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Benders decomposition for the distributionally robust optimization of pricing and reverse logistics network design in remanufacturing systems
- Source :
- European Journal of Operational Research. 297:496-510
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- 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.
- Subjects :
- 050210 logistics & transportation
Mathematical optimization
021103 operations research
Information Systems and Management
General Computer Science
Computer science
media_common.quotation_subject
05 social sciences
0211 other engineering and technologies
Robust optimization
02 engineering and technology
Reverse logistics
Management Science and Operations Research
Solver
Industrial and Manufacturing Engineering
Network planning and design
Safeguard
Modeling and Simulation
0502 economics and business
Quality (business)
Hedge (finance)
Remanufacturing
media_common
Subjects
Details
- ISSN :
- 03772217
- Volume :
- 297
- Database :
- OpenAIRE
- Journal :
- European Journal of Operational Research
- Accession number :
- edsair.doi...........055438c64097824df49a26bae4d38be4