Abstract
Risks control has become a hot research area in supply chain management field. Risks indicate the reduction in revenue and profit or additional costs. Therefore, risks should be avoided as far as possible. This paper integrates risks into the design of closed-loop supply chain network and proposes a multi-period, multi-echelon, and bi-objective closed-loop supply chain network model in a fuzzy environment. The forward supply chain network includes component suppliers, assemblers, distribution centers, and customer zones. While the reverse supply chain network is composed of customer zones, recovery centers, and dismantling centers. The objectives of this model are to minimize the risks and total costs. In order to handle this model with risks caused by fuzziness, fuzzy set theory and posteriori method (compromise programming approach) are used. Computational experiments verify the practicability and validation of the fuzzy model and the solution method. The model and the method in this paper provide decision-makers with an effective tool.
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