Managing transshipments in a multi-retailer system with approximate policies

dc.citation.epage964en_US
dc.citation.issueNumber6en_US
dc.citation.spage947en_US
dc.citation.volumeNumber66en_US
dc.contributor.authorComez, N.en_US
dc.contributor.authorFescioglu Unver, N.en_US
dc.date.accessioned2015-07-28T12:01:55Z
dc.date.available2015-07-28T12:01:55Z
dc.date.issued2015en_US
dc.departmentDepartment of Managementen_US
dc.description.abstractUsing the right transshipment policy is important when transshipments are exercised under demand uncertainty. Optimal transshipment policy can be quite complex in a multi-firm system as optimal actions depend on all system variables. Moreover, both how to select requested retailer and how to respond to requests are in question. We introduce simple, close-to-optimal heuristic transshipment policies for multiple retailers. We first show that heuristic policies may perform even better than self-optimal policy, which is explained by Braess’s paradox. Then we test the performances of various heuristics with respect to centrally optimal policy. When retailers can observe others’ inventory levels, more effective transshipments can be made. Otherwise, a random selection performs quite well. We also observe that although always-accept respond policy is quite close to centrally optimal in small systems, the performance of pairwise-optimal holdback levels to respond requests is more clear and consistent for larger systems.en_US
dc.identifier.doi10.1057/jors.2014.59en_US
dc.identifier.issn0160-5682
dc.identifier.urihttp://hdl.handle.net/11693/12562
dc.language.isoEnglishen_US
dc.publisherPalgrave Macmillanen_US
dc.relation.isversionofhttp://dx.doi.org/10.1057/jors.2014.59en_US
dc.source.titleJournal of the Operational Research Societyen_US
dc.subjectInventory Sharingen_US
dc.subjectDecentralized Systemen_US
dc.subjectHeuristic Transshipment Policyen_US
dc.subjectBraess’s Paradoxen_US
dc.titleManaging transshipments in a multi-retailer system with approximate policiesen_US
dc.typeArticleen_US
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