Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 249, Issue 1, Pages 76-92Publisher
ELSEVIER
DOI: 10.1016/j.ejor.2015.08.028
Keywords
Robustness and sensitivity analysis; Stochastic programming; Robust optimization; Closed-loop supply chain; Benders decomposition
Funding
- National Science Foundation [1130900]
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1130900] Funding Source: National Science Foundation
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Environmental, social and economic concerns motivate the operation of closed-loop supply chain networks (CLSCN) in many industries. We propose a novel profit maximization model for CLSCN design as a mixedinteger linear program in which there is flexibility in covering the proportions of demand satisfied and returns collected based on the firm's policies. Our major contribution is to develop a novel hybrid robust-stochastic programming (HRSP) approach to simultaneously model two different types of uncertainties by including stochastic scenarios for transportation costs and polyhedral uncertainty sets for demands and returns. Transportation cost scenarios are generated using a Latin Hypercube Sampling method and scenario reduction is applied to consolidate them. An accelerated stochastic Benders decomposition algorithm is proposed for solving this model. To speed up the convergence of this algorithm, valid inequalities are introduced to improve the lower bound quality, and also a Pareto-optimal cut generation scheme is used to strengthen the Benders optimality cuts. Numerical studies are performed to verify our mathematical formulation and also demonstrate the benefits of the HRSP approach. The performance improvements achieved by the valid inequalities and Pareto-optimal cuts are demonstrated in randomly generated instances. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.
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