4.7 Article

Benders decomposition for the distributionally robust optimization of pricing and reverse logistics network design in remanufacturing systems

期刊

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 297, 期 2, 页码 496-510

出版社

ELSEVIER
DOI: 10.1016/j.ejor.2021.04.039

关键词

Location; Reverse logistics network; Quality uncertainty; Distributionally robust optimization; Benders decomposition

资金

  1. National Natural Science Foundation of China [71771135, 71991462]
  2. Beijing Natural Science Foundation [9192011]
  3. Tsinghua University Intelligent Logistics and Supply Chain Research Center [THUCSL20182911756-001]

向作者/读者索取更多资源

The study addresses the pricing and reverse logistics network design problem in remanufacturing with price-dependent return quality uncertainty, proposing a distributionally robust risk-averse model and using a Benders decomposition approach to solve it. Computational experiments show that the distributionally robust model can effectively hedge against high uncertainty, and the enhanced Benders decomposition methods outperform classical counterparts and the off-the-shelf solver Gurobi. Managerial insights and future research directions are also explored.
The pricing and reverse logistics network design problem in remanufacturing has attracted considerable attention in recent years due to increasingly serious environmental problems. In this study, we consider a pricing and reverse logistics network design problem with price-dependent return quality uncertainty. To handle the high uncertainty in return quality, we propose a distributionally robust risk-averse model to safeguard the profits of investors in extreme situations. We propose a Benders decomposition approach to solve the proposed model. It is enhanced through valid inequalities, local branching, in-out variant methods and scenario-based aggregated cuts. Computational experiments demonstrate that the distributionally robust model can effectively hedge against high uncertainty and that the enhanced Benders decomposition methods significantly outperform their classical counterparts and the off-the-shelf solver Gurobi. Lastly, managerial insights are explored, and future research directions are outlined. (c) 2021 Elsevier B.V. All rights reserved.

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