Closed-Loop Supply Chain Network Design under Uncertainties Using Fuzzy Decision Making

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2021-03-10
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Hu, Zhengyang
Parwani, Viren
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Hu, Guiping
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Industrial and Manufacturing Systems Engineering
The Department of Industrial and Manufacturing Systems Engineering teaches the design, analysis, and improvement of the systems and processes in manufacturing, consulting, and service industries by application of the principles of engineering. The Department of General Engineering was formed in 1929. In 1956 its name changed to Department of Industrial Engineering. In 1989 its name changed to the Department of Industrial and Manufacturing Systems Engineering.
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The importance of considering forward and backward flows simultaneously in supply chain networks spurs an interest to develop closed-loop supply chain networks (CLSCN). Due to the expanded scope in the supply chain, designing CLSCN often faces significant uncertainties. This paper proposes a fuzzy multi-objective mixed-integer linear programming model to deal with uncertain parameters in CLSCN. The two objective functions are minimization of overall system costs and minimization of negative environmental impact. Negative environmental impacts are measured and quantified through CO2 equivalent emission. Uncertainties include demand, return, scrap rate, manufacturing cost and negative environmental factors. The original formulation with uncertain parameters is firstly converted into a crisp model and then an aggregation function is applied to combine the objective functions. Numerical experiments have been carried out to demonstrate the effectiveness of the proposed model formulation and solution approach. Sensitivity analyses on degree of feasibility, the weighing of objective functions and coefficient of compensation have been conducted. This model can be applied to a variety of real-world situations, such as in the manufacturing production processes.

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This article is published as Hu, Zhengyang, Viren Parwani, and Guiping Hu. "Closed-Loop Supply Chain Network Design under Uncertainties Using Fuzzy Decision Making." Logistics 5, no. 1 (2021): 15. DOI: 10.3390/logistics5010015. Posted with permission.

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Fri Jan 01 00:00:00 UTC 2021
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