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A multi-period, multi-product, multi-echelon, bi-objective, and dynamic closed-loop supply chain network model with fuzzy parameters is put forward in this article. In order to make the model realistic, the uncertainties of capacity, demand, and price are integrated into the model. Thus, a fuzzy bi-objective mixed integer model is developed to maximize the total profit and minimize the wastes of facilities. To deal with the proposed model, a solution method is put forward by incorporating four methods. Computational experiments demonstrate that the proposed hybrid method can find efficient solutions based on CLSC network managers’ risk preferences. And the proposed models and methods in this paper have important managerial implications for decision-makers. [ABSTRACT FROM AUTHOR]

Due to governmental, environmental, and economic concerns, the closed-loop supply chain network has attracted the attention of researchers. In this article, a multiproduct, multi-echelon, and multi-objective closed-loop supply chain network model in a fuzzy environment is proposed. The forward supply chain network includes raw material suppliers, virgin material suppliers, plants, distribution centers, and customer zones, while the reverse supply chain network is composed of customer zones, collection centers, refurbishing centers, dismantling centers, and decomposition centers. The objectives of this model are to minimize the total cost, waste, carbon dioxide, and risks. In order to solve the proposed model, a fuzzy multi-objective optimization method is used. A numerical example validates and verifies the practicability of the fuzzy model and solution method, and demonstrates the proposed model and method can be applied to solve real world problems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]