Joint Supply Chain Finance and Fuzzy Robust Order Decision with unknown Distribution of Demand

This paper addresses the supply chain financing and ordering problem, where the retailer acts as an intermediary to provide guarantees for the supplier’s loans. Traditional supply chain financing investigations usually assume that the demand distribution is known, whereas such complete information cannot be obtained for problems with high uncertainty. In addition, high uncertainty often makes decision-makers pay more attention to the robustness of the strategy. To address these challenges, this paper combines subjective judgment with robust optimization, using fuzzy sets to express expert judgment, and constructs a new robust optimization objective. This optimization objective incorporates risk aversion through a worst-case optimization framework, which can be more in line with decision-makers’ behavioral characteristics. Through comparative analysis, it is concluded that the fuzzy robust ordering strategy proposed in this paper is more reasonable than the traditional optimal expected strategy and the Max–Min robust strategy. Also, it mitigates the extreme conservatism of traditional robust models, providing a more pragmatic approach to handling uncertainty.