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dc.contributor.authorGhaniabadi, M.en_US
dc.contributor.authorMazinani, A.en_US
dc.date.accessioned2018-04-12T11:12:56Z
dc.date.available2018-04-12T11:12:56Z
dc.date.issued2017en_US
dc.identifier.issn0360-8352
dc.identifier.urihttp://hdl.handle.net/11693/37420
dc.description.abstractThis paper studies the dynamic lot sizing problem with supplier selection, backlogging and quantity discounts. Two known discount types are considered separately, incremental and all-units quantity discounts. Mixed integer linear programming (MILP) formulations are presented for each case and solved using a commercial optimization software. In order to timely solve the problem, a recursive formulation and its efficient implementation are introduced for each case which result in an optimal and a near optimal solution for incremental and all-units quantity discount cases, respectively. Finally, the execution times of the MILP models and forward dynamic programming models obtained from the recursive formulations are presented and compared. The results demonstrate the efficiency of the dynamic programming models, as they can solve even large-sized instances quite timely. © 2017en_US
dc.language.isoEnglishen_US
dc.source.titleComputers and Industrial Engineeringen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.cie.2017.05.031en_US
dc.subjectBackloggingen_US
dc.subjectDynamic lot sizingen_US
dc.subjectInventory controlen_US
dc.subjectQuantity discountsen_US
dc.subjectSupplier selectionen_US
dc.subjectInteger programmingen_US
dc.subjectInventory controlen_US
dc.subjectBackloggingen_US
dc.subjectDynamic lot-sizingen_US
dc.subjectDynamic programming modelen_US
dc.subjectEfficient implementationen_US
dc.subjectMixed-integer linear programmingen_US
dc.subjectNear-optimal solutionsen_US
dc.subjectQuantity discounten_US
dc.subjectSupplier selectionen_US
dc.subjectDynamic programmingen_US
dc.titleDynamic lot sizing with multiple suppliers, backlogging and quantity discountsen_US
dc.typeArticleen_US
dc.departmentDepartment of Industrial Engineering
dc.citation.spage67en_US
dc.citation.epage74en_US
dc.citation.volumeNumber110en_US
dc.identifier.doi10.1016/j.cie.2017.05.031en_US
dc.publisherElsevier Ltden_US
dc.embargo.release2020-08-01en_US


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