期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 29, 期 12, 页码 3686-3695出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2021.3053838
关键词
Job shop scheduling; Production; Human factors; Transportation; Linear programming; Routing; Productivity; Cellular manufacturing system; fuzzy linear programming; GA; heuristic algorithm; human factors; inter-cellular automated guided vehicle (AGV)
In today's competitive environment, designing a flexible and responsive manufacturing system with automatic material handling systems is essential. This article proposes a fuzzy mixed integer linear programming model for cell formation problems in cellular manufacturing systems, highlighting the important role of AGVs and human factors in automation systems.
In today's competitive environment, it is essential to design a flexible-responsive manufacturing system with automatic material handling systems. In this article, a fuzzy mixed integer linear programming model is designed for cell formation problems 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 article investigates 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 intercellular 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 the other two well-known algorithms, i.e., particle swarm optimization and ant colony optimization, in terms of computational efficiency and accuracy. Finally, WOA stands out as the best algorithm to solve the problem.
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