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Integrated forward and reverse logistics in cloud manufacturing: an agent-based multi-layer architecture and optimization via genetic algorithm

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Abstract

Cloud manufacturing (CMfg) is a new service-oriented manufacturing paradigm that enables distributed collaboration between manufacturers. As logistics between geographically dispersed manufacturers is indispensable in CMfg environment, and the responsibility for the whole lifecycle of products becomes more important for manufacturers, we have taken into account both forward and reverse logistics services in CMfg. Unlike most of the research in the field, this paper not only addresses the special needs of CMfg system from reverse logistics perspective but also considers the transportation of returned products. For this purpose, an agent-based architecture for tackling reverse flows in CMfg is suggested which includes eight agents: buy-back price determiner, reverse flow cost determiner, resell price determiner, return rate forecaster, redistribution of returned products, transportation planner, supplying planner and manufacturing process planner. To demonstrate the applicability of the proposed agent-based architecture, the performance of transportation planner agent is optimized via genetic algorithm. In addition, its performance is compared to a non-intelligent scenario of assigning and sequencing transportation resources in a distributed manufacturing environment. The results show that Transportation Planner agent reduces the transportation costs of CMfg service providers and the time that their vehicles are traveling while being unloaded.

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Authors and Affiliations

  1. Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran

    Simin Hamidi Moghaddam, Hossein Akbaripour & Mahmoud Houshmand

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  1. Simin Hamidi Moghaddam
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  2. Hossein Akbaripour
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Correspondence to Hossein Akbaripour.

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Hamidi Moghaddam, S., Akbaripour, H. & Houshmand, M. Integrated forward and reverse logistics in cloud manufacturing: an agent-based multi-layer architecture and optimization via genetic algorithm. Prod. Eng. Res. Devel. 15, 801–819 (2021). https://doi.org/10.1007/s11740-021-01069-9

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