Robust Agricultural Supply Chain Management with Various Random and Fuzzy Parameters

This paper proposes a robust model of multiperiod agricultural supply chain to consider both maximizing the total profit and minimizing the environmental load with random and fuzzy parameters. Our proposed model is formulated as a fuzzy and stochastic, multiobjective and multiperiod programming problem, and hence, it is hard to solve the formulated problem directly without setting a specific random distribution and a specific membership function. Therefore, a distribution-free approach based on sample mean and variance derived from received data, which does not assume any specific random distributions and membership functions, is introduced to apply our proposed model to various uncertain conditions. In addition, deterministic equivalent transformations are also introduced to obtain the optimal solution efficiently using the scenario-based approach.