an efficient genetic algorithm for a vehicle routing problem considering the competency of working teams

this paper presents a new mathematical model for a combined manpower vehicle routing problem, in which working teams are considered as servers. having teams with different competency affects the service duration and cost that expands the flexibility of scheduling. a fleet of vehicles with different speed and cost of movement is used to transport these teams to visit the customers before the due date. the goal is to find an efficient schedule for the teams and vehicles movement to serve all the customers in order to minimize the total cost of serving, routing and lateness penalties. a mixed-integer programming model is presented and a number of tests problems are generated. to solve the large-sized problems, two meta-heuristics approaches, namely genetic algorithm (ga) and particle swarm optimization (pso) are developed, and then the taguchi experimental design method is applied to set the proper values of the parameters. the obtained results show the higher performance of the proposed ga compared with pso in terms of solutions quality within comparatively shorter periods of time.