A robust possibilistic optimization model to relief commodities distribution network under uncertainty

One of the most important issues in disaster response phase is to supply the relief items which is needed by affected areas. The uncertainty of this demand causes many problems. This paper presents a novel robust possibilistic programming model for a routing and scheduling problem in a relief commodities distribution network under demand uncertainty. In relief commodities distribution operations, the possibility of servicing each affected area by multiple vehicles and time window constraint have been considered. The objective of the proposed model is to reduce the total time required by the relief vehicles to reach the affected areas. The fourth region of Tehran city as a case study is provided to illustrate the performance and applicability of the proposed model. Finally, to assess the robustness of the solutions obtained by the novel robust optimization model, they are compared to those generated by the deterministic mixed-integer linear programming model in a number of realizations under different test problems.