A dynamic robust optimization model for the routing-scheduling problem in humanitarian logistics and its solution via grouping metaheuristic algorithm (case study: Tehran earthquake)

Every year, various natural disasters affect many people around the world. After a disaster occurs, it is critical to dispatch relief resources through efficient emergency logistics distribution so as to alleviate death and suffering of victims. The velocity and efficiency of relief operations depend on logistics capabilities in procurement, receipt, transmission and distribution goods to affected areas. Decision-making at times of a disaster is usually done based on experience. Hence, it is essential that an effective tool is provided in order to manage humanitarian logistics operations. In this paper, two of the main relief logistics decisions are considered in the distribution of resources to the affected areas to include scheduling and routing decisions. For this purpose, a dynamic, multi-commodity, multi-depot, multi-trip linear mathematical programming model is developed under conditions of uncertainty that simultaneously captures many important aspects relevant to the real world. Multi-period planning provides possibilities for planners to adjust plans respect to new events and past actions as more information becomes available and improve the effectiveness of plans. Since the proposed model has its own complexities, a grouping metaheuristic algorithm is presented and this is the first time that the problem is viewed from a grouping perspective. To evaluate the validity and efficiency of the proposed model, a case study for the megacity of Tehran is presented. The findings demonstrate the applicability of the presented model to solve real problems.