ALBERT

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 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: 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: 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〉