Decision-making and coordination of a fuzzy closed-loop supply chain with collection and return policy

This paper examines the decision-making and coordination issues within a closed-loop supply chain (CLSC) that incorporates a return policy and product collection in a fuzzy environment. As the CLSC often operate under uncertain environment, triangular fuzzy variables are employed to represent parameters, including potential demand, return quantities, and transfer payments. By using game-theoretic methods, we develop centralized and decentralized decision-making models in both deterministic and fuzzy environments, and apply a recycling effort cost-sharing contract to coordinate the CLSC. We then compare the equilibrium outcomes across different models in both environments and find that, relative to the deterministic setting, the collection rate and profit of the CLSC are enhanced in the fuzzy environment, even under the same decision model. An interesting observation is made: when the fuzziness in the refund price sensitivity parameter changes, the manufacturer’s expected profit is affected more significantly than that of the retailer. Furthermore, our analysis reveals that, within the decentralized decision-making model, the recycling effort cost-sharing contract can achieve a win–win scenario for both the manufacturer and the retailer.