Mathematical Model And Agent Based Solution Approach For The Simultaneous Balancing And Sequencing Of Mixed-Model Parallel Two-Sided Assembly Lines

dc.authorid0000-0002-1561-0923en_US
dc.authorid0000-0001-6042-6896en_US
dc.authorid0000-0001-6042-6896en_US
dc.contributor.authorKüçükkoç, İbrahim
dc.contributor.authorZhang, David Z
dc.date.accessioned2019-10-17T07:01:15Z
dc.date.available2019-10-17T07:01:15Z
dc.date.issued2014en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.descriptionKüçükkoç, İbrahim (Balikesir Author)en_US
dc.description.abstractOne of the key factors of a successfully implemented mixed-model line system is considering model sequencing problem as well as the line balancing problem. In the literature, there are many studies, which consider these two tightly interrelated problems individually. However, we integrate the model sequencing problem in the line balancing procedure to obtain a more efficient solution for the problem of Simultaneous Balancing and Sequencing of Mixed-Model Parallel Two-Sided Assembly Lines. A mathematical model is developed to present the problem and a novel agent based ant colony optimisation approach is proposed as the solution method. Different agents interact with each other to find a near optimal solution for the problem. Each ant selects a random behaviour from a predefined list of heuristics and builds a solution using this behaviour as a local search rule along with the pheromone value. Different cycle times are allowed for different two-sided lines located in parallel to each other and this yields a complex problem where different production cycles need to be considered to build a feasible solution. The performance of the proposed approach is tested through a set of test cases. Experimental results indicate that considering model sequencing problem with the line balancing problem together helps minimise line length and total number of required workstations. Also, it is found that the proposed approach outperforms other three heuristics tested.en_US
dc.description.sponsorshipBalikesir University Turkish Council of Higher Educationen_US
dc.identifier.doi10.1016/j.ijpe.2014.08.010
dc.identifier.endpage333en_US
dc.identifier.issn1873-7579
dc.identifier.issn0925-5273
dc.identifier.scopus2-s2.0-84913600698
dc.identifier.scopusqualityQ1
dc.identifier.startpage314en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijpe.2014.08.010
dc.identifier.urihttps://hdl.handle.net/20.500.12462/7423
dc.identifier.volume158en_US
dc.identifier.wosWOS:000345729500029
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherElsevier Science Bven_US
dc.relation.ispartofInternational Journal of Production Economicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectAssembly Line Balancingen_US
dc.subjectMixed-Model Parallel Two-Sided Assembly Linesen_US
dc.subjectSimultaneous Line Balancing and Model Sequencingen_US
dc.subjectAgent Based Ant Colony Optimisationen_US
dc.subjectProduction Linesen_US
dc.subjectMeta-Heuristicsen_US
dc.subjectArtificial Intelligenceen_US
dc.titleMathematical Model And Agent Based Solution Approach For The Simultaneous Balancing And Sequencing Of Mixed-Model Parallel Two-Sided Assembly Linesen_US
dc.typeArticleen_US

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