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Article
Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition
European Journal of Operational Research
  • Esmaeil Keyvanshokooh, University of Michigan-Ann Arbor
  • Sarah M. Ryan, Iowa State University
  • Elnaz Kabir, University of Michigan-Ann Arbor
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
2-1-2016
DOI
10.1016/j.ejor.2015.08.028
Abstract
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 mixed-integer 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.
Comments

NOTICE: this is the author’s version of a work that was accepted for publication in European Journal of Operational Research. 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. A definitive version was subsequently published in European Journal of Operational Research, 249, issue 1, (2016): doi: 10.1016/j.ejor.2015.08.028

Copyright Owner
Elsevier B.V.
Language
en
File Format
application/pdf
Citation Information
Esmaeil Keyvanshokooh, Sarah M. Ryan and Elnaz Kabir. "Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition" European Journal of Operational Research Vol. 249 Iss. 1 (2016) p. 76 - 92
Available at: http://works.bepress.com/sarah_m_ryan/32/