Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors

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dc.authoridErfan Babaee Trikolaee / 0000-0003-1664-9210en_US
dc.authoridNadi Serhan Aydın / 0000-0002-1453-0016en_US
dc.authorscopusidNadi Serhan Aydın / 55904216900
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidNadi Serhan Aydın / X-8938-2018
dc.authorwosidErfan Babaee Trikolaee / U-3676-2017
dc.contributor.authorGoli, Alireza
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorAydın, Nadi Serhan
dc.date.accessioned2021-02-15T11:18:51Z
dc.date.available2021-02-15T11:18:51Z
dc.date.issued2021en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractIn today's competitive environment, it is essential to design a flexible-responsive manufacturing system with automatic material handling systems. In this study, a fuzzy Mixed Integer Linear Programming (MILP) model is designed for Cell Formation Problem (CFP) including the scheduling of parts within cells in a Cellular Manufacturing System (CMS) where several Automated Guided Vehicles (AGVs) are in charge of transferring the exceptional parts. Notably, using these AGVs in CMS can be challenging from the perspective of mathematical modeling due to consideration of AGVs’ collision as well as parts pickup/delivery. This paper tries to investigate the role of AGVs and human factors as indispensable components of automation systems in the cell formation and scheduling of parts under fuzzy processing time. The proposed objective function includes minimizing the makespan and inter-cellular movements of parts. Due to the NP-hardness of the problem, a hybrid Genetic Algorithm (GA/heuristic) and a Whale Optimization Algorithm (WOA) are developed. The experimental results reveal that our proposed algorithms have a high performance compared to CPLEX and other two well-known algorithms, i.e., Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), in terms of computational efficiency and accuracy. Finally, WOA stands out as the best algorithm to solve the problem. IEEEen_US
dc.identifier.citationGoli, A., Tirkolaee, E. B., & Aydin, N. S. (2021). Fuzzy Integrated Cell Formation and Production Scheduling considering Automated Guided Vehicles and Human Factors. IEEE Transactions on Fuzzy Systems.en_US
dc.identifier.doi10.1109/TFUZZ.2021.3053838en_US
dc.identifier.issn1063-6706en_US
dc.identifier.scopus2-s2.0-85100510363en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://www.doi.org/10.1109/TFUZZ.2021.3053838
dc.identifier.urihttps://hdl.handle.net/20.500.12713/1463
dc.identifier.wosWOS:000724479300013en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTirkolaee, Erfan Babaee
dc.institutionauthorAydın, Nadi Serhan
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Transactions on Fuzzy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCellular Manufacturing Systemen_US
dc.subjectFuzzy Linear Programmingen_US
dc.subjectGA/Heuristic Algorithmen_US
dc.subjectHuman Factorsen_US
dc.subjectInter-Cellular AGVen_US
dc.subjectJob Shop Schedulingen_US
dc.subjectLinear Programmingen_US
dc.subjectProductionen_US
dc.subjectProductivityen_US
dc.subjectRoutingen_US
dc.subjectTransportationen_US
dc.titleFuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factorsen_US
dc.typeArticleen_US

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