elaborates on the characteristics and challenges inherent to such distribution chains. In order to obtain a complete overview, both quantitative and qualitative papers are included. In the second part, relevant operations research and operations management literature is structured according to seven classification criteria: decision level, methodology, part of the vaccine distribution chain modelled, uncertainties and characteristics covered, performance measures, real-life applicability, and countries and vaccines covered. Throughout these classifications, a comparison is made between the issues reported in practice and those investigated in the operations research and operations management literature. Based on this analysis, we identify trends and conclude that several gaps exist, providing a promising avenue for future research.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 2 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Boris Shnits, Illana Bendavid, Yariv N. Marmor〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉The appointment scheduling problem is well-known in the literature. The use of appointment systems has been adopted widely in many different fields, including service industries and especially healthcare.〈/p〉 〈p〉This research focuses on healthcare systems where patients arrive according to pre-assigned appointments. We consider healthcare systems with several parallel servers, where a given sequence of patients, with randomly distributed service durations and a possibility of no-shows, is to be scheduled. The aim is to minimize the end of day and increase resource utilization while a minimal probability of each appointment starting on time (quality of service) is required.〈/p〉 〈p〉We formulated the problem using mathematical programing and developed a multi-server numerical-based (MSN) algorithm to solve it. We conducted some experimental runs and checked the impact of the problem parameters on the end of day, customers’ average waiting time and the percentage of customers that waited for service. We also show how server pooling improves the above system measures. Finally, once the appointments are set, we develop a methodology to determine the shift length so as to balance overtime costs (costs of overtime hours) against undertime costs (costs of regular, unused hours).〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 1 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Jose A. Lopez Alvarez, Paul Buijs, Rogier Deluster, Leandro C. Coelho, Evrim Ursavas〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The European Union aims for a 40% reduction in greenhouse gas emissions by 2030, compared to 1990 levels, and recognizes the opportunities of Liquefied Natural Gas (LNG) as an alternative fuel for transportation to reach this goal. The lack of a mature supply chain for LNG as a fuel results in a need to invest in new (satellite) terminals, bunker barges and tanker trucks. This network design problem can be defined as a Two-Echelon Capacitated Location Routing Problem with Split Deliveries (2E-CLRPSP). An important feature of this problem is that direct deliveries are allowed from terminals, which makes the problem much harder to solve than the existing location routing literature suggests. In this paper, we improve the performance of a hybrid exact algorithm and apply our algorithm to a real-world network design problem related to the expansion of the European supply chain for LNG as a fuel. We show that satellite terminals and bunker barges become an interesting option when demand for LNG grows and occurs further away from the import terminal. In those situations, the large investments associated with LNG satellites and bunker barges are offset by reductions in operational costs of the LNG tanker trucks.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 1 November 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Shih-Sian (Sherwin) Jhang, Joseph P. Ogden, Nallan C. Suresh〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper develops and tests a stylized model of a manufacturing firm's operational and financial configuration that integrates the effects of market competition and integration abilities of firms. Market power is predicted to drive trade credit balances of suppliers and customers, capital structure, and firm value. These predicted relationships are tested using data for publicly traded U.S. manufacturing firms for the period 1984–2014. A two-step procedure is adopted wherein, in the first step, the model variables of profitability, asset turnover, inventory, and cash are subjected to a factor analysis to determine the existence of a common factor based on theoretical arguments. In the second step, scores of the major factor (MPscore) are used in regressions with accounts receivable, accounts payable, market leverage, and Tobin's 〈em〉Q〈/em〉 as alternative dependent variables. Evidence from cross-sectional and time series analyses provides strong support for the hypothesized relationships. Market power is not otherwise explained by firm size and asset tangibility. Additional results indicate that U.S. manufacturers generally increased their market power over this timeframe. We also find that firms with high market power tend to have higher survival rates.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 2 November 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Robert W. Hanks, Brian J. Lunday, Jeffery D. Weir〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Robust goal programming (RGP) is a recently developed, powerful new optimization modeling technique that conjoins two widely accepted operations research disciplines: robust optimization (RO) and goal programming (GP). In lieu of applying a probability distribution over possible outcomes, an approach considered by stochastic programming, RO utilizes uncertainty sets to account for data uncertainty. This characteristic of RO is an important attribute because identifying such a probability distribution is challenging, at best. Given this RO context, RGP additionally incorporates GP, traditionally a deterministic procedure, to address optimization problems having multiple objectives. As such, RGP has potential to help address a wide array of data-driven applications, ranging from financial management to engineering design.〈/p〉 〈p〉As a motivating use case for the utility of an RGP approach, this paper demonstrates the applicability of RGP by way of the data-driven United States Transportation Command (USTRANSCOM) liner rate setting problem. USTRANSCOM is responsible for the technical direction and supervision of over $7 billion [1] of annual passenger, cargo, mobility, and personal property movements in support of the Department of Defense (DoD). Transporting people and material with both organic and contracted assets, USTRANSCOM supports DoD organizations and agencies on a reimbursable basis, annually setting and charging rates for air and liner (i.e., sea) transport for their customers and reimbursing the transportation providers accordingly. The Cost Recovery Branch within TCJ8, the Financial Management and Program Analysis staff organization for USTRANSCOM, annually sets liner shipping rates specific to each combination of origin, destination, commodity type, booking terms, and container size for the upcoming fiscal year (FY). As a government entity, USTRANSCOM seeks to neither make a profit nor operate at a loss in any given FY. The current rate setting methodology assumes existing data is deterministic, resulting in process inaccuracies that contribute to unexpected surpluses or deficits each FY. Moreover, the current method fails to consider an additional USTRANSCOM objective: meeting customer's expectations that liner rates will change annually in accordance with industry-specific inflation. Considering the different goals and inherent parametric variance, the use case herein incorporates a decision maker's risk preference regarding parametric variability via 〈em〉a priori〈/em〉 analysis to inform RGP techniques and improve the USTRANSCOM liner rate setting process.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 12 September 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Leonie Hutter, Florian Jaehn, Simone Neumann〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The topic of airplane boarding is receiving increasing attention in practice and in the scientific literature. Shorter boarding times can reduce the time an airplane spends at the gate (the airplane turn-around time), resulting in annual cost savings of several hundred thousand dollars per airplane. Although several researchers have analyzed the boarding process purely theoretically or with simulation models, little empirical research has been performed, even though empirical research is the basis for any theoretical or simulation model. In this paper, we provide the fundamentals for this research area by presenting the results of an empirical study conducted at a large European airport. The aim of this study is to determine whether and to what extent certain factors, such as the number of passengers, the capacity of the airplane, and the amount of carry-on baggage, influence boarding times. Boarding times and additional data for short- and medium-haul flights with single-aisle airplanes have been manually collected in a field study and analyzed. The analyses yield the counter-intuitive result that a significant effect on the boarding time of a flight by the average amount of carry-on baggage per passenger cannot be demonstrated. Finally, we develop a regression model to predict boarding times based on the number of passengers and the capacity of the airplane. This straightforward model explains more than 85% of the variance in the boarding time and could therefore easily be used in the daily business of an airline to estimate the expected boarding times per flight. Furthermore, we compare our regression model to various simulation and analytical models as well as other empirical data for validation and out-of-sample testing.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 82〈/p〉 〈p〉Author(s): Jin Li, Victor Shi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A conventional wisdom in industry and academia is that firms suffer from decentralized procurement. In this paper, we demonstrate an important and counter-intuitive benefit of procurement decentralization in a common setting where a firm with multiple divisions procures a durable good from a supplier. We start with a two-period model and obtain analytic equilibrium results on the supplier's wholesale prices, and the firm's procurement quantities and profits under procurement centralization and decentralization. These results show that the firm's profit will benefit from decentralization if and only if the product is durable. We further show that the profit improvement always increases in durability and the number of divisions. To generalize the basic model with two periods, we design an iterative algorithm to compute the equilibrium results for any number of periods. Our extensive numerical simulations show the robustness of our analytic results and managerial insights.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 10 August 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Amir Shabani, Franco Visani, Paolo Barbieri, Wout Dullaert, Daniele Vigo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Total cost of ownership (TCO) is a management accounting technique that evaluates the total cost of a business partnership using a time-consuming activity-based costing procedure. Studies have suggested that TCO-based data envelopment analysis (DEA) can effectively estimate the results of TCO with substantially less effort and time; however, its adoption in practice is limited due to certain shortcomings. First, managers struggle to understand and accept the uncommon weighting schemes of existing TCO-based DEA models because traditional TCO analyses require a common set of weights. Second, both the traditional TCO approach and TCO-based DEA models are designed to handle precise data, whereas TCO analyses often involve imprecise data from conflicting data sources and estimations.〈/p〉 〈p〉To address the managerial and technical issues of handling weighting schemes and imprecise data, this paper proposes a novel TCO-based model: common set of weights imprecise DEA (CSW-IDEA). We validate the proposed methodology using real-life datasets from 175 suppliers that serve five key components to two multinational mechanical manufacturers. For both precise data and imprecise data, the proposed CSW-IDEA reliably approximates traditional TCO calculations significantly better than existing TCO-based DEA. The cost savings that can be theoretically generated by applying the CSW-IDEA approach are similar to the cost savings estimated by the traditional TCO approach.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 20 August 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Nina Yan, Xiuli He, Ye Liu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study examines two supply chain financing schemes for the capital-constrained retailer: supplier finance (SF) and supplier investment (SI). SF allows a downstream capital-constrained retailer to pay partially with all the initial working capital, and delays the outstanding balance with a deferred interest rate until the end of the selling season. Under SI, the supplier invests in the capital-constrained retailer’s operations as equity and then obtains a portion of dividends in return. Considering the retailer’s aversion to loss, we comparatively analyze the retailer’s ordering decision and the supplier’s pricing decision under these two schemes. We then investigate the value of each scheme and the participation motivations for both parties. We find that loss aversion influences the participants’ decisions. When the retailer is loss-averse, she will make more conservative order decisions, and the supplier will set a higher wholesale price. The loss-averse retailer will order more under the SI than under the SF. Also, the lower the retailer’s initial working capital, the higher the benefit from pure SF or SI for both supply chain members. In particular, if the retailer is highly capital-constrained, both participants prefer SI to SF. Finally, we explore the financing portfolio of pure SF and SI. Our results show that the supplier can achieve the highest profit when offering the financing portfolio and the retailer may accept this menu.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 9 August 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Forough Enayaty-Ahangar, Chase E. Rainwater, Thomas C. Sharkey〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This research details the creation of a large-scale optimization approach for solving an application of a multi-period bilevel network interdiction problem (NIP). In this class of multi-period NIP, interdiction activities must be scheduled to minimize the cumulative maximum flow over a finite time horizon. A logic-based decomposition (LBD) approach is proposed that utilizes constraint programming to exploit the scheduling nature of this multi-period NIP. Computational results–comparing solutions obtained using the proposed approach versus traditional mixed-integer programming approach–suggest that the LBD approach is more efficient in finding solutions for medium to large problem instances.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 19 June 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Kannan Govindan, Miłosz Kadziński, Ronja Ehling, Grzegorz Miebs〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Pressure from legislation and customers has motivated companies to consider reverse logistics (RL) in their operations. Since it is a complex procedure that requires an adequate system, the recent trend consists in outsourcing RL to third-party reverse logistics providers (3PRLPs). This paper provides the background of sustainable triple bottom line theory with focus on economic, environmental, and social aspects under 3PRL concerns. The relevant sustainability criteria are used in a case study conducted in cooperation with an Indian automotive remanufacturing company. To select the most preferred service provider, we use a hybrid method combining a variant of ELECTRE I accounting for the effect of reinforced preference, the revised Simos procedure, and Stochastic Multi-criteria Acceptability Analysis. The incorporated approach exploits all parameters of an outranking model compatible with the incomplete preference information of the Decision Maker. In particular, it derives the newly defined kernel acceptability and membership indices that can be interpreted as a support given to the selection of either a particular subset of alternatives or a single option. The proposed ELECTRE-based method enriches the spectrum of multiple criteria decision analysis approaches that can be used to effectively approach the problem of the 3PRLP selection. As indicated by the extensive review presented in the paper, this application field was so far dominated by Analytic Hierarchy Process and TOPSIS, whose weaknesses can be overcome by applying the outranking methods.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 6 September 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Meysam Arvan, Behnam Fahimnia, Mohsen Reisi, Enno Siemsen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Product forecasts are a critical input into sourcing, procurement, production, inventory, logistics, finance and marketing decisions. Numerous quantitative models have been developed and applied to generate and improve product forecasts. The use of human judgement, either solely or in conjunction with quantitative models, has been well researched in the academic literature and is a popular forecasting approach in industry practice. In the context of judgemental forecasting, methods that integrate an expert's judgement into quantitative forecasting models are commonly referred to as “integrating forecasting” methods. This paper presents a systematic review of the literature of judgemental demand forecasting with a focus placed on integrating methods. We explore the role of expert opinion and contextual information and discuss the application of behaviourally informed support systems. We also provide important directions for further research in these areas.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 19 September 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Pietro De Giovanni, Georges Zaccour〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A manufacturer invests in product quality to encourage consumers who have purchased in the past to substitute their current product version with a new release. Since price deters the adoption of an upgraded quality product, consumers evaluate both the quality improvements and the new release price before deciding whether to return a good. The returns can be either voluntary (passive returns) or dependent on the firm’s controls (active returns), while the pricing strategies can be either fixed (constant intertemporal pricing) or varying over time (updated intertemporal pricing) depending on the quality improvements. By combining these two ingredients (return type and pricing policy) we formulate a two-period model in which a manufacturer invests in quality improvements and sets the product prices over time. Our results show that when consumers passively return old product versions, the manufacturer should always update its pricing strategies according to the quality improvements. However, when consumer returns are sensitive to quality improvements and price, the manufacturer can be indifferent between setting a constant or an updated pricing policy depending on the effect that quality has on returns. If the manufacturer can choose between a market in which consumer returns are passive or active, it decides according to how quality impacts the returns: When the consumers’ willingness to return according to the quality effect is negligible, the manufacturer prefers to work in a market with passive attitudes towards returns. While the choice of updating the price is always dominant from an economic point of view, it turns out to be suboptimal from an environmental perspective when the effects of quality and price on returns are balanced. When the price effect on returns also depends on the discount granted to consumers, then the discrepancy between economic and environmental returns is amplified.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 20 June 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Bayi Cheng, Huijun Zhu, Kai Li, Yongjun Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Motivated by applications in art tile manufacturing and metal working industries, we study the optimization problem with a truncated batch-position-based learning effect. In production, a set of semi-products need to be processed on a single batch facility which has a fixed capacity. Several semi-products can be processed together in one batch if their total size does not exceed the facility capacity. We consider a truncated batch-position-based learning effect which is a typical behavior of workers. During the learning period, the worker can finish the task more and more quickly because of learning effects. After the learning period, the worker reaches the best ability and the ability keeps steady. Then we consider two models of manufacturing with batch operations. In the first model, semi-products have identical sizes and we propose an optimal algorithm with time complexity of 〈em〉O〈/em〉(〈em〉n〈/em〉log 〈em〉n〈/em〉). In the second model, semi-products have arbitrary sizes which are proportional to their processing times and the model is shown to be NP-hard in the strong sense. We propose two types of learning effects including fast and slow truncated batch-position-based learning effects. Then we propose an approximation algorithm with an absolute and asymptotic worst-case ratio less than 2. Finally, we conduct computational experiments and the results show the effectiveness of our algorithms. We also provide managerial insights and detailed suggestions for decision makers of manufacturing companies based on our results.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 27 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): William A. Ellegood, Stanislaus Solomon, Jeremy North, James F. Campbell〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The school bus routing problem (SBRP) is a challenging operations research problem that has been studied by researchers for almost 50 years. SBRP publications address one or more operational sub-problems, including: bus stop selection, bus route generation, bus route scheduling, school bell time adjustment, and strategic transportation policy issues. This paper reviews 64 new SBRP research publications and analyzes them by sub-problem type, problem characteristics and solution approach. The impact of key SBRP characteristics (number of schools, mixed load, fleet mix, service environment, objective and constraints) are discussed and the different solution approaches to the SBRP are summarized by sub-problem type and methodology. We found in recent years, SBRP researchers are examining more complex real-world problem settings, adopting both evolutionary-based and trajectory-based metaheuristic solution approaches, and considering ridership and travel time uncertainty. This review documents recent trends in SBRP research and highlights research gaps and promising opportunities for future SBRP research.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 13 November 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Zoe Theocharis, Nigel Harvey〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉When people make forecasts from series of data, how does their accuracy depend on the length of the series? Previous research has produced highly conflicting findings: some work shows accuracy increases with more data; other research shows that it decreases. In two experiments, we found an inverted U-shaped relation between forecast error and series length for various series containing different patterns and noise levels: error decreased as the length of the series increased from five through 20 to 40 items but also decreased as the series length decreased from five through two to one item. We argue that, with short series, people use a simple heuristic approach to forecasting (e.g., the naïve forecast). With longer series, they extract patterns from the series and extrapolate from them to produce their forecasts. Use of heuristics is poorer but extraction of patterns is better when there are more items in the series. For series of intermediate length, neither type of strategy operates well, thereby producing the inverted U-shaped relation that we observed. Implications for unaided judgmental forecasting and for forecasting based on a combination of judgmental and statistical methods are discussed.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 12 November 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Xinyun Wu, Zhipeng Lü, Fred Glover〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper addresses a network design and traffic grooming problem arising in optical telecommunication networks that are based on wavelength division multiplexing. Given a set of nodes and a set of traffic demands between these nodes, the network design and traffic grooming problem (NDGP) consists of installing a minimum number of lightpaths between the nodes and of routing the demand on the lightpaths while respecting capacity constraints. We introduce a new mathematical formulation of the NDGP as well as a hybrid algorithm capable of finding high quality solutions in short computing times. The proposed algorithm uses linear and mixed integer programming as slave methods and embeds them within a tabu search procedure. Computational results and comparisons with an existing method from the literature show the effectiveness of the proposed algorithm. Further analyses also show the efficiency of the neighborhood structure and of its evaluation technique.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 13 November 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Silvano Martello, Michele Monaci〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We consider a variant of the multiple knapsack problem in which some assignment-type side constraints have to be satisfied. The problem finds applications in logistics sectors related, e.g., to transportation and maritime shipping. We derive upper bounds from Lagrangian and surrogate relaxations of a mathematical model of the problem. We introduce a constructive heuristic and a metaheuristic refinement. We study the computational complexity of the proposed methods and evaluate their practical performance through extensive computational experiments on benchmarks from the literature and on new sets of randomly generated instances.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 14 November 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Ramy Elitzur〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The use of data analytics has enjoyed resurgence over the last two decades in professional sports, businesses, and the government. This resurgence is attributable to Moneyball, which exposed readers to the use of advanced baseball analytics by the Oakland Athletics, and how it has resulted in improved player selection and game management. Moreover, it changed managerial vocabulary, as the term “Moneyballing” now commonly describes organizations that use data analytics. The first research question that this study examines is whether the organizational knowledge related to baseball data analytics has provided any advantage in the competitive Major League Baseball (MLB) marketplace. The second research question is whether this strategic advantage can be sustained once this proprietary organizational knowledge becomes public. First, I identify “Moneyball” teams and executives, i.e., those who rely on baseball data analytics, and track their pay/performance over time. Next, using econometric models, I analyze whether these “Moneyball” teams and GMs, have enjoyed a pay-performance advantage over the rest of MLB, and whether this advantage persists after the information becomes public.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 3 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Vandana, Arshinder Kaur〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In the business world, both the supplier and the retailer accept the credit to make their business position strong, because the credit not only strengthens their business relationships but also increases the scale of their profits. The long period of credit may increase the demand rate but simultaneously it can also increase the credit risk. This paper investigates the two-echelon supply chain model consisting of a supplier supplying a product to a single retailer, which sells this product to the end customers, under the two-level trade credit policy. The supplier offers the retailer a credit time, and the retailer also provides credit time to the end customers for settling the account. The credit time offered by the retailer is lesser than the credit time offered by the supplier, but they both are facing the default risk. Supplier decides to charge compound interest on the principal amount of the retailer if s/he fails to pay within credit time. Whereas, the retailer faces the uncertain demand from customers that may increase the chance of facing stock-out by the customers, taken as partially backlogged. The demand rate of the supplier is dependent on the two decision variables: a) the quantity of the retailer’s order; and b) the credit period offered by the supplier. The main objective of this paper is to determine the distribution-free optimal order quantity of the retailer with an optimal credit period of the supplier, which maximize the profitability of the total supply chain. To find the optimal solution mathematical formulation for the supplier and the retailer has been developed in the solution procedure. Adequate numerical example with uniform distribution has been given to justify the solution procedure. Ultimately, sensitivity analysis of the major parameters, managerial implication, with concluding remarks and future research are discussed.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 30 November 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Erbao Cao, Man Yu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Carbon emission constraints are usually treated as negative factors for enterprise operation, while this work finds that they could also be a source of profit if pledging the carbon emission permits becomes a part of the financial and operational decisions, which could significantly improve the supply chain performance and facilitate sustainability. Specifically, this paper considers an emission-dependent supply chain comprised of a supplier and a manufacturer who has limited capital and obtains the pledged loan by utilizing the carbon emission permits. By characterizing the participants’ optimal decision, some interesting observations can be achieved. The results show that the capital-constrained manufacturer makes more profit by pledging carbon emission permits to obtain a loan compared with having no access to borrowing money. The analysis reveals that the manufacturer may have several possible production regions based on the initial working capital and the pledged number of carbon emission permits. We also find that the production quantity is independent of the manufacturer's initial working capital and carbon emission permits in a no bankruptcy region, while the optimal production quantity decreases under the influence of carbon emission permits in a bankruptcy region. In addition, carbon emission abatement improves production quantity and enhances the manufacturer's profit. Moreover, we present some contracts that serve for coordinating the emission-dependent supply chain or realizing the Pareto improvement.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 2 January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Charlotte Köhler, Jan Fabian Ehmke, Ann Melissa Campbell〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In the competitive world of online retail, customers can choose from a selection of delivery time windows on a retailer’s website. Creating a set of suitable and cost-efficient delivery time windows is challenging, since customers want short time windows, but short time windows can increase delivery costs significantly. Furthermore, the acceptance of a request in a short time window can greatly restrict the ability to accommodate future requests. In this paper, we present customer acceptance mechanisms that enable flexible time window management in the booking of time-window based attended home deliveries. We build tentative delivery routes and check which time windows are feasible for each new customer request. We offer the feasible long delivery time windows and let our approaches decide when to offer short time windows. Our approaches differ in the information they consider with regard to customer characteristics as well as detailed characteristics of the evolving route plan. We perform a computational study to investigate the approaches’ ability to offer short time windows and still allow for a large number of customers to be served. We consider various demand scenarios, partially derived from real order data provided by a German online supermarket.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Liujiang Kang, Xiaoning Zhu, Huijun Sun, Jianjun Wu, Ziyou Gao, Bin Hu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Urban railway transit systems are not only the main source of city trips but also provide important support for city operations. In this study, we address the last train timetable optimization and bus bridging service problem in the context of urban railway transit networks. By exploiting problem-specific knowledge, we present an optimization-based approach that deals with the issue of last-train passengers being stranded at midnight by developing a last train and bus bridging coordination mixed integer linear programming (MILP) model. Due to the large problem size, an effective decomposition method is developed for solving the real-world and large-scale problems, which decomposes the original MILP into two smaller MILP models: maximizing last train connections and minimizing waiting times for rail-to-bus passengers. In addition, we prove that this decomposition method can solve the original MILP to global optimality. Finally, we apply the developed MILP models to the Vienna Subway to assess the effectiveness of the proposed approaches and conduct sensitivity analyses of the bus fleet size involved in the last train timetable optimization and bus bridging service problem.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Guo-liang Yang, Hirofumi Fukuyama, Kun Chen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper aims to investigate the regional sustainable performance of real estate industry of 30 sample provinces on China's mainland from 2007 to 2013, based on a slack-based data envelopment analysis (DEA) approach. We assume that the real estate industry operates with a dynamic three-stage network process. The proposed model specification is dynamic because carryover variables produced in a previous period that constrain the current production are considered. The empirical results show the major findings: (a) the growing speeds of Assets, Completed investment on land and Completed investment in houses are far faster than that of gross domestic product (GDP) in the same period, which indicates the possibility of too much existing investment; (b) the inefficiency status of the Chinese real estate industry increased after 2012; (c) there is severe imbalance among different provinces; (d) there is a need to improve the operational efficiencies of real estate industries in some provinces, especially Qinghai, Gansu, Ningxia, Xinjiang, Hainan, Guizhou, and Shanxi; and (e) GDP and Price of commercial real estate both have negative effects on slack-based inefficiency, on average, in the examined period. (f) The Herfindahl-Hirschman index and other variables can be used to explain the level and difference in inefficiency bias over time and across regions. Based on those findings, we propose several policy suggestions for sustainable development of Chinese real estate industry.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Kellie Schneider, Sarah G. Nurre〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉According to the US Department of Agriculture, more than 49 million Americans do not have access to a sufficient quantity of affordable, nutritious food. To address the issue of food insecurity, non-profit food banks service a number of regional agencies that provide emergency food relief. To maintain regulatory compliance, each agency serviced by a food bank must receive an on-site audit once every 12–18 months. All audits are made by a member of the food bank’s staff. In collaboration with our community partner, we develop a multi-criteria capacitated vehicle routing with multiple time windows approach to improve the efficiency of the auditing schedule. Using real data from The Foodbank Inc., in Dayton, OH, we evaluate our model using both exact and heuristic methods and analyze the trade-offs between three competing objectives which correspond to our system’s three key stakeholders. Our computational results demonstrate an ability to quickly find solutions which improve upon the current operations at The Foodbank Inc. thereby benefiting all stakeholders and ultimately the community.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Kun Chen, Joe Zhu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The current paper visits a set of data envelopment analysis (DEA) models that identify inefficiency by optimizing input and output slacks. These slacks are aggregated either in an additive or ratio form. Only the ratio slacks-based DEA models can be solved as a linear program and generate a DEA score between zero and unity. The additive slacks-based model can be equivalent to the Russell graph measure and converted into a second order cone programming (SOCP) problem whose solving procedure has become a mature technology. As such, the additive slacks-based model can also yield a DEA score between zero and unity. This study shows that the additive slacks-based model can be applied to modelling network DEA where the internal structures of decision making units (DMUs) are of interest. The additive slacks-based network DEA can be solved using SOCP technique and adapted to the preference of the decision maker by choosing the weights for aggregating individual components in the network structures. It is shown that the additive slacks-based approach can yield divisional efficiencies of Pareto optimal equivalences to be selected by the decision maker when compared to the existing ratio slacks-based measure. An example and solving codes are provided in the current study.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Tadeusz Sawik〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉An innovative portfolio approach and stochastic MIP formulations with an embedded network flow problem are developed for selection of primary and recovery suppliers and assembly plants in the presence of supply chain disruption risks. Local and regional multi-level disruptions of suppliers and assembly plants are considered. Unlike most of reported research on supply chain disruption management a disruptive event is assumed to impact both a primary supplier of parts and the buyer’s firm primary assembly plant. Then the firm may choose alternate (recovery) suppliers and move production to alternate (recovery) plants along with transshipment of parts from the impacted primary plant to the recovery plants. The resulting allocation of unfulfilled demand for parts among recovery suppliers and unfulfilled demand for products among recovery assembly plants determines recovery supply and demand portfolio, respectively. The selection of supply and demand portfolios is determined simultaneously with production scheduling in assembly plants. An integrated decision-making approach with the perfect information about the potential future disruption scenarios is compared with a hierarchical approach with no such information available ahead of time. In the integrated approach a two-stage stochastic model is applied, in which the first stage decision considers disruption scenarios to happen in the second stage so that the impact of disruption risks is mitigated. The second stage decision optimizes the supply chain recovery process. The scenario analysis indicates that for the hierarchical approach the best-case and worst-case disruption scenarios are, respectively subsets and supersets of the corresponding scenarios for the integrated approach. In addition to risk-neutral decision-making based on expected cost or expected service level optimization, an integrated risk-averse approach is developed using CVaR risk measure. The findings indicate that the developed portfolio approach leads to computationally efficient MIP models with a very strong LP relaxation.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Claudio Arbib, Giovanni Felici, Mara Servilio〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Common operation scheduling (COS) problems arise in real-world applications, such as industrial processes of material cutting or component dismantling. In COS, distinct jobs may share operations, and when an operation is done, it is done for all the jobs that share it. We here propose a 0-1 LP formulation with exponentially many inequalities to minimize the weighted number of tardy jobs. Separation of inequalities is in 〈em〉NP〈/em〉, provided that an ordinary min 〈em〉L〈sub〉max〈/sub〉〈/em〉 scheduling problem is in 〈em〉P〈/em〉. We develop a branch-and-cut algorithm for two cases: one machine with precedence relation; identical parallel machines with unit operation times. In these cases separation is the constrained maximization of a submodular set function. A previous method is modified to tackle the two cases, and compared to our algorithm. We report on tests conducted on both industrial and artificial instances. For single machine and general processing times the new method definitely outperforms the other, extending in this way the range of COS applications.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Brian J. Lunday, Matthew J. Robbins〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Within the private pediatric vaccine market, we consider the problem of pricing a polyvalent vaccine developed via collaboration between manufacturers that otherwise compete for market share. This study is motivated by the joint development by Merck and Sanofi Pasteur of a hexavalent pediatric vaccine that is undergoing clinical trials for childhood immunizations in the United States. Adopting a cooperative game theoretic framework, we formulate a mixed-integer linear program and a bilevel mixed-integer nonlinear program. When solved sequentially, these mathematical programming formulations identify whether the collaborative venture begets a stable coalition of manufacturers and, if such is the case, a price for the new vaccine that maximizes the net increase in profits among the collaborating manufacturers, subject to the actions of a rational customer seeking to minimize the cost of meeting the Recommended Childhood Immunization Schedule using pediatric vaccines available in the private sector market. We consider a set of profit sharing mechanisms and demonstrate that a convex combination of a subset of the mechanisms assures financial incentive for all participants. Finally, we demonstrate the formulations and price sharing mechanism for the aforementioned hexavalent vaccine in the context of the contemporary United States pediatric vaccine market.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Xiaoyu Tian, Zhi-Hai Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study examines a capacitated disassembly scheduling and pricing problem, in which the disassembly yield of returned products depends on their acquisition prices. The problem is formulated as a non-convex mixed-integer program. The particle swarm optimization and dynamic programming are combined to address the problem in order to determine the proper acquisition prices of returned products and the appropriate disassembly timing and quantity. To evaluate the quality of solutions, a lower bound is obtained from a mixed-integer linear model established using piecewise linearization and compact hyper-rectangle methods. Extensive computational experiments reveal that the combined algorithm performs well for a wide spectrum of randomly generated problem test instances. Managerial implications are then explored. Conclusions and directions for future research are presented.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Wade D. Cook, Nuria Ramón, José L. Ruiz, Inmaculada Sirvent, Joe Zhu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Incentive plans involve payments for performance relative to some set of goals. In this paper, we extend Data Envelopment Analysis (DEA) to the evaluation of performance in the specific context of pay-for-performance incentive plans. The approach proposed ensures that the evaluation of performance of decision making units (DMUs) that follow the implementation of incentive plans, is made in terms of targets that are attainable, as well as representing best practices. A model is developed that adjusts the benchmarking to the goals through the corresponding payment of incentives, thus DEA targets are established taking into consideration the improvement strategies that were set out in the incentive plans. To illustrate, we examine an application concerned with the financing of public Spanish universities.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Jingchen Liu, Xin Zhai, Lihua Chen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Many innovating firms use trade-in programs to encourage consumers’ repeat purchasing. They can choose between dynamic pricing and preannounced pricing strategies to mitigate the impacts of consumers’ strategic behavior. This paper develops a dynamic game framework to explore the optimal pricing strategy when the firm sequentially introduces new generations of products to a market populated by strategic consumers with trade-in option offered. Results show that under either pricing strategy, the firm has an incentive to sell the old generation products to new consumers in the second period if the salvage value of the old generation product is high enough. When consumers are sufficiently strategic, if both the innovation incremental value of the new generation product and the salvage value of the old generation product are low enough, the firm is better off following the preannounced pricing strategy. Besides, as the firm becomes more farsighted, the comparatively dominant position of preannounced pricing over dynamic pricing disappears gradually.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Baoshan Liu, Xu Guan, Haijun Wang, Shihua Ma〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper investigates the manufacturer’s optimal channel configuration strategy when it provides a pay-on-delivery service directly to consumers through its on-line selling channel. Due to the delivery lead time, we assume that consumers will discount their future utility from the online channel. In particular, the pay-on-delivery service through the direct channel can induce a retailer to lower its price (〈em〉pricing induction〈/em〉) and/or segment consumers by regulating their valuations of buying products through different channels (〈em〉consumers segmentation〈/em〉). Furthermore, the firms’ equilibrium strategies are determined by the consumer’s sensitivity to the delivery time, delivery cost and marginal cost of the indirect channel. When selling through an indirect channel is much more costly, the manufacturer’s encroachment on the retail market can achieve Pareto improvement. Compared with the payment scheme of pay-before-delivery, the results show that the manufacturer’s encroachment is more aggressive in the pay-on-delivery scheme. Finally, we extend the pay-on-delivery scheme to different pricing sequences and consider the case in which consumers and the manufacturer have different discount factors.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Eduardo Fernández, José Rui Figueira, Jorge Navarro〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper presents a new outranking method whose main feature is its capacity to handle imperfect knowledge. This research is interested in two important sources of imperfect knowledge: 1) poorly known model parameters, and 2) imperfectly known (even missing) criterion values characterizing the actions. The use of interval numbers to model imperfect knowledge is suggested, and a new interval-based outranking method is proposed as an extension of the outranking approach to the interval framework. This method handles different sources of imperfect knowledge coming from model parameters (weights, veto thresholds, majority threshold) and from ill-determined, imprecise, uncertain, arbitrary (even missing) criterion values. The index of likelihood of the interval outranking is interpreted from a logical perspective, and could be used for choice, ranking and ordinal classification. Specifically, this paper proposes the method INTERCLASS for ordinal classification, which is inspired by ELECTRE TRI-B. Their assignment rules and structural properties are similar, but INTERCLASS is able to handle imprecisions in weights, veto thresholds, cutting level, and even in criteria defining limiting profiles.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 17 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Chia-Yen Lee, Jia-Ying Cai〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The curse of dimensionality problem arises when a limited number of observations are used to estimate a high-dimensional frontier, in particular, by data envelopment analysis (DEA). The study conducts a data generating process (DGP) to argue the typical “rule of thumb” used in DEA, e.g. the required number of observations should be at least larger than twice of the number of inputs and outputs, is ambiguous and will produce large deviations in estimating the technical efficiency. To address this issue, we propose a Least Absolute Shrinkage and Selection Operator (LASSO) variable selection technique, which is usually used in data science for extracting significant factors, and combine it in a sign-constrained convex nonparametric least squares (SCNLS), which can be regarded as DEA estimator. Simulation results demonstrate that the proposed LASSO-SCNLS method and its variants provide useful guidelines for the DEA with small datasets.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Guiping Li, Xiuli He, Jing Zhou, Hao Wu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper studies a joint pricing, replenishment and preservation technology investment problem for non-instantaneous deteriorating items. Preservation technology affects both the length of non-deterioration period and deterioration rate. Shortages are allowed and partially backlogged. We use price-dependent demand, time-varying deterioration and waiting-time-dependent backlog rates in a general framework to formulate the model. We consider two cases: shortages happen after or before the non-deterioration period. We analytically show the existence and uniqueness of the optimal replenishment schedule, price or preservation investment for any given two of them in two cases. We also prove that there exists a global replenishment policy for any given pricing and preservation investment policies. We then provide an iterative algorithm to search for the optimal solution. Finally, we use numerical examples to illustrate the algorithm, and conduct sensitivity analysis to derive more managerial insights.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Shichen Zhang, Jianxiong Zhang, Guowei Zhu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Noting the rise of dominant retailers, we explore the retailer’s strategies to deter the manufacturer from encroaching in a retailer-led setting where the manufacturer keeps his own direct selling cost private. Our results show that the dominant retailer is always worse off while the manufacturer is always better off by manufacturer encroachment in a retailer-led supply chain when the fixed encroaching cost is negligible. This gives rise to a question that whether there exist effective anti-encroachment strategies for the retailer. We investigate a noted and prevailing strategy of retailers, retail service investing, to examine if it can help the retailer to prevent encroachment. Results show that the retail service level is reduced by encroachment. Retail service investing may actually be an effective anti-encroachment measure for the dominant retailer, especially when retail service investing is highly efficient and the retailer holds a great downward estimation deviation on the direct selling cost of the manufacturer. Retail service investing may lead to Pareto improvement for both the supply chain members and consumers. Additionally, the manufacturer may have incentives to share cost information with the retailer, depending on the retailer’s estimation deviation on the direct selling cost. Finally, we find that a prisoner’s dilemma may occur for a moderate fixed cost of encroachment.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Larry J. LeBlanc, Thomas A. Grossman, Michael R. Bartolacci〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Linear programming models implemented in spreadsheets are understood to be difficult to reuse, whether with modified data that increases or decreases model scale (such as routine model maintenance), as well as with new data (such as deploying a model to a new business setting). The difficulty arises because spreadsheets commingle cell formulas with data, which requires editing cell formulas when the data changes. We provide a novel technique to implement a linear programming model in a spreadsheet that allows for full re-use of the spreadsheet code. It robustly accommodates modified or new data, and enables a spreadsheet LP easily to be reused or even deployed to a new setting with an entirely new dataset. This technique applies to any linear programming model up to approximately 1 million non-zero constraint coefficients, and operates in native Excel without use of macros or VBA. Spreadsheet LP models can now be re-used, re-deployed, and re-optimized as easily as with algebraic software.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 84〈/p〉 〈p〉Author(s): Juan R. Trapero, Manuel Cardós, Nikolaos Kourentzes〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Supply chain risk management has drawn the attention of practitioners and academics alike. One source of risk is demand uncertainty. Demand forecasting and safety stock levels are employed to address this risk. Most previous work has focused on point demand forecasting, given that the forecast errors satisfy the typical normal i.i.d. assumption. However, the real demand for products is difficult to forecast accurately, which means that—at minimum—the i.i.d. assumption should be questioned. This work analyzes the effects of possible deviations from the i.i.d. assumption and proposes empirical methods based on kernel density estimation (non-parametric) and GARCH(1,1) models (parametric), among others, for computing the safety stock levels. The results suggest that for shorter lead times, the normality deviation is more important, and kernel density estimation is most suitable. By contrast, for longer lead times, GARCH models are more appropriate because the autocorrelation of the variance of the forecast errors is the most important deviation. In fact, even when no autocorrelation is present in the original demand, such autocorrelation can be present as a consequence of the overlapping process used to compute the lead time forecasts and the uncertainties arising in the estimation of the parameters of the forecasting model. Improvements are shown in terms of cycle service level, inventory investment and backorder volume. Simulations and real demand data from a manufacturer are used to illustrate our methodology.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 27 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Franco Basso, Mario Guajardo, Mauricio Varas〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper proposes a horizontal collaborative approach for the wine bottling scheduling problem. The opportunities for collaboration in this problem are due to the fact that many local wine producers are usually located around the same region and that bottling is a standard process. Collaboration among wineries is modeled as a cooperative game, whose characteristic function is derived from a mixed integer linear programming model. Real world instances of the problem are, however, unlikely to be solved to optimality due to its complex combinatorial structure and large dimension. This motivates the introduction of an approximated version of the original game, where the characteristic function is computed through a heuristic procedure. Unlike the exact game, the approximated game may violate the subadditivity property. Therefore, it turns relevant not only to find a stable cost allocation but also to find a coalition structure for selecting the best partition of the set of firms. We propose a maximum entropy methodology which can address these two problems simultaneously. Numerical experiments illustrate how this approach applies, and reveal that collaboration can have important positive effects in wine bottling scheduling decreasing delay by 33.4 to 56.9% when improvement heuristic solutions are used. In contrast to the exact game in which the grand coalition is always the best outcome, in the approximated game companies may be better forming smaller coalitions. We also devise a simple procedure to repair the characteristic function of the approximated game so that it recovers the subadditivity property.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 10 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Dexiang Wu, Desheng Dash Wu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We present a decision support approach for a network structured stochastic multi-objective index tracking problem in this paper. Due to the non-convexity of this problem, the developed network is modeled as a Stochastic Mixed Integer Linear Program (SMILP). We also propose an optimization-based approach to scenario generation to protect against the risk of parameter estimation for the SMILP. Progressive Hedging (PH), an improved Lagrangian scheme, is designed to decompose the general model into scenario-based sub-problems. Furthermore, we innovatively combine tabu search and the sub-gradient method into PH to enhance the tracking capabilities of the model. We show the robustness of the algorithm through effectively solving a large number of numerical instances.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 7 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Jens Van Engeland, Jeroen Beliën, Liesje De Boeck, Simon De Jaeger〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In scientific literature two large, partly overlapping areas regarding the environmental and economical attractive removal of waste coexist: reverse logistics and waste management. Both fields study, among other topics, the flows of discarded products leaving the end consumer. This review takes an integrated point of view on reverse logistics and waste management and aims at a better integration. More specifically, it gives a concise but complete overview of the efforts already performed in the area of strategic network design in waste reverse supply chains by means of combinatorial optimization models. Its purpose is to guide interested readers and researchers directly to publications of their interest, and let them identify courses other than the well-worn paths. Among others, we explicitly refer to (1) the importance of environmental, social and performance indicators in multi-objective models, (2) the potential of incorporating the different waste reverse supply chain stakeholders into the network design model, (3) the consideration of future waste reverse supply chain developments like extended producer responsibility schemes and the circular economy and their challenges, and (4) better heuristics to deal with the increasingly complex strategic network design models.〈/p〉〈/div〉

Description: 〈p〉Publication date: March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 83〈/p〉 〈p〉Author(s): Davide Duma, Roberto Aringhieri〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The approaches for the management of elective and non-elective surgery can be classified with respect to the choice of sharing or not the operating theater. The 〈em〉dedicated operating room〈/em〉 policy consists in reserving, each day, one or more operating rooms to perform only non-elective surgeries. Conversely, the 〈em〉shared operating room〈/em〉 policy allows to perform elective and non-elective surgeries in the same operating room session. Furthermore, hybrid policies are defined providing, each day, both dedicated and shared operating rooms. The issue of adopting one of these policies is debated in the literature and they all could be the best policy depending on the scenario and the operative conditions. In this paper we propose a hybrid and flexible model to deal with the surgery process scheduling of both elective and non-elective patients, in which new online and offline optimization algorithms are introduced, taking into account both patient- and facility-centered objectives. The aim of this paper is to provide a detailed comparison among different policies taking into account several scenarios and operative conditions in such a way to consider the characteristics of the operating theater and those of the patients it serves.〈/p〉〈/div〉

Description: 〈p〉Publication date: March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 83〈/p〉 〈p〉Author(s): Torbjørn Hanson〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The ability of public sector policy makers to prioritize has a huge impact on the effectiveness of public service provision. Public services can take the form of final outputs demanded by consumers or of intermediate outputs contributing to a process of realizing the higher goals of society. In doing the right things, policy makers choose a mix of intermediate outputs maximizing their preference value for public service outcomes, while managers do things right when responsible for producing outputs efficiently. This distinction enables us to pinpoint important reasons for inefficiencies in the provision of public services. Taking advantage of the method of scenario based planning, a model for measuring effectiveness is developed for situations where traditional methods such as two-stage regressions fail due to long time lags and lack of variation in the variables. Scenarios take the role of outcomes in the modeling of outcome mapping functions, where each scenario represents a set of environmental variables. The model is specified for the provision of defense outcomes, where the lag between changes in input and impacts on outcomes are substantial. From a sample of 12 combat units in the Norwegian Armed Forces, producing different outputs, we find that inefficiencies in output mix can explain most of the changes in overall effectiveness over a four-year period of time.〈/p〉〈/div〉

Description: 〈p〉Publication date: March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 83〈/p〉 〈p〉Author(s): Federico Mosquera, Pieter Smet, Greet Vanden Berghe〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Home care services are in high demand given how they are steadily becoming the primary source of care for the elderly. Powerful decision support tools are indispensable for effectively managing available staff in the context of ever-increasing demand for care and limited caregiver availability. This paper advances home care literature by introducing flexible task durations, thereby enabling tasks to be completed faster and ultimately more care to be scheduled. This new concept, which originates from practice, introduces an additional decision to be made when creating a schedule, thereby greatly increasing the scheduling complexity. Consequently, this paper introduces a new optimization-based decision support model which allows for scheduling with flexible task duration, as well as other types of flexibility. A computational study quantifies the impact of: (i) scheduling with a finer task granularity thereby enabling accurate prioritization of high and low priority care, (ii) flexibility in task duration enabling tasks to be completed faster and more care to be scheduled, and (iii) increasing the number of different locations visited by a caregiver thereby enabling a trade-off between the number of serviced clients and caregiver workload. A new publicly available real-world data set is used, obtained directly from home care organizations operating in Flanders. Analysis of the computational results demonstrates that significant improvements in operational efficiency may be realized with minimal effort required by organizations. Furthermore, the proposed algorithm’s performance is confirmed by comparison against the bounds obtained by solving an integer programming formulation of the problem. Finally, a management policy scheme is proposed which, when gradually implemented in a home care organization, results in a more efficient and therefore cost-effective deployment of its workforce.〈/p〉〈/div〉

Description: 〈p〉Publication date: March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 83〈/p〉 〈p〉Author(s): Ömer Burak Kınay, Francisco Saldanha-da-Gama, Bahar Y. Kara〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This work aims at investigating multi-criteria modeling frameworks for discrete stochastic facility location problems with single sourcing. We assume that demand is stochastic and also that a service level is imposed. This situation is modeled using a set of probabilistic constraints. We also consider a minimum throughput at the facilities to justify opening them. We investigate two paradigms in terms of multi-criteria optimization: vectorial optimization and goal programming. Additionally, we discuss the joint use of objective functions that are relevant in the context of some humanitarian logistics problems. We apply the general modeling frameworks proposed to the so-called stochastic shelter site location problem. This is a problem emerging in the context of preventive disaster management. We test the models proposed using two real benchmark data sets. The results show that considering uncertainty and multiple objectives in the type of facility location problems investigated leads to solutions that may better support decision making.〈/p〉〈/div〉

Description: 〈p〉Publication date: March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 83〈/p〉 〈p〉Author(s): Jasmine (Ai-Chih) Chang, Haibing Lu, Jim (Junmin) Shi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Production-inventory systems with continuous production or continuous manufacturing have been implemented in a variety of manufacturing contexts. Most recently, the Commissioner of the FDA has called on drug and biological product manufacturers to begin switching from batch manufacturing processes to continuous production. Motivated by prevailing applications and the emerging and promising landscape in the healthcare and pharmaceutical industries, this paper studies a continuous-review production-inventory system with a constant production rate and compound Poisson demands, in which the cost of the system is assessed via inventory holding, stockout penalty and production costs. For any initial inventory, we derive a closed-form expression for the expected discounted cost function until stockout occurrence. We systemically quantify the stockout risk on four different dimensions (i.e., 〈em〉time, volume, frequency〈/em〉 and 〈em〉percentage〈/em〉) and derive explicit expressions for each type of risk metric. The objective is to derive the production rate that minimizes the expected discounted system cost subject to a given risk tolerance level on stockouts. With the aid of the derived explicit forms of stockout risk and the cost function, we develop a computationally-efficient algorithm for the optimal solution. Extensive numerical studies are conducted to illustrate our results with rich insights. Numerically, we show that it is outrageously costly to reduce stockout risk, especially when this risk is relatively low; the value of risk is more sensitive to the stockout risk level if the demand distribution has a higher volatility.〈/p〉〈/div〉

Description: 〈p〉Publication date: March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 83〈/p〉 〈p〉Author(s): Chiang Kao, Shiang-Tai Liu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The data envelopment analysis (DEA) technique uses the most favorable weights for each decision making unit (DMU) to calculate efficiency. The resulting efficiency scores are thus incomparable and difficult to discriminate. This phenomenon is more prominent for network systems, which involves the ranking of the component divisions, in addition to the system. This paper applies the idea of cross evaluation, which has been demonstrated to be an effective approach in ranking DMUs for systems considered as a whole-unit, to measure the efficiency of the two basic structures of network systems, series and parallel. The proposed model is able to decompose the cross efficiency measure of the system into the product of those of the divisions for the series structure and a weighted average for the parallel structure. The results from two real-world cases, one for the basic series structure and another for the parallel one, show that the cross efficiency measures proposed in this paper not only increase the discriminating power in ranking systems and divisions, but also identify the relationship between the system and division efficiencies. Which division has stronger effects on the performance of the system is reflected from this relationship.〈/p〉〈/div〉

Description: 〈p〉Publication date: March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 83〈/p〉 〈p〉Author(s): Rubén Ruiz, Quan-Ke Pan, Bahman Naderi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Large manufacturing firms operate more than one production center. As a result, in relation to scheduling problems, which factory manufactures which product is an important consideration. In this paper we study an extension of the well known permutation flowshop scheduling problem in which there is a set of identical factories, each one with a flowshop structure. The objective is to minimize the maximum completion time or makespan among all factories. The resulting problem is known as the distributed permutation flowshop and has attracted considerable interest over the last few years. Contrary to the recent trend in the scheduling literature, where complex nature-inspired or metaphor-based methods are often proposed, we present simple Iterated Greedy algorithms that have performed well in related problems. Improved initialization, construction and destruction procedures, along with a local search with a strong intensification are proposed. The result is a very effective algorithm with little problem-specific knowledge that is shown to provide demonstrably better solutions in a comprehensive and thorough computational and statistical campaign.〈/p〉〈/div〉

Description: 〈p〉Publication date: March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 83〈/p〉 〈p〉Author(s): Samah Jradi, Tatiana Bouzdine Chameeva, Juan Aparicio〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we measure and decompose revenue inefficiency over time while accounting for all sources of technical inefficiencies. Our proposed decomposition exploits the dual relationship between the weighted additive distance function and revenue inefficiency in Aparicio et al. [1]. With the aid of the Luenberger indicator, we decompose this indicator into productivity change, and overall allocative change components. The importance of such decomposition is that it provides a complete picture of the sources of productivity change, thus obtaining a slack free allocative component. Finally, the model is applied to the French wine sector to illustrate its practicality: we track how revenue inefficiency evolves in French wine regions over the 2004–2013 period, before and after the implementation of Common Market Organization policies in Europe in 2008.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 3 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Mengyue Wang, Hongxuan Huang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The amount of capital possessed by a supply chain usually is a hard constraint for the design of its configuration. In this paper, a scenario-based approach is proposed for designing a flexible capital-constrained global supply chain (CCGSC) in which the capital constraint can be relaxed through loans from financial institutions or institutional investors, and the coordination with operational strategies such as constructing or leasing facilities to meet uncertain demands. The integration of operational and financial strategies is formulated as a mixed-integer linear programming model to maximize the quasi shareholder value (QSV) of the supply chain, which is defined by the summation of the present value of cash in hand and the assets at the end of the planning horizon, along with hedging the uncertainties from demands and exchange rates. In particular, the strategy of leasing a facility, which provides an alternative selection for the facility location, requires less amount of the capital than that of buying or constructing it. The research on the CCGSC also indicates that the complementary property holds for both the remaining capital of loans with higher costs and another available loan with a lower cost under certain conditions. A case study is presented to illustrate effectiveness and efficiency of the scenario-based approach. The numerical results verify that the collaborative decisions on the flows of goods and financial resources could improve the flexibility of the supply chain and its performance, even in extreme scenarios.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 3 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Jianqiang Zhang, Qingning Cao, Xiuli He〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper studies manufacturer encroachment in a supply chain wherein the manufacturer and/or the retailer should invest in informative advertising. Using a game-theoretic framework, we explore three schemes of advertising and their influences on manufacturer encroachment. First, if the manufacturer controls advertising, encroachment will result in higher advertising intensity relative to the non-encroachment case, sometimes leading to a win-win situation for both the manufacturer and the retailer as the boosted demand flows into the wholesale market under encroachment. Second, if the retailer controls advertising, an encroaching manufacturer should further reduce the wholesale price as compared to the counterpart with no advertising. This downward pressure on wholesale price can benefit the retailer, but might hurt the encroaching manufacturer. Third, we incorporate manufacturer advertising and retailer advertising into a cooperative advertising scheme, where the manufacturer can set a participation rate to adjust the advertising cost for the retailer. Interestingly, an encroaching manufacturer will pay the retailer more to subsidize his advertising cost. Under this scheme, the manufacturer is always better off under encroachment, and the retailer can also gain as a result of advertising cost-sharing from the manufacturer. Our results also apply to the case of persuasive advertising. Although persuasive advertising leads to different prices or advertising decisions, there are always chances for the retailer to benefit from manufacturer encroachment.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 25 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Yongjun Li, Lizheng Wang, Feng Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Performance prediction is an issue of vital importance in many real managerial applications. This paper will propose a prediction approach for sports team performance based on data envelopment analysis (DEA) methodology and data-driven technique. The proposed approach includes two steps: The first one conducts a multivariate logistic regression analysis to examine the relationship between the winning probability and game outcomes at the team-level. The other one addresses a DEA-based player portfolio efficiency analysis to optimally choose players and plan the playing time among players in the court. The second step aims to use players’ and team's historical data to train the future and obtain the most promising outcomes in terms of their average inefficiency status. Finally, we apply the proposed performance prediction approach to National Basketball Association and take Golden State Warriors as an example to illustrate its usefulness and efficacy. We obtain the prediction results for the 2015-16 regular season based on a four-season dataset from the 2011-12 season to the 2014-15 season. Further, we carry out multiple experiments to provide deeper discussion and analysis on according prediction results. It shows that the DEA-based data-driven approach can predict the sports team performance very well and can also provide interesting insights into the performance prediction problem.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 2 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Jan Eise Fokkema, Martin J. Land, Leandro C. Coelho, Hans Wortmann, George B. Huitema〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We address an inventory routing problem (IRP) in which routing and inventory decisions are dictated by supply rather than demand. Moreover, inventory is held in containers that act as both a storage container and a movable transport unit. This problem emanates from logistics related to biogas transportation in which biogas is transported in containers from many suppliers to a single facility. We present a novel and compact formulation for the supply-driven IRP which addresses the routing decisions in continuous-time in which inventory levels within the containers are continuous. Valid inequalities are included and realistic instances are solved to optimality. For all experiments, we found that the total transportation time is minimized when the storage capacity at each supplier is larger than or equal to the vehicle capacity. These routes are characterized by tours in which mostly single suppliers are visited. In 95% of the instances, the average content level of the exchanged containers exceeded 99.6%.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 11 October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Sebastián Lozano, Somayeh Khezri〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper the smallest improvement DEA approach to general networks of processes is proposed. The corresponding projection direction is endogenously computed by the model so that the relative distance to the frontier is minimal. Both the cooperative and the non-cooperative scenarios for the intermediate products are considered. For the cooperative case, the computed inefficiency score can be decomposed into the sum of the inefficiency scores of the different processes. For the non-cooperative scenario, an alternative decomposition based on the inefficiency of the different intermediate products is also presented. The proposed approach has been validated on different network DEA configurations.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 8 October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Aliaa Alnaggar, Fatma Gzara, James H. Bookbinder〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Crowdsourced delivery systems have created many new industry last-mile delivery solutions and have received some attention in the academic literature. In this review paper, we analyze the current industry status of this emergent concept and provide a classification of available platforms based on their matching mechanisms, target markets and compensation schemes. We review the operations research (OR) literature explicitly addressing this topic and assess the realism of the assumptions, and applicability to real-world applications. We also compare the management decisions within crowdsourced delivery systems to well-studied OR problems in the literature, and pinpoint new challenges that arise in the context of crowdsourced delivery. The purpose of this paper is to identify key elements of such systems that distinguish them from other transportation systems, to ultimately shed light on some promising research directions.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 9 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Amalia Benítez-Fernández, Francisco Ruiz〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Solving multicriteria decision making problems often requires the assessment of certain preferential information. In some occasions, this information must be given by several individuals or social groups, and these individual assessments need to be aggregated into single global preferences. This cardinal preferences aggregation problem has been tackled using different techniques, including multicriteria decision making ones. In this paper, a Meta-Goal Programming approach is proposed, where different target values can be set on several achievement functions that measure the goodness of the global assessments. This methodology presents strong advantages due to its modeling flexibility and its ability to find balanced solutions. The proposed approach is demonstrated with an illustrative example and a series of computational experiments, and it is shown that the Meta-Goal Programming method produces results with better values of the achievement functions than other classical and multicriteria approaches.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 7 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Mehdi Toloo, Jana Hančlová〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Data envelopment analysis (DEA) evaluates the relative efficiency of a set of comparable decision making units (DMUs) with multiple performance measures (inputs and outputs). Classical DEA models rely on the assumption that each DMU can improve its performance by increasing its current output level and decreasing its current input levels. However, undesirable outputs (like wastes and pollutants) may often be produced together with desirable outputs in final products which have to be minimized. On the other hands, in some real-world situations, we may encounter some specific performance measures with more than one value which are measured by various standards. In this study, we referee such measures as 〈em〉multi-valued〈/em〉 measures which only one of their values should be selected. For instance, unemployment rate is a multi-valued measure in economic applications since there are several definitions or standards to measure it. As a result, selecting a suitable value for a multi-valued measure is a challenging issue and is crucial for successful application of DEA. The aim of this study is to accommodate multi-valued measures in the presence of undesirable outputs. In doing so, we formulate two individual and summative selecting directional distance models and develop a pair of multiplier- and envelopment-based selecting approaches. Finally, we elaborate applicability of the proposed method using a real data on 183 NUTS 2 regions in 23 selected EU-28 countries.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 6 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Marco A. Boschetti, Matteo Golfarelli, Simone Graziani〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Pivot tables are one of the most popular tools for data visualization in both business and research applications. Although they are in general easy to use, their comprehensibility becomes progressively lower when the quantity of cells to be visualized increases (i.e., 〈em〉information flooding problem〈/em〉). Pivot tables are largely adopted in OLAP, the main approach to multidimensional data analysis. To cope with the information flooding problem in OLAP, the 〈em〉shrink operation〈/em〉 enables users to balance the size of query results with their approximation, exploiting the presence of multidimensional hierarchies. The only implementation of the shrink operator proposed in the literature is based on a greedy heuristic that, in many cases, is far from reaching a desired level of effectiveness.〈/p〉 〈p〉In this paper we propose a model for optimizing the implementation of the shrink operation which considers two possible problem types. The first type minimizes the loss of precision ensuring that the resulting data do not exceed the maximum allowed size. The second one minimizes the size of the resulting data ensuring that the loss of precision does not exceed a given maximum value. We model both problems as set partitioning problems with a side constraint. To solve the models we propose a dual ascent procedure based on a 〈em〉Lagrangian pricing approach〈/em〉, a Lagrangian heuristic, and an exact method. Experimental results show the effectiveness of the proposed approaches, that is compared with both the original greedy heuristic and a commercial general-purpose MIP solver.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 20 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Enrique Jélvez, Nelson Morales, Pierre Nancel-Penard, Fabien Cornillier〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this work we address the Precedence Constrained Production Scheduling Problem (PCPSP), the problem of scheduling tasks in such a way that total profit is maximized, while satisfying conditions such as precedence constraints among tasks and side constraints. A motivation for addressing this problem comes from open-pit mining industry, where the PCPSP seeks to maximize the net present value of an ore deposit by selecting the blocks (tasks) to extract, their extraction periods and their processing options, while satisfying constraints as precedences among blocks, limited availability of operational resources and maximum and/or minimum allowable concentrations of ore-grade or pollutants. Since real-world models have millions of blocks and constraints, the monolithic problem is computationally intractable. This article presents a hybrid heuristic algorithm that combines a rolling horizon decomposition with a block preselection procedure, allowing near-optimal solutions to be quickly determined. The proposed heuristic was tested on all the PCPSP instances of the MineLib library and has shown a significant improvement over the previous reported results. Moreover, a good feasible solution has been found for the instance 〈code〉W23〈/code〉, for which no solution has been previously reported.〈/p〉〈/div〉

Description: 〈p〉Publication date: June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 85〈/p〉 〈p〉Author(s): Zelong Yi, Fan Li, Lijun Ma〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Many software developers provide end-users with feature-limited free trials to facilitate the distribution of their commercial products. Some international developers also authorize local agents to resell software products to consumers. In this paper, we explore a software developer’s optimal trial strategy in the presence of an agent. Due to the involvement of the agent, the double marginalization effect may result in a higher price and a lower demand for the commercial software. We find that, if the network effect is strong enough, the developer will release a trial version and attract trial users to secure greater positive externality, which is consistent with the case without an agent involved. However, if the network effect is weak, the developer may still release a trial version to the public, which sharply contrasts the case without an agent involved. A trial is offered in a bid to countervail the negative double marginalization effect. We also extend this work by endogenizing the trial-version quality, and examining the differentiation induced by trial offer decisions made by different firms in the supply chain. Furthermore, we investigate the effects induced by consumers’ differentiated costs of using the free trial and commercial versions.〈/p〉〈/div〉

Description: 〈p〉Publication date: June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 85〈/p〉 〈p〉Author(s): Fridolin Haag, Judit Lienert, Nele Schuwirth, Peter Reichert〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Multi-criteria decision analysis (MCDA) requires an accurate representation of the preferences of decision-makers, for instance in the form of a multi-attribute value function. Typically, additivity or other stringent assumptions about the preferences are made to facilitate elicitation by assuming a simple parametric form. When relaxing such assumptions, parameters cannot be elicited easily with standard methods. We present a novel approach for identifying multi-attribute value functions which can have any shape. As preference information indifference statements are used that can be elicited by trade-off questions. Instead of asking one indifference statement for each pair of attributes, we ask for multiple trade-offs at different points in the attribute space. This allows inferring parameters of complex value functions despite the simplicity of the preference statements. Parameters are estimated by taking into account preference and elicitation uncertainty with a probabilistic model. Statistical inference supports identifying the most adequate preference model out of several candidate models through quantifying the uncertainty and assessing the need for non-additivity. The approach is elaborated for determining value functions by hierarchical aggregation. We apply it to an assessment of the ecological state of rivers, which is used to support environmental management decisions in Switzerland. Preference models of four experts were quantified, confirming the feasibility of the approach and the relevance of considering non-additive functions. The method suggests a promising direction for improving the representation of preferences.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S0305048317308204-fx1.jpg" width="301" alt="Image, graphical abstract" title="Image, graphical abstract"〉〈/figure〉〈/p〉〈/div〉

Description: 〈p〉Publication date: June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 85〈/p〉 〈p〉Author(s): Qingxian An, Yao Wen, Tao Ding, Yongli Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Resource sharing exists not only among multiple entities but also among various stages of a single network structure system. Previous studies focused on how to allocate total given sharable resources to stages to maximize the efficiency of the network structure system, and a few discussed the fair allocation of potential gains obtained from resource sharing. In this study, we explore a new case in which the common inputs (or shared resources) of all stages are known. By constructing a game that regards each stage as a player, we integrate cooperative game theory with network data envelopment analysis (DEA) to explore the payoff allocation problem in a three-stage system. We build network DEA models to calculate the optimal profits of the system before and after resource sharing (i.e., pre- and post-collaboration optimal profits), and then apply the Shapley value method to allocate the increased profits of the system to its stages. Results indicate that the game among stages in a three-stage system is superadditive. A numerical example is provided to illustrate our method.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 15 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Amar Oukil〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Under a data envelopment analysis (DEA) framework, full ranking of a group of decision making units (DMUs) can be carried out through an adequate amalgamation of the cross-efficiency (CE henceforth) scores produced for each DMU. In this paper, we propose a ranking procedure that is based on amalgamating the weight profiles selected over the cross-evaluation rather than related CE scores. The new approach builds, for each DMU, a collective weight profile (CWP henceforth) by exploiting the preference voting system embedded within the matrix of weights, which views the assessing DMUs as voters and the input/output factors as candidates. The occurrence of zero votes is discussed as a special case and a two-level aggregation procedure is developed. The CWPs that are produced extend the concept of collective appreciation to the input/output factors of each DMU so that group dynamics is truly reflected, mainly in decision making circumstances where factor prioritization is necessary for making choices or allocating resources. The robustness of the proposed ranking approach is evaluated with three examples drawn from the literature.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 14 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Ana C.L. Vieira, Mónica D. Oliveira, Carlos Bana e Costa〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper proposes a new Collaborative Value Modelling framework, that combines Delphi and multicriteria decision conferencing, to build widely informed evaluation models. Multicriteria Decision Analysis (MCDA) is commonly used to help decision-makers and other stakeholders in complex evaluation contexts. Further to the technical soundness and meaningfulness of the methods and tools used, it is critical to design adequate social processes to promote shared understanding around key evaluation issues while capturing multiple stakeholders’ values and perspectives. Multicriteria decision conferencing processes have been typically adopted for collaborative modelling using MCDA methods in decision conferences with relatively small groups. Such a socio-technical approach has proven to be effective, in a variety of contexts, in creating a collaborative environment that enables surfacing individual beliefs, identifying common concerns, managing eventual value conflicts and promoting agreement in group model building. But, extending this framework to broader participatory contexts requires a different design of the social process, in order to ensure that model building captures the full panoply of points of view. This challenge can be tackled by enhancing multicriteria decision conferencing with an all-embracing (Web-)Delphi participatory process. We depart from the existing collaborative knowledge acquisition methodology to design, with the Delphi method, a participatory knowledge construction process that elicits and analyses individual judgmental knowledge from a (very) large and diverse number of stakeholders. The knowledge acquired is then digested by a small group of key-players, in a subsequent decision conferencing, to collaboratively develop a widely informed multicriteria evaluation model. This new Web-Delphi-decision conferencing social setting has been tested already in real complex evaluation contexts using a specific multicriteria method, the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH), to develop a variety of value modelling activities. We call this socio-technical design the Collaborative Value Modelling framework. Here, we describe its real use to support the construction of value functions, focusing on how the judgmental knowledge collected flows between the participatory and collaborative stages of the framework. Results validate that enhancing MACBETH decision conferencing with an 〈em〉ex ante〈/em〉 Web-Delphi process fosters higher participation and collaboration in multicriteria modelling.〈/p〉〈/div〉

Description: 〈p〉Publication date: June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 85〈/p〉 〈p〉Author(s): Xu Wang, Bart Baesens, Zhen Zhu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Social media has become a widely used marketing tool for reaching potential customers. Because of its low cost, social media marketing is especially appealing to customer-to-customer (C2C) sellers. Customers can also benefit from social media marketing by learning about products and by interacting with sellers in real time. However, a seller’s marketing microblogs may backfire on her for dominating the social space. Defining the marketing popularity as the average number of likes each seller receives per marketing-related microblog and defining the marketing aggressiveness level as the proportion of her marketing-related microblogs, this paper empirically quantifies the optimal level of marketing aggressiveness in social media to achieve the maximum popularity. We gather the data from China’s largest microblogging platform, Sina Weibo, and the sellers in our sample are from China’s largest C2C online shopping platform, Taobao. We find that the empirical relationship between the marketing aggressiveness level and the marketing popularity follows an inverted U-shape curve, where the optimal level is around 30%. In addition, we find a saturation effect of the number of followers on marketing popularity after it reaches around 100,000. Our findings imply that social media marketing should not overlook customers’ social needs. Our measure of marketing aggressiveness provides a dynamic business metric for practitioners to monitor so as to improve their marketing and managerial decision making process.〈/p〉〈/div〉

Description: 〈p〉Publication date: June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 85〈/p〉 〈p〉Author(s): Mohammad Khoveyni, Hirofumi Fukuyama, Robabeh Eslami, Guo-liang Yang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Network data envelopment analysis (NDEA) deals with evaluating the performance of a set of homogeneous production processes (or decision making units (DMUs)) taking into account the internal structure of DMUs. A large number of studies in NDEA are based on two-stage structures with intermediate products. For a two-stage DMU, if the intermediate products increase by increasing the inputs of the first stage, then increasing of the intermediate products may lead to some variations on the outputs of the second stage. In this research, this issue is called “variations effect” of the intermediate products on the outputs of the second stage. These variations are very significant for a decision maker from the economic viewpoint because, in this case, he/she can identify variations of the outputs of the second stage and also recognize congestion by increasing the intermediate products; and then decides whether increasing the intermediate products is good or not for improving the performance of the two-stage DMU. This fact is the motivation for creating this study which hitherto, none of the studies in NDEA pay attention to this issue. The current research with the aim of tackling this issue proposes a DEA approach to determine the range of variations of the intermediate products and specify the effect of these variations on the outputs of the second stage in the presence of both negative and non-negative data. Finally, two numerical and empirical examples are provided to illustrate the potential application of the proposed approach.〈/p〉〈/div〉

Description: 〈p〉Publication date: June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 85〈/p〉 〈p〉Author(s): Anna Nagurney, Pritha Dutta〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we focus on the blood services industry in the US, which is undergoing major changes, including a rise in competition, and we develop a novel game theory model for blood donations among blood service organizations in which the organizations seek to maximize their transaction utilities and compete on the quality of service that they provide at the blood collection sites in different regions. The governing equilibrium concept is that of Nash Equilibrium. We formulate the equilibrium as a variational inequality problem and prove the existence of the equilibrium quality level service pattern. We also provide conditions for uniqueness and present a Lagrange analysis and interpretation associated with the lower and upper bounds on the quality service levels. An algorithm with nice features for computations is proposed and then applied to numerical examples of increasing complexity. The results demonstrate that enhanced competition can improve the quality service level and that blood service organizations can also benefit from having collection sites in multiple regions.〈/p〉〈/div〉

Description: 〈p〉Publication date: June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 85〈/p〉 〈p〉Author(s): Xiaoyang Zhou, Zhongwen Xu, Jian Chai, Liming Yao, Shouyang Wang, Benjamin Lev〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Efficiency evaluations are vital for banks so that they can determine their future development and enhance their competitiveness. To comprehensively explore a bank’s internal structures and identify the specific reasons for any inefficiencies, three stages of banking systems need to be examined; capital organization, capital allocation, and profitability. To measure the efficiencies over consecutive periods, this paper developed a multi-period, multi-stage DEA model, in which unused assets were carried over to subsequent periods, fixed assets and employee salaries were regarded as shared inputs for all three stages, and non-performing loans, which were characterized using triangular type-2 fuzzy numbers, were introduced as undesirable outputs to reflect credit risk. The developed model was applied to a case study to evaluate the efficiencies of listed Chinese commercial banks from 2014 to 2016, from which a disparity in efficiencies was found; that is all banks were found to be generally inefficient; however, the inefficiencies occurred in different stages for different types of banks. Varying optimistic-pessimistic attitudes were applied to identify the overly sensitive banks and comparisons were conducted to provide managerial insights and verify the superiority of the proposed model. It was concluded that to enhance overall efficiency, banks need to have a reasonable business scale, that adopting a three-stage analytical framework can better identify efficiencies and the weaker stages, and that neglecting carryovers can overestimate bank performance.〈/p〉〈/div〉

Description: 〈p〉Publication date: June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 85〈/p〉 〈p〉Author(s): Trung Hieu Tran, Yong Mao, Paul Nathanail, Peer-Olaf Siebers, Darren Robinson〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we develop an integrated model for slacks-based measure (SBM) simultaneously of both the efficiency and the super-efficiency for decision-making units (DMUs) in data envelopment analysis (DEA). Unlike the traditional solution approaches in which we need to identify the efficient DMUs by the SBM model of Tone (2001) [20] before applying the super SBM model of Tone (2002) [21] for the DMUs to achieve their super-efficiency scores, our integration can obtain the efficiency scores of the inefficient DMUs and the super-efficiency scores of the efficient DMUs by solving simultaneously these two models by an one-stage approach. Therefore, it may save computational time for large-scale practical applications. Due to the non-linearity in the objective function of this integrated model, we develop a linearisation technique to deal with the non-linear model. The numerical experiments, carried out on several examples in the literature and a case study, have demonstrated the accuracy and the computational time effectiveness of our proposed model as compared with the traditional solution approaches.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 19 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Rebecca Chan, Zhaolin Li, Dmytro Matsypura〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Assortment optimisation is a critical decision that is regularly made by retailers. The decision involves a trade-off between offering a larger assortment of products but smaller inventories of each product and offering a smaller number of varieties with more inventory of each product. We propose a robust, distribution-free formulation of the assortment optimisation problem such that the assortment and inventory levels can be jointly optimised without making specific assumptions on the demand distributions of each product. We take a max-min approach to the problem that provides a guaranteed lower bound to the expected profit when only the mean and variance of the demand distribution are known. We propose and test three heuristic algorithms that provide solutions in 〈em〉O〈/em〉(〈em〉n〈/em〉log (〈em〉n〈/em〉)) time and identify two cases where one of the heuristics is guaranteed to return optimal policies. Through numerical studies, we demonstrate that one of the heuristics performs extremely well, with an average optimality gap of 0.07% when simulated under varying conditions. We perform a sensitivity analysis of product and store demand attributes on the performance of the heuristic. Finally, we extend the problem by including maximum cardinality constraints on the assortment size and perform numerical studies to test the performance of the heuristics.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 10 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Junfei Chu, Jie Wu, Chengbin Chu, Tinglong Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper proposes new fixed cost allocation approaches for allocating a fixed cost among decision-making units (DMUs) with two-stage structures under the framework of data envelopment analysis (DEA). Firstly, we give the set of possible fixed cost allocations, prove that all DMUs can be overall efficient when evaluated by a common set of weights after fixed cost allocation. Secondly, from a centralized point of view, we consider the competition between the DMUs’ two stages in fixed cost allocation and regard these two kinds of stages as two unions. Then, we incorporate leader-follower models to propose a fixed cost allocation approach to handle the situation in which the two unions make decisions sequentially. Based on the result of these models, a concept of satisfaction degree of each union on a fixed cost allocation is presented. A satisfaction degree bargaining game model is then proposed to obtain a fixed cost allocation which is a bargaining equilibrium of the two unions. We show that the proposed approaches always obtain a fixed cost allocation that is proportionally invariant. Additionally, the satisfaction degree bargaining game approach automatically guarantees the uniqueness of the fixed cost allocation. These properties make the fixed cost allocation generated by our approaches more stable and more acceptable. Finally, a numerical example and an application of fixed cost allocation among bank branches are given to illustrate the proposed approach and to compare it with a benchmark approach among the current studies, respectively.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 10 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Alena Otto, Xiyu Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Modern markets demand mass customization, that is, the manufacture of customized products at low cost. Mass customization represents a major challenge for the organization of assembly lines, which were originally designed for the manufacture of homogeneous products. The multiple-piece-flow assembly line is an organizational innovation that can address this challenge. Here, several customized workpieces, each associated with a separate customer order and, hence, a separate due date, are handled simultaneously in one cycle. Consequently, the idle times decrease as do the manufacturing costs. Multiple-piece-flow assembly lines are used, for instance, in manufacturing industrial equipment.〈/p〉 〈p〉To the best of our knowledge, this paper is the first to investigate product sequencing in multiple-piece-flow assembly lines. We formalize the underlying planning problem, establish a mixed-integer model, examine its relation to several classic optimization problems, and describe useful problem properties. We leverage these properties to design an effective iterative variable neighborhood heuristic (IVNH). A detailed simulation based on real-world data and the rolling-horizon planning framework confirms that the IVNH is well suited for practical use. Furthermore, extensive computational experiments on well-structured randomly generated data sets show that the IVNH identifies optimal or near-optimal solutions within short run times. It outperformed an off-the-shelf optimization software, and in certain practice settings, the IVNH was even able to substantially reduce average order delays.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 6 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Huchang Liao, Xingli Wu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper develops a comprehensive algorithm for multi-expert multi-criteria decision making problems considering quantitative and qualitative criteria in forms of benefit, cost or target types. We focus on using probabilistic linguistic term sets to express the qualitative evaluations due to their excellence in expressing complex individual and collective linguistic assessments. Firstly, we develop a target-based linear normalization technique and a target-based vector normalization technique. A weight adjustment method is proposed to achieve the tradeoff between criteria after normalization. Given that the two target-based normalization techniques have different advantages, we then propose a ranking method, which consists three subordinate models, based on these two target-based normalization approaches and three aggregation techniques. Reliable results of a multi-expert multi-criteria decision making problem is determined by integrating the subordinate utility values and the ranks of alternatives. The proposed method is implemented to solve the green enterprise ranking problems and the excavation scheme selection problem for shallow buried tunnels, respectively. The advantages of the proposed method are emphasized through comparative analyses with other ranking methods.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 19 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Lei Fang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Conventional DEA models treat the entire production system as a black box and ignore its internal structures. To address this issue, many studies have examined the DEA efficiencies of two-stage systems in which all outputs of the first stage are the only inputs to the second stage. Based on game theory, the non-cooperative model and centralized model were developed for such a two-stage network structure. However, for the centralized model with multiple optimal solutions and the non-cooperative model, an assumption is required as to whether the first or second stage should be assigned the absolute priority for optimization. In many cases, certain circumstances might exist in which one stage does not completely dominate the other stage. In this paper, we develop a methodology for assessing the overall and stage efficiencies by considering the different and DMU-specific degree of priority given to the stages. Particularly, the non-cooperative model and the centralized model can be deemed as special cases. Moreover, we compare the proposed approaches with the existing approaches, which indicates that our approaches can greatly reduce the computational burden. Two empirical examples are used to demonstrate the proposed approach.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 10 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Elisa Fusco, Francesco Vidoli, Nicky Rogge〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper introduces a new composite indicator method integrating the spatial dependence into the robust directional model in the case of undesirable outputs. The proposed approach is advantageous compared to the traditional and conditional robust Benefit-of-the-Doubt (BoD) models in that it allows to compare the performance of individual units with local cluster of peers. The methodology has been tested on a very detailed database of Italian municipalities for the year 2015 in the municipal solid waste collection and processing sector and confirms the existence of strong local constraints linked to the disposal facilities planned by higher level Authorities.〈/p〉〈/div〉

Description: 〈p〉Publication date: July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 86〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: Available online 21 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega〈/p〉 〈p〉Author(s): Vincenzo Patrizii〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Multiplicative decomposition of stages indices is shown to be consistent with V〈span〉rs〈/span〉 network technologies. It is also shown why the primal dual correspondence breaks for serial network V〈span〉rs〈/span〉 models. Different V〈span〉rs〈/span〉 models can be associated with alternative transfer pricing systems, within the network. Multiplicative decomposition implies marginal cost pricing across stages. While other pricing systems (full cost) correspond to some of the known non-multiplicatively decomposable V〈span〉rs〈/span〉 models, proposed in the literature. Stages indices, therefore, respond not only to efficiency, but also to the network’s distributive criteria across stages. The distributive contents of stage indices provide the key element for a solution to the problem of measuring scale efficiency in network systems. Multiplicative decomposable V〈span〉rs〈/span〉 models can be extended to more general network systems, containing both parallel and in series structures. The cost of this generalisation is that efficiency indices are referred to modified stages, that is to stages that include dummy processes. In perspective, these results contribute to show how organisational aspects, such as transfer pricing systems, could be modelled once network technologies are approached from the multiplier (ratio) side.〈/p〉〈/div〉

Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Omega, Volume 87〈/p〉 〈p〉Author(s): 〈/p〉