An Evolutionary Multi-Objective Approach for Fuzzy Vehicle Routing Problem

In this thesis, Evolutionary Multi-objective Optimization Algorithm for solving Fuzzy Vehicle Routing Problem (FVRP) is described. FVRP is an extension of VRP with Time Windows, which is one of the variants of VRP. In addition to FVRP, Multiple Depot VRP (MDVRP) is used in solving the problem. So, the proposed work and the solution approach is a Fuzzy Multiple Depot VRP (FMDVRP). The objectives that are to be optimized in this solution approach are the minimization of: total travelled distance by vehicles, waiting time of vehicles and customers, and maximization of: load capacity of vehicles and service satisfaction of customers. NSGA-II is a multi-objective optimization algorithm that is used for problems with several objectives to be optimized. In NSGA-II, there is population, which is initialized randomly, and then through several generations a new population is generated from the previous one, and the best of these populations are chosen. The typical genetic operators are applied for generating new population. In addition, NSGA-II uses a new parameter called crowding distance, which is used for better divergence. In experimental results, benchmark problem instances classified by geographical distribution of customers are used in order to compare the results obtained with others. From the results, it is observed that the proposed solution minimizes the waiting time of vehicles by 30% more than the proposed solutions of other researchers.