Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company

Basit Öğe Kaydı
dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authoridAl-Khaqani, Dhulfiqar Hakeem/0000-0002-5815-0023
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.authorwosidAl-Khaqani, Dhulfiqar Hakeem/AHA-7383-2022
dc.contributor.authorDhayef, Dhulfiqar Hakeem
dc.contributor.authorAl-Zubaidi, Sawsan S. A.
dc.contributor.authorAl-Kindi, Luma A. H.
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2024-05-19T14:51:20Z
dc.date.available2024-05-19T14:51:20Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractCell formation (CF) and machine cell layout are two critical issues in the design of a cellular manufacturing system (CMS). The complexity of the problem has an exponential impact on the time required to compute a solution, making it an NP-hard (complex and non-deterministic polynomial-time hard) problem. Therefore, it has been widely solved using effective meta-heuristics. The paper introduces a novel meta-heuristic strategy that utilizes the Genetic Algorithm (GA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to identify the most favorable solution for both flexible CF and machine layout within each cell. GA is employed to identify machine cells and part families based on Grouping Efficiency (GE) as a fitness function. In contrast to previous research, which considered grouping efficiency with a weight factor (q = 0.5), this study utilizes various weight factor values (0.1, 0.3, 0.7, 0.5, and 0.9). The proposed solution suggests using the TOPSIS technique to determine the most suitable value for the weighting factor. This factor is critical in enabling CMS to design the necessary flexibility to control the cell size. The proposed approach aims to arrange machines to enhance GE, System Utilization (SU), and System Flexibility (SF) while minimizing the cost of material handling between machines as well as inter- and intracellular movements (TC). The results of the proposed approach presented here show either better or comparable performance to the benchmark instances collected from existing literature.en_US
dc.identifier.doi10.1371/journal.pone.0296133
dc.identifier.issn1932-6203
dc.identifier.issue1en_US
dc.identifier.pmid38170733en_US
dc.identifier.scopus2-s2.0-85181628692en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1371/journal.pone.0296133
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5921
dc.identifier.volume19en_US
dc.identifier.wosWOS:001135937000026en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPublic Library Scienceen_US
dc.relation.ispartofPlos Oneen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectGroup-Technologyen_US
dc.subjectManufacturing Systemsen_US
dc.subjectPartsen_US
dc.titleCell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Companyen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu