ALBERT

Beschreibung: 〈p〉Publication date: Available online 24 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Claire Hanen, Zdenek Hanzalek〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉This paper is motivated by the repetitive and periodic transmission of messages in Wireless Sensor Networks (WSNs) given by the ZigBee standard. In order to save energy, communication tasks are grouped in time. The WSN applications, such as control loops used in production lines, impose deadlines on the message delivery time.〈/p〉 〈p〉By introducing a grouping constraint, this paper extends the polynomial cyclic scheduling problem of building a periodic schedule with uniform precedence constraints and no resource constraints. We consider that each task belongs to one group, and each group is to be scheduled as a super-task in every period. We show two examples issued from different applications of such grouping constraints, using uniform constraints to model the deadlines expressed in the number of periods. We tackle the feasibility problem (the existence of a periodic schedule), which has shown to be independent of the processing times. We propose a graph formulation of the problem using the retiming technique.〈/p〉 〈p〉In the ZigBee case, we prove that the particular tree structure of the groups and of the uniform precedences based upon the communication tree, lead to a polynomial algorithm to solve the problem. But the general problem is proven to be NP-complete even if no additional resource constraints are considered, and unit processing times are assumed. This extension of the cyclic scheduling leads to a new range of grouping problems with applications, not only in communication networks but also in production and logistics scheduling.〈/p〉 〈/div〉

Beschreibung: 〈p〉Publication date: Available online 24 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Jan Gmys, Mohand Mezmaz, Nouredine Melab, Daniel Tuyttens〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this work we propose an efficient branch-and-bound (B&B) algorithm for the permutation flow-shop problem (PFSP) with makespan objective. We present a new node decomposition scheme that combines dynamic branching and lower bound refinement strategies in a computationally efficient way. To alleviate the computational burden of the two-machine bound used in the refinement stage, we propose an online learning-inspired mechanism to predict promising couples of bottleneck machines. The algorithm offers multiple choices for branching and bounding operators and can explore the search tree either sequentially or in parallel on multi-core CPUs. In order to empirically determine the most efficient combination of these components, a series of computational experiments with 600 benchmark instances is performed. A main insight is that the problem size, as well as interactions between branching and bounding operators substantially modify the trade-off between the computational requirements of a lower bound and the achieved tree size reduction. Moreover, we demonstrate that parallel tree search is a key ingredient for the resolution of large problem instances, as strong super-linear speedups can be observed. An overall evaluation using two well-known benchmarks indicates that the proposed approach is superior to previously published B&B algorithms. For the first benchmark we report the exact resolution – within less than 20 minutes – of two instances defined by 500 jobs and 20 machines that remained open for more than 25 years, and for the second a total of 89 improved best-known upper bounds, including proofs of optimality for 74 of them.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 23 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Lina Al-Kanj, Juliana Nascimento, Warren B. Powell〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We address a comprehensive ride-hailing system taking into account many of the decisions required to operate it in reality. The ride-hailing system is formed of a centrally managed fleet of autonomous electric vehicles which is creating a transformative new technology with significant cost savings. This problem involves a dispatch problem for assigning riders to cars, a surge pricing problem for deciding on the price per trip and a planning problem for deciding on the fleet size. We use approximate dynamic programming to develop high-quality operational dispatch strategies to determine which car is best for a particular trip, when a car should be recharged, when it should be re-positioned to a different zone which offers a higher density of trips and when it should be parked. These decisions have to be made in the presence of a highly dynamic call-in process, and assignments have to take into consideration the spatial and temporal patterns in trip demand which are captured using value functions. We prove that the value functions are monotone in the battery and time dimensions and use hierarchical aggregation to get better estimates of the value functions with a small number of observations. Then, surge pricing is discussed using an adaptive learning approach to decide on the price for each trip. Finally, we discuss the fleet size problem.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 22 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Gustavo Bergantiños, María Gómez-Rúa, Natividad Llorca, Manuel Pulido, Joaquín Sánchez-Soriano〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper deals with the problem of allocating costs in set covering situations. In particular, we focus on set covering situations where the optimal covering is given in advance. Thus, we take into account only the facilities that have to be opened and look for rules distributing their cost. We define a cooperative game and study the core and the nucleolus. We also introduce two new rules: the equal split rule on facilities and the serial rule. We axiomatically characterize the core, the nucleolus, and the two rules. Finally, we study several monotonicity properties of the rules.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 23 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Dr Shari De Baets, Nigel Harvey〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Forecasting support systems allow users to choose different statistical forecasting methods. But how well do they make this choice? We examine this in two experiments. In the first one (N = 191), people selected the model that they judged to perform the best. Their choice outperformed forecasts made by averaging the model outputs and improved with a larger difference in quality between models and a lower level of noise in the data series. In a second experiment (N = 161), participants were asked to make a forecast and were then offered advice in the form of a model forecast. They could then re-adjust their forecast. Final forecasts were more influenced by models that made better forecasts. As forecasters gained experience, they followed input from high-quality models more readily. Thus, both experiments show that forecasters have ability to use and learn from visual records of past performance to select and adjust model-based forecasts appropriately.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 23 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Zixiang Li, Ibrahim Kucukkoc, Zikai Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This research presents a new branch, bound and remember (BBR) algorithm to minimize the number of mated-stations in two-sided assembly lines. The proposed methodology modifies the Hoffman heuristic to achieve high-quality upper bounds, and employs two new dominance rules, referred to as memory-based maximal load rule and memory-based extended Jackson rule, to prune the sub-problems. The BBR algorithm also employs several other improvements to enhance the performance, including renumbering the tasks and new lower bounds. Computational results demonstrate that BBR achieves the optimal solutions for all the tested instances within 1.0 second on average, including two optimal solutions for the first time. Comparative study shows that BBR outperforms the current best exact method (branch and bound algorithm) and the current best heuristic algorithm (iterated greedy search algorithm). As a consequence, the proposed BBR can be regarded as the state-of-the-art method for TALBP.〈/p〉〈/div〉 〈h5〉Graphical Abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S0377221720300539-fx1.jpg" width="230" alt="Image, graphical abstract" title="Image, graphical abstract"〉〈/figure〉〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: 1 June 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 283, Issue 2〈/p〉 〈p〉Author(s): 〈/p〉

Beschreibung: 〈p〉Publication date: Available online 25 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Imadeddine Aziez, Jean-François Côté, Leandro C. Coelho〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In the multi-pickup and delivery problem with time windows (MPDPTW) a set of vehicles must be routed to satisfy a set of client requests between given origins and destinations. A request is composed of several pickups of different items, followed by a single delivery at the client location. This paper introduces two new formulations for the MPDPTW, the 2-index formulation, and the asymmetric representatives formulation. In addition, we also present an existing 3-index formulation for this problem and improve it by means of several preprocessing and valid inequalities. We solve the problem exactly via a branch-and-cut algorithm. We introduce several families of valid inequalities to strengthen the LP relaxations of the proposed formulations. Computational results are reported on different types of instances to firstly highlight the advantage of adding different families of valid inequalities then to compare the performance of the different formulations presented in this paper. While the heuristic and exact algorithms of the literature prove optimality for 16 instances containing up to 50 nodes, we prove optimality for 41 instances for cases containing up to 100 nodes from the existing benchmark set.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 25 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Shu Zhou, Boqian Song, Srinagesh Gavirneni〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We evaluate the impact of bundling on firms’ product and pricing strategies by developing and solving a multi-stage game theoretic model to represent strategic interactions between two competing firms. Each firm is able to produce two homogeneous products and can opt to bundle them together, which may have the dual benefits of providing added value to consumers and reducing marginal cost for firms. One firm (the leader) determines its product offering before the other (the follower), and both then simultaneously set prices. We demonstrate the existence and uniqueness of a Nash equilibrium and show that the option to bundle can benefit all competitors simultaneously. When mixed bundling is not an option, we characterize and quantify the leader’s advantage in terms of profitability. However, when mixed bundling is an option, the follower may reverse its profit disadvantage by using it as a potential threat. Furthermore, our numerical results show that (i) bundling enhances the balance between firms’ total profit and consumer surplus; (ii) for the firm that bundles, value addition and cost saving brought by bundling act as strategic complements in that their combined benefit is larger than the sum of individual benefits; and (iii) value addition and cost saving improve the market demand, firms’ total profit as well as consumer surplus.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 25 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Miguel Alves Pereira, José Rui Figueira, Rui Cunha Marques〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In a world in permanent (r)evolution that revolves around money, seeking new ways to contain costs, better allocate resources, and, overall, improve performance is a constant across all fields. Hence, the use of computational methods based on operational research and statistical science is crucial for achieving an appropriate combination of efficiency and effectiveness, especially in domains where the decision-making process is a complex task. This is where Data Envelopment Analysis (DEA) comes in. However, as a non-parametric and, usually, purely objective technique, DEA makes up for what it lacks in incorporating preference information with flexibility and adaptability, which is particularly important in areas where the decision-makers’ judgements are crucial. This work proposes a cutting-edge and original approach to fill in this knowledge gap by linking DEA and multiple criteria decision-making with an additive DEA model that takes into account criteria interactivity, by using an inference methodology to determine their weights, and decision-makers’ preference information incorporation, by taking advantage of the Choquet multiple criteria preference aggregation model. Thus, this approach was applied to a case study of performance assessment of Portuguese National Healthcare Service secondary healthcare providers across robustness-testing perspectives, generating credible weights stemmed from the decision-maker’s judgements and yielding acceptable and valid results.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 23 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Léopold Simar, Valentin Zelenyuk〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We propose an improvement of the finite sample approximation of the central limit theorems (CLTs) that were recently derived for statistics involving production efficiency scores estimated via Data Envelopment Analysis (DEA) or Free Disposal Hull (FDH) approaches. The improvement is very easy to implement since it involves a simple correction of the variance estimator with an estimate of the bias of the already employed statistics without any additional computational burden and reserves the original asymptotic results such as consistency and asymptotic normality. The proposed approach persistently showed improvement in all the scenarios that we tried in various Monte-Carlo experiments, especially for relatively small samples or relatively large dimensions (measured by total number of inputs and outputs) of the underlying production model. This approach therefore is expected to produce more accurate estimates of confidence intervals of aggregates of individual efficiency scores in empirical research using DEA or FDH approaches and so must be valuable for practitioners. We also illustrate this method using a popular real data set to confirm that the difference in the estimated confidence intervals can be substantial. A step-by-step implementation algorithm of the proposed approach is included in the Appendix.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 17 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Tien Mai, Andrea Lodi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This work concerns the maximum capture facility location problem with random utilities, i.e., the problem of seeking to locate new facilities in a competitive market such that the captured demand of users is maximized, assuming that each individual chooses among all available facilities according to a random utility maximization model. The main challenge lies in the nonlinearity of the objective function. Motivated by the convexity and separable structure of such an objective function, we propose an enhanced implementation of the outer approximation scheme. Our algorithm works in a cutting plane fashion and allows to separate the objective function into a number of sub-functions and create linear cuts for each sub-function at each outer-approximation iteration. We compare our approach with the state-of-the-art method and, for the first time in an extensive way, with other existing nonlinear solvers using three data sets from recent literature. Our experiments show the robustness of our approach, especially on large instances, in terms of both computing time and number instances solved to optimality.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 17 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Quynh Nga Nguyen, Sofiane Aboura, Julien Chevallier, Zhang Lyuyuan, Bangzhu Zhu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We investigate the increased correlations between commodity and U.S. financial markets, as well as among commodity markets from 1992 to 2017 under a non-linear framework. We study how far the phenomenon of financialization is sustainable, given the several significant events that shaped the 2000s. We use a new measure of asymmetric dependence, namely the local Gaussian correlation (Tjøstheim and Hufthammer, 2013), which distinguishes between positive and negative local dependence, to detect whether the dependence between commodities and securities and with each other has become stronger. By examining the local Gaussian correlation before and after the break dates, we can apply the contagion test with a bootstrap procedure. Several robustness checks have been performed. The contagion tests give consistent results with the local Gauss correlation estimates. Our findings confirm the existence of the financialization phenomenon between stock markets and commodity markets particularly after the 08/2008 break date. A remarkable exception to financialization remains the gold market.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 17 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Jully Jeunet, Mayassa Bou Orm〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In spite of its significant contribution to project success, quality has been scarcely addressed in the literature on deterministic project scheduling problems. Although it is recognized that higher qualities are associated with longer processing times, no relationship between quality and resource consumption has been analytically derived to support this statement. As manufacturing projects can be accelerated using additional manpower such as overtime and temporary workers, we derive an analytical relationship between quality and manpower since overtime and overmanning negate quality. We also take into account productivity losses due to overmanning. Contrary to most previous contributions that focus on the project overall quality as an aggregation of quality levels attained at the individual activities, we impose each activity to reach a minimum quality threshold, which is consistent with project management practices. Consequently, we develop a mixed integer linear programming (MILP) to optimize temporary work and overtime so as to accelerate a project with quality and productivity considerations. The objective is to simultaneously determine for each activity the number of permanent, temporary and overtime workers over the processing periods in order to minimize the makespan, the total cost and the overall quality losses subject to individual quality constraints, precedence relationships, nonpreemption and availability of resources. Our approach is successfully applied on numerous instances based on a real project of a high speed locomotive as well as on other projects taken from the literature.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 13 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Hendrik Supper, Felix Irresberger, Gregor N.F. Weiß〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We review several commonly used methods for estimating the tail dependence in a given data sample. In simulations, we show that especially static estimators produce severely biased estimates of tail dependence when applied to samples with time-varying extreme dependence. In some instances, using static estimators for time-varying data leads to estimates more than twice as high as the true tail dependence. Our findings attenuate the need to account for the time-variation in extreme dependence by using dynamic models. Taking all simulations into account, the dynamic tail dependence estimators perform best with the Dynamic Symmetric Copula (DSC) taking the lead. We test our findings in an empirical study and show that the choice of estimator significantly affects the importance of tail dependence for asset prices.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 13 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Zhuolin Wang, Keyou You, Shiji Song, Yuli Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper proposes a data-driven distributionally robust shortest path (DRSP) model where the distribution of the travel time in the transportation network can only be partially observed through a 〈em〉finite〈/em〉 number of samples. Specifically, we aim to find an optimal path to minimize the worst-case 〈em〉α〈/em〉-reliable mean-excess travel time (METT) over a Wasserstein ball, which is centered at the empirical distribution of the sample dataset and the ball radius quantifies the level of its confidence. In sharp contrast to the existing DRSP models, our model is equivalently reformulated as a tractable mixed 0-1 convex problem, e.g., 0-1 linear program or 0-1 second-order cone program. Moreover, we also explicitly derive the distribution achieving the worst-case METT by simply perturbing each sample. Experiments demonstrate the advantages of our DRSP model in terms of the out-of-sample performance and computational complexity. Finally, our DRSP model is easily extended to solve the distributionally robust bi-criteria shortest path problem and the minimum cost flow problem.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 13 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Danilo Marcondes Filho, Marcio Valk〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In the field of Statistical Process Control (SPC) there are several different approaches to deal with monitoring of batch processes. Such processes present a 〈em〉three-way〈/em〉 data structure 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si9.svg"〉〈mrow〉〈mo〉(〈/mo〉〈mi mathvariant="italic"〉batches〈/mi〉〈mo linebreak="goodbreak"〉×〈/mo〉〈mi mathvariant="italic"〉variables〈/mi〉〈mo linebreak="goodbreak"〉×〈/mo〉〈mi mathvariant="italic"〉time-instants〈/mi〉〈mo〉)〈/mo〉〈mo〉,〈/mo〉〈/mrow〉〈/math〉 so that for each batch a multivariate time series is available. Traditional approaches do not take into account the time series nature of the data. They deal with this kind of data by applying multivariate techniques in a reduced 〈em〉two-way〈/em〉 data structure, in order to capture variables dynamics in some way. Recent developments in SPC have proposed the use of the Vector Autoregressive (VAR) time series model considering the original 〈em〉three-way〈/em〉 structure. However, they are restricted to control approaches focused on VAR residuals. This paper proposes a new approach to deal with batch processes focusing on VAR coefficients instead of residuals. In short, we estimate VAR coefficients from historical in-control reference batch samples and build two multivariate control charts to monitoring new batches. We showcase the advantages of the proposed methodology for offline and online monitoring in a simulate example comparing it with the residual-based approach.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 13 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Mukul Gupta, Pradeep Kumar〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In today's era of electronic markets with information overload, generating personalized recommendations for e-commerce users is a challenging and interesting problem. Recommending top-〈em〉N〈/em〉 items of interest to e-commerce users is more challenging using binary implicit feedback. The training data is usually highly sparse and has binary values capturing a user's action or inaction. Due to the sparseness of data and lack of explicit user preferences, neighborhood-based and model-based approaches may not be effective to generate accurate recommendations. Of late, network-based item recommendation methods, which utilize item related meta-information, have started getting attention. In this work, we propose a heterogeneous information network-based recommendation model called HeteroPRS for personalized top-〈em〉N〈/em〉 recommendations using binary implicit feedback. To utilize the potential of meta-information related to items, we use the concept of meta-path. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various meta-paths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback. The proposed model is experimentally evaluated and compared with various recommendation techniques for implicit feedback using real-world datasets, and the results show the effectiveness of the proposed model.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 17 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Yasmin A Rios Solis, Omar J. Ibarra Rojas, Marta Cabo, Edgar Possani〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper studies a lot-sizing and scheduling problem to maximize the profit of assembled products over several periods. The setting involves a plastic injection production environment where pieces are produced using auxiliary equipment (molds) to form finished products. Each piece may be processed in a set of molds with different production rates on various machines. The production rate varies according to the piece, mold and machine assignments. The novelty lies on the problem definition, where the focus is on finished products. We developed a two-stage iterative heuristic based on mathematical programming. First the lot-size of the products is determined together with the mold-machine assignments. The second stage determines if there is a feasible schedule of the molds with no overlapping. If unsuccessful, it goes back to the first stage and restricts the number of machines that a mold can visit, until a feasible solution is found. This decomposition approach allows us to deal with a more complex environment that incorporates idle times and assembly line considerations. We show the advantages of this methodology on randomly generated instances and on data from real companies. Experimental results show that our heuristic converges to a feasible solution with few iterations, obtaining solutions that the companies find competitive both in terms of quality and running times.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 17 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Radek Janostik, Jan Konecny, Petr Krajča〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Logical Analysis of Data and Formal Concept Analysis are separately developed methodologies based on different mathematical foundations. We show that the two methodologies utilize the same basic building blocks. That enables us to develop an interface between the two methodologies. We provide some preliminary benefits of the interface; most notably efficient algorithms for computing spanned patterns in Logical Analysis of Data using algorithms of Formal Concept Analysis.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 15 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Armagan Bayram, Bahriye Cesaret〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉One of the recent trends in omni-channel retailing is ship-from-store which allows a retailer to fulfill online orders by using inventory from a nearby store. The benefits of this fulfillment model include faster delivery, lower shipment costs, higher in-stock probability, increased sales and customer satisfaction, etc. Despite its many benefits, this fulfillment model introduces many new operational challenges to the retailer, including the need to identify from which location to fulfill an online order when it arrives. In this study, we consider a retailer having both online and store operations, with each channel carrying its own inventory. Store orders are fulfilled from store inventories, whereas an online order can be shipped either from an online fulfillment center or from any other store that maximizes the retailer’s overall profit. Our study investigates dynamic fulfillment decisions: from which location to fulfill an online order when it arrives. We incorporate the uncertainty both in demand and in the cost of shipment to individual customers to characterize the optimal cross-channel fulfillment policy. Due to the optimal policy being computationally intractable for large-sized problems, we construct an intuitive heuristic policy to guide the retailers in their fulfillment decisions. We find that the proposed heuristic method is effective and obtains solutions within a reasonable amount of time for the cross-channel fulfillment problem.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 15 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Sharethram Hariharan, Tieming Liu, Zuo-Jun Max Shen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Two effective strategies that mitigate a firm’s demand risk are resource flexibility investment and responsive pricing. In addition to demand uncertainties firms also face capacity uncertainties and capacity disruptions and the effectiveness of these strategies under these risks are less clear. We investigate the value of resource flexibility and responsive pricing under different risk settings for a firm that produces two substitutable products each with its own dedicated resource that can be optionally reconfigured to produce the other product. Reconfiguration or cross-production incurs efficiency loss which can be mitigated by choosing the degree of flexibility of these resources, at a cost, in the planning stage along with capacity levels. In the production stage, after capacities and market potentials are realized, the firm allocates resources and sets prices.〈/p〉 〈p〉We find that under only demand uncertainties the value of flexibility is very low and only a moderate degree of flexibility is sufficient under high demand risk. Responsive pricing is the dominant strategy as the firm avoids investment in costly flexibility. When facing both demand and capacity uncertainties the firm invests in higher levels of flexibility but the value of flexibility is lower than the value of responsive pricing. However, under demand and capacity disruptions flexibility arises as the dominant strategy due to the resource risk pooling effect and the value of flexibility eclipses the value of pricing as the firm invests in full flexibility. For a firm with responsive pricing investment in flexibility is economically justified under high capacity uncertainties and capacity disruptions.〈/p〉 〈/div〉

Beschreibung: 〈p〉Publication date: Available online 11 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): José Brandão〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The problem studied in this paper is the multi-depot open vehicle routing problem, which has the following two differences in relation to the classical vehicle routing problem: there are several depots; the vehicles do not return to the depot after delivering the goods to the customers, i.e., the end of the route is not the starting point. There are many practical applications for this problem, however the great majority of the studies have only addressed the open vehicle routing problem with a single depot. In this paper, we present an iterated local search algorithm, in which the moves performed during the local search are recalled and this historical search information is then used to define the moves executed inside the perturbation procedures. Therefore, it is recorded the number of times that each customer is moved during the local search. Since this information is continuously updated and changes in each iteration, the search is driven to potentially better regions of the solution space, and increases the chance of avoiding cycling, even when using deterministic perturbations. The performance of this algorithm was tested using a large set of benchmark problems and was compared with other algorithms, and the results show that it is very competitive.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 17 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Shaofu Du, Jing Peng, Tengfei Nie, Yugang Yu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉With consideration of the ambiguity and overfunding effects, this study develops generalized models to compare the sequential and simultaneous mechanisms in reward-based crowdfunding. Ambiguity effect is a negative psychological perception of customers since they lack the information to infer the product’s quality, however, overfunding effect is a positive psychological perception that enhances customers’ confidence in the product’s quality. Theoretically, we show that the two mechanisms may dominate each other in terms of entrepreneur and the two opposite effects can play significant roles in deciding the optimal crowdfunding mechanism. Furthermore, we propose a simple compensation mechanism to improve entrepreneur’s expected payoff and the crowdfunding’s success rate under both mechanisms. Finally, we analyse the informational cascade in crowdfunding and show how entrepreneur can use this phenomenon to maximize her expected payoff.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 17 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Marcello Galeotti, Giovanni Rabitti, Emanuele Vannucci〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Building on several contributions to the analysis of insurance fraud, we propose a dynamical model of the 〈em〉fraudulence game〈/em〉, where three typologies of players interact: the insurance company, the fraudsters and the honest insured (who may be tempted to become dishonest), each one taking decisions on the basis of an adaptive strategy.〈/p〉 〈p〉It follows from the mathematical analysis that several scenarios and different asymptotic outcomes of the 〈em〉game〈/em〉 are possible. In all cases, managerial/actuarial interpretations and implications are provided, suggesting how insurers can adapt proper control policies both to evolving behaviours of policyholders and to different external (economical, geographical, social) contexts.〈/p〉 〈/div〉

Beschreibung: 〈p〉Publication date: Available online 17 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Mariano Jiménez, Amelia Bilbao-Terol, Mar Arenas-Parra〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In multi-objective optimization models, it is common that the decision maker expresses the relative importance of objectives through a weighting scheme. However, many solving techniques do not assure that the corresponding solution fits the preferential weights. It could be the case that an objective with a very low weight achieves a good value, whereas another with a high weight yields a very poor achievement. In order to overcome the aforementioned drawback, this paper proposes a new resolution method based on the well-known Reference Point Method. The methodology consists in generating a sequence of Reference Point Method models which share the same reference point fixed at the vector of preferential weights. In the iterative process, the projection direction on the Pareto frontier changes in each iteration according to the deviations between the preferential weights and the current normalised objective values. In this way, a sequence of Pareto-efficient solutions is generated which converges towards a solution that best fits the decision maker's preferential weights. The proposed method is illustrated by means of a numerical example. In order to show its feasibility and usefulness, the methodology is applied to a portfolio selection problem where the corporate sustainability performance of each firm is taken into account.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 17 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Cristiano Arbex Valle, John E Beasley〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉In this paper we investigate the problem of order batching for picker routing. Our approach is applicable to warehouses (storage areas) arranged in the standard rectangular grid layout, so with parallel aisles and two or more cross-aisles. The motivation underlying our work is online grocery shopping in which orders may be composed of dozens of items. The approach presented directly addresses order batching, but uses a distance approximation to influence the batching of orders without directly addressing the routing problem.〈/p〉 〈p〉We present a basic formulation based on deciding the orders to be batched together so as to optimise an objective that approximates the picker routing distance travelled. We extend our formulation by improving the approximation for cases where we have more than one block in the warehouse. We present constraints to remove symmetry in order to lessen the computational effort required, as well as constraints that significantly improve the value of the linear programming relaxation of our formulation. A heuristic algorithm based on partial integer optimisation of our mathematical formulation is also presented. Once order batching has been decided we optimally route each individual picker using a previous approach presented in the literature.〈/p〉 〈p〉Extensive computational results for publicly available test problems involving up to 75 orders are given for both single and multiple block warehouse configurations.〈/p〉 〈/div〉

Beschreibung: 〈p〉Publication date: Available online 7 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Sami Serkan Özarık, Banu Lokman, Murat Köksalan〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We study and exploit the characteristics of the nondominated sets of Multi-objective Integer Programs (MOIPs). We introduce a density measure and search for common properties of the distributions of nondominated points for different MOIPs. We design a procedure that categorizes the nondominated set into regions based on the densities of nondominated points. We develop an approach that generates representative sets of nondominated points using the estimated density information in different regions for general MOIPs. Experiments show that our approach is robust across different types of MOIPs.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 7 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Jianchang Fan, Debing Ni, Xiang Fang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We consider a two-echelon supply chain in which an upstream manufacturer (M) and a downstream retailer (R) share the product liability cost caused by quality defects. We assume that M and R share the expected liability cost per unit of the low-quality product (the ULQ liability cost). We use two respective Stackelberg games to model two different channel leadership structures: the manufacturer-led Stackelberg (MS) and the retailer-led Stackelberg (RS). We first obtain the analytical subgame perfect equilibria for the games. Subsequently, we investigate the impacts of the ULQ liability cost sharing on the product quality and the pricing decisions and on the profitability for supply chain members and the entire supply chain in equilibrium. We find that (1) in response to a shift of the share of the ULQ liability cost from R to M, the leader M under the MS structure is able to use a higher wholesale price to transfer its increased ULQ liability cost completely to R without the need to improve its product quality, while the leader R under the RS structure would decrease its retail margin to incentivize M to improve its product quality; (2) such a shift of the ULQ liability cost sharing does not have any impact on the supply chain efficiency under the MS structure, but it enhances the supply chain efficiency under the RS structure; (3) despite the different responses of the leaders under the MS and the RS structures, we are able to establish a same set of contracts combining quantity discounts with quality improvement cost sharing to coordinate the supply chain, regardless of the channel leadership structures.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 5 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Thomas Setzer, Sebastian M. Blanc〈/p〉

Beschreibung: 〈p〉Publication date: Available online 3 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Yossi Bukchin, Tal Raviv, Ilya Zaides〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We study a variant of the multiprocessor job scheduling problem, where jobs are processed by several identical machines. The machines are ordered in a sequence, and each job is processed by several consecutive machines simultaneously. The jobs are characterized by their processing time, the number of required consecutive machines, and their ready time. The objective function is to minimize the sum of general functions defined over the completion time of each job. This study is motivated by a real problem in the semiconductor industry. We present a time-indexed integer programming and a constraint programming formulations for the problem and demonstrate their applicability through an extensive numerical study and an industrial case study.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 3 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Dilson Lucas Pereira, Alexandre Salles da Cunha〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we introduce a dynamic Dantzig-Wolfe (DW) reformulation framework for the Adjacent Only Quadratic Minimum Spanning Tree Problem (AQMSTP). The approach is dynamic in the sense that the structures over which the DW reformulation takes place are defined on the fly and not beforehand. The idea is to dynamically convexify multiple promising regions of the domain, without explicitly formulating DW master programs over extended variable spaces and applying column generation. Instead, we use the halfspace representation of polytopes as an alternative to mathematically represent the convexified region in the original space of variables. Thus, the numerical machinery we devise for computing AQMSTP lower bounds operates in a cutting plane setting, separating projection cuts associated to the projection of the variables used in the extended DW reformulations. Our numerical experience indicates that the bounds are quite strong and the computational times are mostly spent by linear programming reoptimization and not by the separation procedures. Thus, we introduce a Lagrangian Relax-and-cut algorithm for approximating these bounds. The method is embedded in a Branch-and-Bound algorithm for the AQMSTP. Although it does not strictly dominate the previous state-of-the-art exact method, it is able to solve more instances to proven optimality and is significantly faster for the hardest AQMSTP instances in the literature.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 27 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Panagiotis Tziogkidis, Dionisis Philippas, Alexandros Leontitsis, Robin C. Sickles〈/p〉

Beschreibung: 〈p〉Publication date: Available online 24 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): K. Maclean, F. Ødegaard〈/p〉

Beschreibung: 〈p〉Publication date: Available online 19 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Saamrat Kasina, Benjamin F. Hobbs〈/p〉

Beschreibung: 〈p〉Publication date: Available online 19 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Anna Timonina-Farkas, Argyro Katsifou, Ralf W. Seifert〈/p〉

Beschreibung: 〈p〉Publication date: Available online 15 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Sandun Perera, Varun Gupta, Winston Buckley〈/p〉

Beschreibung: 〈p〉Publication date: Available online 14 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Tianbao Qin, Yuquan Du, Jiang Hang Chen, Mei Sha〈/p〉

Beschreibung: 〈p〉Publication date: Available online 15 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): T.H. Bhuiyan, H.R. Medal, S. Harun〈/p〉

Beschreibung: 〈p〉Publication date: Available online 15 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Gonzalo Mejía, Francisco Yuraszeck〈/p〉

Beschreibung: 〈p〉Publication date: Available online 15 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Amin Hocine, Zheng-Yun Zhuang, Noureddine Kouaissah, Der-Chiang Li〈/p〉

Beschreibung: 〈p〉Publication date: Available online 14 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Collin Drent, Minou Olde Keizer, Geert-Jan van Houtum〈/p〉

Beschreibung: 〈p〉Publication date: Available online 11 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Anthony J. Glass, Karligash Kenjegalieva, Mustapha Douch〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The principal contribution of this paper is to present the first method to sift through a large number of firms in an industry to uncover which firms act as large spatial total factor productivity (TFP) growth centers. We define a large spatial TFP growth center as a firm that is a large net generator of spatial TFP growth spillovers, i.e., it is a source of large TFP growth spill-outs to other firms vis-à-vis the size of the TFP growth spill-ins that permeate to the firm from other firms. We use this definition because, other things being equal, firms would want to locate near a firm that is a net generator of TFP growth spillovers. In the process of presenting the above method we make three further contributions, two of which are methodological and the other relates to our application. First, rather than follow the literature on spatial frontier modeling by considering spatial interaction between firms in a single network, we introduce a more sophisticated model that is able to account for spatial interaction in multiple networks. Second, we obtain bidirectional spatial TFP growth decompositions by complementing a unidirectional decomposition in the literature, where the spillover components are spill-ins to a firm, with a decomposition that includes spill-out components. Third, from a spatial revenue frontier for U.S. banks 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si98.svg"〉〈mrow〉〈mo〉(〈/mo〉〈mn〉1998〈/mn〉〈mo linebreak="goodbreak"〉−〈/mo〉〈mn〉2015〈/mn〉〈mo〉)〈/mo〉〈mo〉,〈/mo〉〈/mrow〉〈/math〉 we find a number of cases where banks that represent large spatial TFP growth centers have branches that cluster together, while in several states we find no such clusters.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 10 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Dylan Huizing, Guido Schäfer, Rob D. van der Mei, Sandjai Bhulai〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper studies a setting in emergency logistics where emergency responders must also perform a set of known, non-emergency jobs in the network when there are no active emergencies going on. These jobs typically have a preventive function, and allow the responders to use their idle time much more productively than in the current standard. When an emergency occurs, the nearest responder must abandon whatever job he or she is doing and go to the emergency. This leads to the optimisation problem of timetabling jobs and moving responders over a discrete network such that the expected emergency response time remains minimal. Our model, the Median Routing Problem, addresses this complex problem by minimising the expected response time to the next emergency and allowing for re-solving after this. We describe a mixed-integer linear program and a number of increasingly refined heuristics for this problem. We created a large set of benchmark instances, both from real-life case study data and from a generator. On the real-life case study instances, the best performing heuristic finds on average a solution only 3.4% away from optimal in a few seconds. We propose an explanation for the success of this heuristic, with the most pivotal conclusion being the importance of solving the underlying 〈em〉p〈/em〉-Medians Problem.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 8 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Rui Peng Liu, Alexander Shapiro〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The aim of this paper is to show that in some cases risk averse multistage stochastic programming problems can be reformulated in a form of risk neutral setting. This is achieved by a change of the reference probability measure making “bad” (extreme) scenarios more frequent. As a numerical example we demonstrate advantages of such change-of-measure approach applied to the Brazilian Interconnected Power System operation planning problem.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 7 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Olivier Briant, Hadrien Cambazard, Diego Cattaruzza, Nicolas Catusse, Anne-Laure Ladier, Maxime Ogier〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Order picking is the process of retrieving products from inventory. It is mostly done manually by dedicated employees called pickers and is considered the most expensive of warehouse operations. To reduce the picking cost, customer orders can be grouped into batches that are then collected by traveling the shortest possible distance.〈/p〉 〈p〉This work presents an exponential linear programming formulation to tackle the joint order batching and picker routing problem. Variables, or columns, are related to the picking routes in the warehouse. Computing such routes is generally an intractable routing problem and relates to the well known traveling salesman problem (TSP). Nonetheless, the rectangular warehouse’s layouts can be used to efficiently solve the corresponding TSP and take into account in the development of an efficient subroutine, called oracle. We therefore investigate whether such an oracle allows for an effective exponential formulation.〈/p〉 〈p〉Experimented on a publicly available benchmark, the algorithm proves to be very effective. It improves many of the best known solutions and provides very strong lower bounds. Finally, this approach is applied to another industrial case to demonstrate its interest for this field of application.〈/p〉 〈/div〉

Beschreibung: 〈p〉Publication date: Available online 31 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): M. Ambrosius, J. Egerer, V. Grimm, Adriaan H. van Der Weijde〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Ongoing policy discussions on the reconfiguration of bidding zones in European electricity markets induce uncertainty about the future market design. This paper deals with the question of how this uncertainty affects market participants and their long-run investment decisions in generation and transmission capacity. Generalizing the literature on pro-active network expansion planning, we propose a stochastic multilevel model which incorporates generation capacity investment, network expansion, and market operation, taking into account uncertainty about the future bidding zone configuration. Using a stylized two-node network, we disentangle different effects that uncertainty has on market outcomes. If there is a possibility that future bidding zone configurations provide improved regional price signals, welfare gains materialize even if the change does not actually take place. As a consequence, welfare gains of an actual change of the bidding zone configuration are substantially lower due to those anticipatory effects. Additionally, we show substantial distributional effects in terms of both expected gains and risks, between producers and consumers and between different generation technologies.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 1 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Guoyin Jiang, Jennifer Shang, Wenping Liu, Xiaodong Feng, Junli Lei〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Online review system (ORS) can unite members who share similar interests in topic discussion or knowledge exchange that entails dynamics and complexity. In this work, we propose a multiagent system to replicate the evolution of social bonds and online reviews in ORS. To validate the proposed method, we use big data from a real-world ORS collected over a period of 153 months. Results show that the proposed agent-based model can accurately predict the construction of bonds and the volumes of online reviews. Moreover, social moderation, economic moderation, and the combined moderating mechanism can motivate ORS members to post reviews. The stage (phase) of the review life cycle and the degree of moderation significantly impact the effectiveness of the mechanisms. Depending on the stage of the review life cycle, social and economic moderators have different degrees of success in generating online reviews. In the early stage, economic moderation is effective, whereas social moderation works better in the late stage. When the moderating levels are medium or high, the combined social and economic moderation is much better than the stand-alone mechanism. Although social bonds are positively associated with more posting, none of the moderating mechanisms (economic, social, or combined) can significantly enhance the bond. To manage ORS effectively, managers need to switch from the conventional static view (relying on single theory) to the dynamic view. In addition, they should incorporate social exchange, motivation, and social network concepts at different stages of the ORS life cycle.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 1 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): H. Wang, Chen Pan, Qunwei Wang, P. Zhou〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Measuring the sustainability performance of supply chains is fundamental to sustainable supply chain management. Sustainability performance is usually evaluated from multiple aspects within the triple bottom line framework. With globalization, supply chains have also been characterized by the complex and global natures. Ignoring the multidimensional and transnational features imposes challenges on the performance assessment of global supply chains (GSCs). To resolve this issue, we propose an input-output modeling approach based on the multi-region input-output (MRIO) model and the data envelopment analysis (DEA) technique, which is able to account for the multidimensional characteristic of supply chains in a global context. Two indices are introduced to measure the status and evolvement of environmental sustainability performance of GSCs. We apply the proposed approach to empirically examine the environmental performance of GSCs of the manufacturing sectors in 16 major economies during 2005-2014. The average environmental inefficiency of the economies was considerable, and roughly 40% of the pollution could potentially be reduced along GSCs. Overall the environmental performance of GSCs averagely rose by 20.6% during the study period with fluctuations and regional/sectoral heterogeneities observed.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 5 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Lena Wolbeck, Natalia Kliewer, Inês Marques〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The nurse rescheduling problem denotes the problem of finding a new, feasible schedule when a disruption occurs during schedule operation. In most cases, rescheduling is needed when a nurse requires short-term absence and a substitution is essential to maintain care. Basically, rescheduling focuses on minimizing the shift changes between the current and the new schedule in order to best avoid disturbing the schedule operation and so prevent nurse dissatisfaction due to shift changes. Constraints within the nurse rescheduling problem may differ depending on the case. In this study, we develop a general optimization model that can be adjusted to various cases with different characteristics. The model incorporates a fair shift change penalization scheme, in which the type, timing and distribution of shift changes among nurses are taken into account. In addition, information from previous periods is considered, using an individual penalty score to distribute the shift changes fairly among the nurses. Based on the proposed penalization scheme and model, optimized schedules are generated in a short computational time for instances from literature, real-world based instances from a Portuguese hospital, and real instances from a care facility from Germany. The results of the computational study demonstrate the practical applicability of the approach. For most problem instances, the number of shift changes corresponds to the minimum number or deviates only slightly from it. At the same time, a much fairer distribution of these changes among nurses can be achieved.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 17 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Xun Zhou, Timo Kuosmanen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Transition towards a low-carbon transport sector fundamentally depends on decarbonization of the passenger car fleet. Therefore, it is critically important to understand the driving factors behind decreasing CO〈sub〉2〈/sub〉 emissions of new passenger cars. This paper develops a new decomposition method to break down the change in the average CO〈sub〉2〈/sub〉 emissions of new passenger cars into components representing changes in available technology, carbon efficiency of consumer choices, vehicle attributes, fuel mix, and the gap between type-approval and on-road CO〈sub〉2〈/sub〉 emissions of passenger cars. Our decomposition draws insights from the traditional index decomposition analysis and frontier-based decomposition of productivity growth. It satisfies such desirable properties as factor reversal, time reversal, and zero-value robustness. An empirical application to a unique data set that covers all registered passenger cars in Finland sheds light on why and how the CO〈sub〉2〈/sub〉 emissions of new cars decreased from year 2002 to year 2014.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 18 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Mike G. Tsionas, Athanasios Andrikopoulos〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This article provides an alternative to High-Dimensional Model Representation using a Copula approximation of an unknown functional form. We apply our methodology in the context of an extensive Monte Carlo study and to a sample of large US commercial banks. In the Monte Carlo experiment, the approximations errors of the Copula approach are small and behave randomly. In our empirical application, we find that the Copula Approximation performs much better, in terms of Bayes factors for model comparison, compared to High-Dimensional Model Representation, which, in turn, provides better results when compared with standard flexible functional forms, like the translog, the minflex Laurent, and the Generalized Leontief, or a Multilayer Perceptron. Moreover, the choice of approximation has significant implications for productivity and its components (returns to scale, technical inefficiency, technical change, and efficiency change).〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 17 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Jan Christiaens, Hatice çalik, Tony Wauters, Reshma Chirayil Chandrasekharan, Greet Vanden Berghe〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Prisoners often require transportation to and from services such as hospital appointments, court proceedings and family visits during their imprisonment. Organising daily prisoners transportation consumes a huge amount of resources. A large fleet of highly protected vehicles, their drivers and security guards must be assigned to all prisoner transports such that all safety and time-related constraints are satisfied while inter-prisoner (inter-passenger) conflicts are avoided. It is beyond human planners’ capabilities to minimize costs while attempting to feasibly schedule all prisoner transportation requests. Whereas the prisoner transportation problem (PTP) bears resemblance with vehicle routing, common software systems for vehicle routing fail to address the intricacies associated with the PTP. A dedicated decision support system is required to both support human planners as well as reduce operational costs. The considerable computational challenge due to problem-specific components (inter-passenger conflicts and simultaneous servicing) also makes the PTP interesting from an academic point of view. We formally introduce the problem by providing mixed integer programming models. We implement exact iterative procedures to solve these formulations and evaluate their performance on small instances. In order to solve instances of a realistic size, we present a heuristic. Academic PTP instances generated and employed for experimentation are made publicly available with a view towards encouraging further follow-up research. The heuristic presented in this paper provides all the necessary components to solve the PTP adequately and sets initial benchmarks for the new public instance set.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 17 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Ekin Can Erkuş, Vilda Purutçuoğlu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Outlier detection is one of the main challenges in the pre-processing stage of data analyses. In this study, we suggest a new non-parametric outlier detection technique which is based on the frequency-domain and Fourier Transform definitions and call it as the frequency-domain based outlier detection (FOD). From simulation results under various distributions and real data applications, we observe that our proposal approach is capable of detecting quasi-periodic outliers in time series data more successfully compared with other commonly used methods like z-score, box-plot and also faster than some specialized methods Grubbs method and autonomous anomaly detection (AAD) method. Therefore, we consider that our proposal approach can be an alternative approach to find quasi-periodic outliers in time series data.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 15 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Saltuk Buğra Selçuklu, David W. Coit, Frank A. Felder〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Many engineering problems have multiple conflicting objectives, and they are also stochastic due to inherent uncertainties. One way to represent the multi-objective nature of problems is to use the Pareto optimality to show the trade-off between objectives. Pareto optimality involves the identification of solutions that are not dominated by other solutions based on their respective objective functions. However, the Pareto optimality concept does not contain any information about the uncertainty of solutions. Evaluation and comparison of solutions becomes difficult when the objective functions are subjected to uncertainty. A new metric, the Pareto Uncertainty Index (PUI), is presented. This metric includes uncertainty due to the stochastic coefficients in the objective functions as part of the Pareto optimality concept to form an extended probabilistic Pareto set, we define as the 〈em〉p〈/em〉-Pareto set. The decision maker can observe and assess the randomness of solutions and compare the promising solutions according to their performance of satisfying objectives and any undesirable uncertainty. The PUI is an effective and convenient decision-making tool to compare promising solutions with multiple uncertain objectives.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 15 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Rainer Kleber, Marc Reimann, Gilvan C. Souza, Weihua Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We study the competition between two remanufacturers in the acquisition of used products and the sales of remanufactured products. One firm has a market advantage; we consider two separate cases where either firm could have an acquisition advantage. The problem is formulated as a simultaneous game on a market that is vertically differentiated in both acquisition and sales, where both firms decide on their respective acquisition prices for used products, and selling prices for remanufactured products. A key finding is that a market advantage is significantly more powerful than an acquisition advantage. The firm with a market advantage can preempt the entry of the other firm, even if that firm has a significant acquisition advantage, but not the other way around. This is accomplished through an aggressive acquisition strategy, where the firm with a market advantage sets significantly higher acquisition prices.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 13 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Konstantinos Nikolopoulos〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Operational Research (OR) is the ‘science of better’. People constantly try to get better, in practically all aspects of their personal and professional life, and thus OR is de facto a ubiquitous science. What might however not be that clear, is that the way we improve is driven by the very OR science, and the scientific results that constitute the respective body of literature, theory and practice. Of all the tasks, that can be related to the OR science, the one that more frequently we do, is forecasting. We do constantly try to estimate what is coming next, and we drive our decisions for each and every situation based on these forecasts: from where to put our money to who will be the best surgeon to operate us. Within the broad boundaries of OR, forecasting stands out as the most ubiquitous sub-discipline. In the forecasting literature, a lot of attention has been given to modelling fast-moving time series and building causal models; however, very limited attention has been given to intermittent time series and intermittent demand forecasting. In this research, we advocate for the broader use of intermittent demand forecasting methods for forecasting special events, as a simpler, faster, and robust alternative to more complex non-OR models. Furthermore, in a foresight context, we argue for a novel way of deciding the strategic planning horizon for phenomena prone to appearance of special events.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 11 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Yanhai Li, Jinwen Ou〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A firm (assembler) faces random demand for a final product which is made up of multiple complementary components. Before demand is realized, the firm purchases the components via a regular channel. After demand realization, unsatisfied demand is allowed to be partially backordered in case of component shortages, where the firm is assumed to have an option to purchase the components via an emergency channel with a relatively higher unit cost. The firm needs to adopt an appropriate ordering policy to maximize the 〈em〉spectral risk measure〈/em〉 of its profit. We formulate and transform the newsvendor problem into a concise optimization problem which can be further decomposed into two sub-problems. We provide optimality properties and show that the objective function of each sub-problem is separable, which enables us to determine the optimal order quantity of each component independently. We show that the optimal order quantity of each component decreases in the firm's level of risk-aversion. Numerical experiments are conducted to illustrate the effectiveness of our solution method and examine the impacts of lost sales and risk attitude on the optimal solutions.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 11 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Carol Alexander, Johannes Rauch〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We classify all functions of multivariate stochastic processes having time-series estimates that are independent of data frequency. Such an estimator applied to high-frequency data may be used to infer properties of estimates relating to low-frequency data. Our property encompasses two previously-proposed time-aggregation properties (with limited solutions) as different special cases. Our general time-aggregating functions satisfy a pair of coupled second-order partial differential equations. We derive analytic solutions for arbitrary-dimensional martingales and log-martingales. The time-aggregation property of a time-series model is similar – indeed time-aggregating functions always correspond to point estimators based on expected values – but we do not propose a specific new forecasting model. However, we do derive time-aggregating unbiased and efficient estimators for 〈em〉n〈sup〉th〈/sup〉〈/em〉-order moments of log returns, applying these results to problems facing portfolio managers who re-optimise portfolios or hedge their risks at lower frequencies than the frequency at which their risk premia are monitored.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 10 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Evangelos Spiliotis, Vassilios Assimakopoulos, Spyros Makridakis〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The Theta method became popular due to its superior performance in the M3 forecasting competition. Since then, although it has been shown that Theta provides accurate forecasts for various types of data, being a solid benchmark to beat, limited research has been conducted to exploit its full potential and generalize its reach. This paper examines three extensions on Theta’s framework to boost its performance. This includes (i) considering both linear and non-linear trends, (ii) allowing to adjust the slope of such trends, and (iii) introducing a multiplicative expression of the underlying forecasting model along with the existing, additive one. The proposed modifications transform Theta into a generalized forecasting algorithm, suitable for automatic time series predictions. The proposed algorithm is evaluated using the series of the M, M3, and M4 competitions. Such an evaluation shows that the proposed approach produces more accurate forecasts than the original, classic Theta, both in terms of point forecasts and prediction intervals, and is also more accurate than other well-known methods for yearly series.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 10 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Alexander C. Kalloniatis, Timothy A. McLennan-Smith, Dale O. Roberts〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We advance a mathematical representation of Command and Control as distributed decision makers using the Kuramoto model of networked phase oscillators. The phase represents a continuous Perception-Action cycle of agents at each network node; the network the formal and informal communications of human agents and information artefacts; coupling the strength of relationship between agents; native frequencies the individual decision speeds of agents when isolated; and stochasticity temporal noisiness in intrinsic agent behaviour. Skewed heavy-tailed noise captures that agents may randomly ‘jump’ forward (rather than backwards) in their decision state under time stress; there is considerable evidence from organisational science that experienced decision-makers behave in this way in critical situations. We present a use-case for the model using data for military headquarters staff tasked to drive a twenty-four hour ‘battle-rhythm’. This serves to illustrate how such a mathematical model may be used realistically. We draw on a previous case-study where headquarters’ networks were mapped for routine business and crisis scenarios to provide advice to a military sponsor. We tune the model using the first data set to match observations that staff performed synchronously under such conditions. Testing the impact of the crisis scenario using the corresponding network and heavy-tailed stochasticity, we find increased probability of decision incoherence due to the high information demand of some agents in this case. This demonstrates the utility of the model to identify risks in headquarters design, and potential means of identifying points to change. We compare to qualitative organisational theories to initially validate the model.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 9 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Marina Leal, Diego Ponce, Justo Puerto〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper presents novel bilevel leader-follower portfolio selection problems in which the financial intermediary becomes a decision-maker. This financial intermediary decides on the unit transaction costs for investing in some securities, maximizing its benefits, and the investor chooses his optimal portfolio, minimizing risk and ensuring a given expected return. Hence, transaction costs become decision variables in the portfolio problem, and two levels of decision-makers are incorporated: the financial intermediary and the investor. These situations give rise to general Nonlinear Programming formulations in both levels of the decision process. We present different bilevel versions of the problem: financial intermediary-leader, investor-leader, and social welfare; besides, their properties are analyzed. Moreover, we develop Mixed Integer Linear Programming formulations for some of the proposed problems and effective algorithms for some others. Finally, we report on some computational experiments performed on data taken from the Dow Jones Industrial Average, and analyze and compare the results obtained by the different models.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 3 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Christopher M. Durugbo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Problem structuring methods (PSMs) are operational research (OR) techniques commonly used to investigate and capture the multidimensional and complex nature of situations. This article proposes a novel PSM, termed the situational affordance structuring approach (SASA), for structuring work situations with a view to identifying opportunities for workplace innovation. SASA involves a series of sense-making principles to define work systems, source operational data, gather domain information, and represent situational knowledge using an abstraction-decomposition space (ADS) diagram. The ADS representation is premised on the theory of affordances which posits that objects and events possess inherent meaning as detected and exploited by users. The article intends to support the OR community by introducing the ADS as a novel approach to represent situations during problem structuring and presents findings from a case example involving the use of SASA for industrial design. The case involves an expert-led approach to unravelling perceived messiness in work situations with particular emphasis on representing the situational knowledge of the industrial domain using an ADS. SASA is a novel and unique PSM in two main ways: (i) it is inspired by ecological psychology with an emphasis on the affordances of work situations, and (ii) it uses the ADS as an artefact to illustrate the superior-subordinate arrangements associated with perceived messiness in structured (or semi-structured) work situations. The article also discusses the research implications, practical relevance of the study for the OR field, research limitations and possible future research directions.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 26 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Sebastian Kohl, Jens O. Brunner〈/p〉

Beschreibung: 〈p〉Publication date: Available online 27 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Mohammad E. Nikoofal, Mehmet Gümüş〈/p〉

Beschreibung: 〈p〉Publication date: Available online 26 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): I. Aldana-Galván, J.C. Catana-Salazar, J.M. Díaz-Báñez, F. Duque, R. Fabila-Monroy, M.A. Heredia, A. Ramírez-Vigueras, J. Urrutia〈/p〉

Beschreibung: 〈p〉Publication date: Available online 26 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Tomas Baležentis, Kai Sun〈/p〉

Beschreibung: 〈p〉Publication date: Available online 26 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Stefan Schwerdfeger, Nils Boysen〈/p〉

Beschreibung: 〈p〉Publication date: Available online 26 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Frederic Ang, Pieter Jan Kerstens〈/p〉

Beschreibung: 〈p〉Publication date: Available online 26 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Bert M. Balk, José L. Zofío〈/p〉

Beschreibung: 〈p〉Publication date: Available online 22 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Zhimin Guan, Tong Ye, Rui Yin〈/p〉

Beschreibung: 〈p〉Publication date: Available online 19 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Ratnaji Vanga, Jayendran Venkateswaran〈/p〉

Beschreibung: 〈p〉Publication date: Available online 19 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Y. Meinard, O. Cailloux〈/p〉

Beschreibung: 〈p〉Publication date: Available online 17 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): A. Saavedra-Nieves, P. Saavedra-Nieves〈/p〉

Beschreibung: 〈p〉Publication date: Available online 14 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Yann Bouchery, Johan Woxenius, Jan C. Fransoo〈/p〉

Beschreibung: 〈p〉Publication date: Available online 14 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Maritza E. Cervantes-Gaxiola, Erik F. Sosa-Niebla, Oscar M. Hernández-Calderón, José M. Ponce-Ortega, Jesús Raúl Ortiz-del-Castillo, Eusiel Rubio-Castro〈/p〉

Beschreibung: 〈p〉Publication date: Available online 13 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Koen W. De Bock, Kristof Coussement, Stefan Lessmann〈/p〉

Beschreibung: 〈p〉Publication date: Available online 11 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Victor Martínez-de-Albéniz, Abdel Belkaid〈/p〉

Beschreibung: 〈p〉Publication date: Available online 11 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Timofey Shalpegin〈/p〉

Beschreibung: 〈p〉Publication date: Available online 11 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): İbrahim Muter〈/p〉

Beschreibung: 〈p〉Publication date: Available online 7 February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Maryam Karimi-Mamaghan, Mehrdad Mohammadi, Payman Jula, Amir Pirayesh, Hadi Ahmadi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we present an agile integrated inspection-operation planning model wherein inspection actions are planned alongside the machining operations to make the production process agile. Such an agile integrated plan can respond quickly to inspection-machining needs while still controlling costs and quality. A tri-objective mixed-integer nonlinear programming (TMINLP) model is developed for planning the integrated process in a serial multi-stage production (MSP) system. This model addresses several inter-related decisions; 1) what is the most appropriate inspection process for a quality characteristic, 2) at which stage the inspection of these quality characteristics should be performed, 3) how these inspections should be performed, 4) which inspection tools should be used, and 5) which machine should operate on products. The three objectives are: 1) minimizing the total manufacturing cost, 2) minimizing the number of nonconforming products shipped, and 3) minimizing the total manufacturing time for each product. We also address the uncertainty of manufacturing parameters and equipment disruptions. To solve the model, a novel learning-based metaheuristic is developed based on Multi-Objective Differential Evolution (MODE) algorithm, 〈em〉k〈/em〉-Means clustering method, and an Iterated Local Search (ILS) algorithm. The proposed learning-based metaheuristic algorithm is then integrated with the Taguchi Loss Function and Monte Carlo methods to address the input parameters’ uncertainty. The proposed model and solution algorithm are validated through a set of experiments against optimal solutions, and benchmarked against four existing well-known approaches, i.e. NSGA-II, MODE and two learning-based metaheuristics. The proposed approach is applied to a real industrial case and insights are provided.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 30 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Tsan-Ming Choi, Shu Guo, Na Liu, Xiutian Shi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉On-demand service platforms are popular nowadays. Many platforms hire agents to serve customers who are risk sensitive towards the waiting-time. In this paper, we apply the mean-risk theory to analytically explore how the risk attitude of customers affects the optimal service pricing decision of the on-demand platform, consumer surplus (CS) of customers, the expected profit (EP) and profit risk (PR) of the platform (and the hired service agents). In the basic model, assuming consumers are homogeneous, we find that if the customers are more risk averse (risk seeking), the optimal service price will drop (increase). Comparing among the three different risk attitudes of customers, we find that when the customers are risk seeking, the CS and the platform's EP are highest, even though the platform's PR is also highest. While the opposite happens when the customers are risk averse. In the extended model with a market including customers with different risk attitudes, the blockchain technology helps the platform assess the proportion of risk seeking, risk neutral and risk averse customers accurately. We explore the optimal service prices under both the common pricing policy and the customized pricing policy (with-respect-to customer's risk attitude), and derive the value of blockchain technology mediated customized service pricing strategy. We conclude by highlighting that the risk attitudes of customers play a critical role in determining the optimal on-demand service pricing, and the blockchain technology is a valuable technological tool to help.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 30 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Pouya Baniasadi, Mehdi Foumani, Kate Smith-Miles, Vladimir Ejov〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The clustered generalized traveling salesman problem (CGTSP) is an extension of the classical traveling salesman problem (TSP), where the set of nodes is divided into clusters of nodes, and the clusters are further divided into subclusters of nodes. The objective is to find the minimal route that visits exactly one node from each subcluster in such a way that all subclusters of each cluster are visited consecutively. Due to the additional flexibility of the CGTSP compared to the classical TSP, CGTSP can incorporate a wider range of complexities arising from some practical applications. However, the absence of a good solution method for CGTSP is currently a major impediment in the use of the framework for modeling. Accordingly, the main objective of this paper is to enable the powerful framework of CGTSP for applied problems. To attain this goal, we first develop a solution method by an efficient transformation from CGTSP to TSP. We then demonstrate that not only the solution method provides far superior solution quality compared to existing methods for solving CGTSP, but also it enables practical solutions to far larger CGTSP instances. Finally, to illustrate that the modeling framework and the solution method apply to some practical problems of realistic sizes, we conduct a computational experiment by considering the application of CGTSP to two modern logistics problems; namely, automated storage and retrieval systems (logistics inside the warehouse) and drone-assisted parcel delivery service (logistics outside the warehouse).〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 28 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Shannon L. Harris, Jerrold H. May, Luis G. Vargas, Krista M. Foster〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper studies how appointment cancellations affect scheduling strategies in outpatient healthcare clinics. While cancellation rates in outpatient clinics have been reported to be as high as 27%, cancelled appointments are often ignored, or grouped with no-shows in healthcare scheduling models. We find that there exists a value of total demand that, when calculated over a scheduling horizon, marks the boundary between where cancellations hurt or help a clinic. We refer to that value as the 〈em〉switch point〈/em〉. Up to the switch point, clinics can achieve a greater reward when patients do not cancel. However, for values of expected total demand greater than the switch point, the clinic reward is reduced if more patients retain (do not cancel) appointments. To assist us in evaluating the switch point, we construct a mixed-integer nonlinear programming model to solve a multi-day outpatient scheduling problem. The model accounts for both inter-day (appointment day) and intra-day (appointment time slot) scheduling decisions, while balancing service benefits against service costs. We include probabilities of no-show and cancellation, which allows us to discuss how cancellations affect scheduling decisions through the switch point. The knowledge of the switch point allows a clinic to understand when appointment no-shows and cancellations negatively affect clinic service, and can assist the clinic in determining the number of patients for which it is committed to provide service, i.e., its panel size. In this paper, we discuss methodologies for calculating the switch point, and discuss its sensitivity to model parameters.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 28 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Tsan-Ming Choi, Shu Guo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In the real world, some manufacturers supply their products to retailers at a zero wholesale price (ZWP), and receive compensation by sharing the revenue or receiving some side payment from the retailers. In this paper, we analyse this kind of ZWP-based supply-chain contract. In the basic model, ordering is done either by the manufacturer or the retailer. For both of these cases, we explore ZWP-revenue-sharing (ZR) contracts, ZWP-side-payment (ZS) contracts, and ZWP-revenue-sharing-plus-side-payment (ZRS) contracts. We prove that, irrespective of the ordering scenario being retailer-led or manufacturer-led, only a ZRS contract can achieve win-win coordination. In the extended models, we first study the scenario with multiple products and discuss how a generalised ZRS contract can coordinate the supply chain efficiently. We then investigate greedy wholesale price (GWP)–based contracts, in which the manufacturer charges the retailer a wholesale price equal to the retail price. We find that a GWP-based revenue-sharing-plus-side-payment (GRS) contract and a ZRS contract can both achieve win-win coordination. However, the ZRS contract mean-variance dominates the GRS contract in bringing a lower level of risk to both the retailer and manufacturer if the unit production cost is sufficiently small. We further discuss cases of differences in the perception of ZWP versus non-ZWP contracts. Important managerial insights are derived from the findings.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 29 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Alan T. Murray, Richard L. Church, Xin Feng〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Single facility siting is often viewed as the most basic of location planning problems. It has been approached by many researchers, across a range of disciplines, and has a rich and distinguished history. Much of this interest reflects the general utility of single facility siting, but also the mathematical and computational advancements that have been made over past decades to support better decision making. This paper discusses a recently rediscovered form of Weber's classic single facility location problem that is both important and relevant in contemporary planning and decision making. This form of the Weber problem involves locating a production plant where there are multiple sources of each needed raw material (input) distributed throughout a region. This means that the selection of a given raw material source may vary depending on the plant location. In essence, this makes the problem non-convex, even when locating only one production plant. We review elements of the Weber problem that have been addressed in the literature along with proposed solution techniques. In doing so, we highlight elements of the problem originally noted by Weber, but to date have not been operationalized in practice – allocation selection among multiple sources of given raw material inputs. A problem formulation involving allocation decisions for this generalization is derived and an optimal solution approach is developed. Application results demonstrate the significance of addressing important planning characteristics and the associated nuances that result.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 28 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Régis Chenavaz, Gustav Feichtinger, Richard F. Hartl, Peter M. Kort〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The marketing-mix of price-quality and advertising-quality relationship is well studied. Less understood is the price-advertising-quality relationship. This article fills the gap, investigating the interplay between price, advertising, and quality in an optimal control model. Our results generalize the condition of Dorfman-Steiner in a dynamic context. Also, they point to the impact of greater product quality on the dynamic policies of pricing and advertising. Furthermore, a phase diagram analysis shows that quality develops monotonically in time and converges to a unique steady state. We also show that quality investment could either decrease or increase over time but this depends on its effectiveness. Our results spot the profitable opportunities of a firm managing a more complex marketing-mix.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 27 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Erin Baker, Valentina Bosetti, Ahti Salo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Inspired by challenges in designing energy technology policy in the face of climate change, we address the problem of decision making under “deep uncertainty.” We introduce an approach we call Robust Portfolio Decision Analysis, building on Belief Dominance as a prescriptive operationalization of a concept that has appeared in the literature under a number of names. The Belief Dominance concept synthesizes multiple conflicting sources of information to uncover alternatives that are intelligent responses in the presence of many beliefs. We use this concept to determine the set of non-dominated portfolios and to identify corresponding robust individual alternatives, thereby uncovering viable alternatives that may not be revealed otherwise. Our approach is particularly appropriate with multiple stakeholders, as it helps identify common ground while leaving flexibility for negotiation. We develop a proof-of-concept application aimed at informing decisions over investments into clean energy technology R&D portfolios in the context of climate change and illustrate how Robust Portfolio Decision Analysis helps identify robust individual investments.〈/p〉〈/div〉