Solution approaches for the bi-objective Skiving Stock Problem

dc.authoridSamanlioglu, Funda/0000-0003-3838-8824
dc.authoridKARACA, TOLGA KUDRET/0000-0001-5562-6367
dc.authoridAltay, Ayca/0000-0001-6066-5336
dc.authorwosidSamanlioglu, Funda/H-9126-2016
dc.contributor.authorSamanlıoğlu, Funda
dc.contributor.authorSamanlıoğlu, Funda
dc.contributor.authorAltay, Ayca
dc.date.accessioned2023-10-19T15:12:13Z
dc.date.available2023-10-19T15:12:13Z
dc.date.issued2023
dc.department-temp[Karaca, Tolga Kudret] Topkapi Univ, Dept Comp Engn, TR-34087 Istanbul, Turkiye; [Samanlioglu, Funda] Kadir Has Univ, Dept Ind Engn, TR-34083 Istanbul, Turkiye; [Altay, Ayca] Rutgers State Univ, Dept Ind & Syst Engn, 96 Frelinghuysen Rd, Piscataway, NJ 08540 USAen_US
dc.description.abstractThe Skiving Stock Problem (SSP) aims to determine an optimal plan for producing as many large objects as possible by combining small items. The skiving process may need different considerations depending on the production environment and the product characteristics. In this study, we address bi-objective 1D-SSP with two conflicting objectives. One common objective is to minimize the trim loss remaining after skiving, as removing the excess width is an extra procedure. When welding is an element of the skiving process, increasing the number of items for each product indicates compromised quality. Therefore, minimizing the number of small items for each product becomes a primary objective in such cases. To solve this bi-objective version of the NP-hard problem, we implement a Lexicographic Method (LM) in which the importance of the objectives imposes their preference orders. We propose two methodologies within the LM framework. The first methodology integrates Column Generation (CG) and Branch & Bound (B&B) to search for an exact solution. Given the excessive computational time an exact solver may require for tight or large-sized problems, we propose a heuristic method integrating the Dragonfly Algorithm (DA) and a Constructive Heuristic (CH). Real-world application results validate the exact solver and demonstrate comparable results for the heuristic solver in terms of solution quality and computational time. The efficiency of the solution methodologies for a preemptive multi-objective SSP aims to support decision-makers with make-or-buy decisions.en_US
dc.identifier.citation1
dc.identifier.doi10.1016/j.cie.2023.109164en_US
dc.identifier.issn0360-8352
dc.identifier.issn1879-0550
dc.identifier.issn0360-8352
dc.identifier.issn1879-0550
dc.identifier.scopus2-s2.0-85152596053en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.cie.2023.109164
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5377
dc.identifier.volume179en_US
dc.identifier.wosWOS:000986945600001en_US
dc.identifier.wosqualityQ1
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Industrial Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLinear-Programming ApproachEn_Us
dc.subjectCutting-StockEn_Us
dc.subjectPackingEn_Us
dc.subjectOptimizationEn_Us
dc.subjectInstancesEn_Us
dc.subjectPaperEn_Us
dc.subjectModelEn_Us
dc.subjectLinear-Programming Approach
dc.subjectCutting-Stock
dc.subjectSkiving Stock Problemen_US
dc.subjectPacking
dc.subjectLexicographic methoden_US
dc.subjectOptimization
dc.subjectBi-objective programmingen_US
dc.subjectInstances
dc.subjectColumn generationen_US
dc.subjectPaper
dc.subjectInteger programmingen_US
dc.subjectModel
dc.subjectDragonfly algorithmen_US
dc.titleSolution approaches for the bi-objective Skiving Stock Problemen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication4e74c274-0592-4792-ac57-00061bd273aa
relation.isAuthorOfPublication.latestForDiscovery4e74c274-0592-4792-ac57-00061bd273aa

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