Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition

dc.contributor.author Keyvanshokooh, Esmaeil
dc.contributor.author Ryan, Sarah
dc.contributor.author Ryan, Sarah
dc.contributor.author Kabir, Elnaz
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.date 2018-02-17T18:32:55.000
dc.date.accessioned 2020-06-30T04:49:08Z
dc.date.available 2020-06-30T04:49:08Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2016
dc.date.embargo 2017-02-16
dc.date.issued 2016-02-01
dc.description.abstract <p>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.</p>
dc.description.comments <p>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: <a href="http://dx.doi.org/10.1016/j.ejor.2015.08.028" target="_blank">10.1016/j.ejor.2015.08.028</a></p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/imse_pubs/75/
dc.identifier.articleid 1070
dc.identifier.contextkey 8809294
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath imse_pubs/75
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/44597
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/imse_pubs/75/2016_RyanS_HybridRobustStochastic.pdf|||Sat Jan 15 01:49:13 UTC 2022
dc.source.uri 10.1016/j.ejor.2015.08.028
dc.subject.disciplines Industrial Engineering
dc.subject.disciplines Systems Engineering
dc.subject.keywords Robustness and sensitivity analysis
dc.subject.keywords Stochastic programming
dc.subject.keywords Robust optimization
dc.subject.keywords Closed-loop supply chain
dc.subject.keywords Benders decomposition
dc.title Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 22d808f1-c309-4cb1-8d3e-14c57a6b96a9
relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
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