Designing Closed-Loop Supply Chain Network for High-Salvage Perishable Products Under Uncertain Environment

High-salvage perishable products have the characteristics of a short life cycle and high residual value. To extract the maximum value of this kind of product, a closed-loop supply chain network model based on the product life cycle is constructed under various uncertainties. This model incorporates various assumptions such as multiple products, multiple periods, timeliness of recycling, and four-echelon supply chain including manufacturers, wholesalers, recycling centers, and consumer markets. Where the recovery cost and the reuse rate are denoted by interval numbers, the demand and the capacity are denoted by stochastic and fuzzy numbers respectively. A Two-step interactive method based on fuzzy flexible programming is used to handle the uncertainties. The optimum solutions in the harsh environment and favorable environment are obtained by a numerical example, and the results show the proposed method is more accurate in grey degree and more robust in site selection than the interval chance-constraint mixed-integer nonlinear programming method. Besides, we demonstrate the relationships between the sensitivity coefficients and the profits. i.e., the higher the probability of stockout in the secondary market, the harsher the external environment is, the stockout will get increasing, and the corresponding expected profit will get lower; inversely, the higher the degree of satisfaction in the e-commerce market, the more favorable the external environment is, the stockout will get decreasing, and the corresponding expected profit will get higher. Furthermore, the numerical simulation also shows that the price of recycled products fluctuates with the increase of the probability of no stockout, where the price fluctuation in the secondary market and the e-commerce market are fundamentally the same, while the price of parts fluctuates least. Finally, according to the results of the sensitivity analysis, some managerial insights are given in the conclusion.