Applying metaheuristic for time-dependent traveling salesman problem in postdisaster

Abstract

The Time-Dependent Traveling Salesman Problem (TDTSP) is a generalization of the Traveling Salesman Problem (TSP) and Traveling Repairman Problem (TRP). In the TSP and TRP, the travel time to travel is assumed to be constant. However, in prac-tice, the travel times vary according to several factors that naturally depend on the time of day. Therefore, the TDTSP is closer to several real practical situations than the TSP. In this paper, we introduce a new variant of the TDTSP, that is, the Time-Dependent Traveling Salesman Problem in Postdisaster (TDTSP-PD). In the problem, the travel costs need to be added debris removal times after a disaster occurs. To solve the TDTSP-PD, we present an effective population-based algorithm that com-bines the diversification power of the Genetic Algorithm (GA) and the intensification strength of Local Search (LS). Therefore, our metaheuristic algorithm maintains a balance between diversification and intensification. The results of the experimental simulation are compared with the well-known and successful metaheuristic algorithms. These results show that the proposed algorithm reaches better solutions in many cases.