A decomposition approach to a stochastic model for supply-and-return network design

This paper presents a generic stochastic model for the design of networks comprising both supply and return channels, organized in a closed loop system. Such situations are typical for manufacturing/re-manufacturing type of systems in reverse logistics. The model accounts for a number of alternative scenarios, which may be constructed based on critical levels of design parameters such as demand or returns. We propose a decomposition approach for this model based on the branch and cut procedure known as the integer L-shaped method. Computational results show a consistent performance efficiency of the method for the addressed location problem. The stochastic solutions obtained in a numerical setting generate a significant improvement in terms of average performance over the individual scenario solutions. A solution methodology as presented here can contribute to overcoming notorious challenges of stochastic network design models, such as increased problem sizes and computational difficulty.Decomposition;Location;Remanufacturing;Integer L-shaped;Uncertainty

A decomposition approach to a stochastic model for supply-and-return network design

This paper presents a generic stochastic model for the design of networks comprising both supply and return channels, organized in a closed loop system. Such situations are typical for manufacturing/re-manufacturing type of systems in reverse logistics. The model accounts for a number of alternative scenarios, which may be constructed based on critical levels of design parameters such as demand or returns. We propose a decomposition approach for this model based on the branch and cut procedure known as the integer L-shaped method. Computational results show a consistent performance efficiency of the method for the addressed location problem. The stochastic solutions obtained in a numerical setting generate a significant improvement in terms of average performance over the individual scenario solutions. A solution methodology as presented here can contribute to overcoming notorious challenges of stochastic network design models, such as increased problem sizes and computational difficulty

Methods and Analysis for Recovery Logistics Networks with Uncertainty and Channel Selection Considerations

In this dissertation, we develop models and methodologies for effective design and efficient operation of product recovery logistics networks. Recovery networks, employed for recycle-reuse-refurbish-remanufacture purposes, constitute an ever-expanding portion of supply chain networks. For such activities to make business-sense, it is important that the logistical decisions associated with designing and operating underlying networks are made carefully. With this main motivation, we focus on two fundamental problems. First, we consider a generic Closed-Loop Supply Chain (CLSC) network setting under demand and return uncertainty and provide a new model and an efficient solution approach for the associated network design problem. Consideration of uncertainties and their impact on the CLSC network design is a largely ignored area in the literature, thus, this work contributes to closing this gap, in both modeling and solution methodology contexts, as well as in analysis. Second, we consider the specific case of commercial returns, which is quite common in today's business climate, given the generous return policies provided by electronics and department stores as well as retail superstores. In this setting, for operational efficiency and financial effectiveness, it is important for providers to best determine appropriate return channels, i.e., the return channel selection, for commercial products whose values decrease over time. Return channel selection for commercial products is also a largely ignored area in the literature. We first address this problem from an operational efficiency perspective given an underlying network of facilities. In the related models and analysis, we introduce and capture the concepts of channel selection dependence on product and logistics network characteristics. Later, recognizing that the design of an underlying network may be under the control of the provider, we take an integrated design and operation perspective and incorporate the logistics network design into the model to further study dependence of channel selection on network characteristics. In addition to new models and analysis for commercial return logistics, our contributions also include the development of efficient solution algorithms with measurable solution quality. We introduce the problems of interest and their context in today's business environment in the first chapter. In the second chapter of the dissertation, we develop a two-stage stochastic programming model for the generic CLSC network design problem under demand and return uncertainty, represented by a set of scenarios. For the model's solution, we develop a Benders Decomposition (BD) approach that significantly improves computational efficiency via surrogate constraints, strengthened Benders cuts, multiple Benders cuts, and mean value scenario based lower bounding inequalities. In the third chapter, we develop models for the channel selection problem for commercial products under time-value consideration. Based on this model, we analyze the optimal return channel selection strategies under varying underlying logistics network and product characteristics. For this purpose, we utilize real geographical data from the U.S. and product data for Hewlett Packard and Bosch. In the fourth chapter of this dissertation, we develop a Mixed Integer Linear Programming (MILP) model for integrated design and channel selection for commercial product returns under product time-value consideration. For the model's solution, we develop an efficient algorithm based on the Simulated Annealing (SA) approach, benchmarking the quality of solutions against the upper bound obtained by a Benders Decomposition approach. Using this model and the solution approach, we provide an extensive analysis of the relationship between recovery logistics network structure and product characteristics

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Integrating Closed-loop Supply Chains and Spare Parts Management at IBM

Ever more companies are recognizing the benefits of closed-loop supplychains that integrate product returns into business operations. IBMhas been among the pioneers seeking to unlock the value dormant inthese resources. We report on a project exploiting product returns asa source of spare parts. Key decisions include the choice of recoveryopportunities to use, the channel design, and the coordination ofalternative supply sources. We developed an analytic inventory controlmodel and a simulation model to address these issues. Our results showthat procurement cost savings largely outweigh reverse logistics costsand that information management is key to an efficient solution. Ourrecommendations provide a basis for significantly expanding the usageof the novel parts supply source, which allows for cutting procurementcosts.supply chain management;reverse logistics;product recovery;inventory management;service management