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• W2894847423 abstract "Cross-industry learning of the Toyota production system has inspired the precast factories in the construction industry to adopt an automated guided vehicle (AGV)-based flow production system for the manufacturing of their modular prefabricated products. Compared to the production process of automobiles, the manufacturing process of modular prefabricated products is very unbalanced leading to a large pool of queues. And additionally, after some operations, settling is needed. Hence, due to these unique features, facility layout is a crucial element that needs to be well planned in order to achieve a feasible and efficient system. In this regard, this paper proposes an approach to plan the facility layout of the investigated AGV-based modular prefabricated manufacturing system. The paper firstly gives an optimization method for the size arrangement of the workstation area and the storage area. There are two conflicting objectives in the optimization model: one is to minimize the production time and the other is to maximize the workstation utilization. A simulation based non-dominated sorting genetic algorithm is developed to solve the model. Then, the paper proposes a heuristic method to guide the placement, reshuffle, and retrieval of the modular prefabricated products in the storage area. According to the heuristic, there is no need of dedicated paths for AGVs. The storage area can be fully occupied by the work-in-progress and the AGV traveling paths are dynamically generated. And thirdly, the paper is also able to provide a suitable size of the AGV fleet which is able to accomplish the moving tasks in time. The experimental test on an industrial case shows the potential of the proposed planning approach to guide the real practice." @default.
• W2894847423 created "2018-10-12" @default.
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• W2894847423 date "2019-02-01" @default.
• W2894847423 modified "2023-10-14" @default.
• W2894847423 title "Optimal facility layout planning for AGV-based modular prefabricated manufacturing system" @default.
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• W2894847423 doi "https://doi.org/10.1016/j.autcon.2018.08.008" @default.
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