A new bi-stage interactive possibilistic programming model for perishable logistics distribution systems under uncertainty.

Setting the optimal vehicle routes and the optimal level of inventory to avoid shortage, in addition to reducing costs of transportation, are two of the main objectives in developing the distribution systems of perishable goods. Despite its importance, an area that still requires more investigation is the simultaneous consideration of hub location selection and routing and warehousing operations. In other words, addressing decisions related to the locations of the warehouses, vehicle routing, and warehouse management at the same time could improve the outcome. Therefore, this paper presents a new non-linear mixed-integer model for warehouse location selection, in addition to an integrated multi-objective mixed-integer model for routing and warehousing operations. Moreover, features of real operations are considered, such as dynamic conditions, order picking, and delivery, customer prioritization, time windows for customers, and different perishable goods. Minimizing the operations' costs and time under uncertain conditions is achieved by proposing a possibilistic-robust optimization algorithm. The optimization approach regards the weight of each objective using the opinions of experts by using a novel hesitant fuzzy approach. Finally, a case study is presented, and the results are compared with the actual data along with validating the model by using several numerical examples. The results have shown that the method can perfectly form the supply chain of perishable goods. Furthermore, the optimization method can provide the experts with more flexibility in finding a compromise solution. [ABSTRACT FROM AUTHOR]