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An improved genetic algorithm with variable neighborhood search to solve the assembly line balancing problem
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Production and Automation Engineering)ORCID-id: 0000-0001-5530-3517
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Production and Automation Engineering)
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Production and Automation Engineering)ORCID-id: 0000-0003-0111-1776
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Production and Automation Engineering)ORCID-id: 0000-0003-3973-3394
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2019 (engelsk)Inngår i: Engineering computations, ISSN 0264-4401, E-ISSN 1758-7077Artikkel i tidsskrift (Fagfellevurdert) Epub ahead of print
Abstract [en]
  • Purpose – This study aims to propose an efficient optimization algorithm to solve the assembly line balancing problem (ALBP). The ALBP arises in high-volume, lean production systems when decision makers aim to design an efficient assembly line while satisfying a set of constraints.
  • Design/methodology/approach – An improved genetic algorithm (IGA) is proposed in this study to deal with ALBP in order to optimize the number of stations and the workload smoothness.
  • Findings – To evaluate the performance of the IGA, it is used to solve a set of well-known benchmark problems and a real-life problem faced by an automobile manufacturer. The solutions obtained are compared against two existing algorithms in the literature and the basic genetic algorithm. The comparisons show the high efficiency and effectiveness of the IGA in dealing with ALBPs.
  • Originality/value – The proposed IGA benefits from a novel generation transfer mechanism that improves the diversification capability of the algorithm by allowing population transfer between different generations. In addition, an effective variable neighborhood search is employed in the IGA to enhance its local search capability.
sted, utgiver, år, opplag, sider
Emerald Group Publishing Limited, 2019.
Emneord [en]
assembly line balancing, genetic algorithm, variable neighborhood search, generation transfer
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik
Identifikatorer
URN: urn:nbn:se:his:diva-17157DOI: 10.1108/EC-02-2019-0053Scopus ID: 2-s2.0-85071617279OAI: oai:DiVA.org:his-17157DiVA, id: diva2:1326289
Prosjekter
This study is supported by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 723711 through the MANUWORK project.
Forskningsfinansiär
EU, Horizon 2020, 723711Tilgjengelig fra: 2019-06-18 Laget: 2019-06-18 Sist oppdatert: 2019-09-24bibliografisk kontrollert

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Fathi, MasoodNourmohammadi, AmirNg, Amos H. C.Syberfeldt, Anna

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