Hybrid particle swarm optimization with genetic algorithm for solving capacitated vehicle routing problem with fuzzy demand – A case study on garbage collection system

This study intends to propose hybrid particle swarm optimization (PSO) with genetic algorithm (GA) (HPSOGA) for solving capacitated vehicle routing problems with fuzzy demand (CVRPFD). The CVRPFD is developed by using change-constraint program model with credibility measurement. The proposed method uses the idea of a particle’s best solution and the best global solution in a PSO algorithm, then combining it with crossover and mutation of GA. This method also modifies the particle’s coding to ensure that particle always generate a new feasible solution. The proposed method is verified using some CVRPFD datasets which are modified from CVRP instances. Then, it is applied for solving garbage collection system data in Indonesia. Computational results indicate that the proposed HPSOGA outperforms single DPSO and GA for CVRPFD.