An effective evolutionary algorithm for the practical capacitated vehicle routing problems

The evolutionary algorithms are extensively adopted to resolve complex optimization problem. Genetic algorithm (GA), an evolutionary algorithm, has been proved capable of solving vehicle routing problems (VRPs). However, the resolution effectiveness of GA decreases with the increase of nodes within VRPs. Normally, a hybrid GA outperforms pure GA. This study attempts to solve a capacitated vehicle routing problem (CVRP) by applying a novel hybrid genetic algorithm (HGA) that is practical for use by manufacturers. The proposed HGA involves three stages. First, a diverse and well-structured initial chromosome population was constructed. Second, response surface methodology (RSM) experiments were conducted to optimize the crossover and mutation probabilities in performing GA. Finally, a combined heuristics containing improved insertion algorithm and random insertion mutation operator was established to stir over gene permutations and enhance the exploration capability of GA diversely. Furthermore, an elitism conservation strategy was implemented that replace inferior chromosomes with superior ones. As the proposed HGA is primarily used to solve practical problems, benchmark problems involving fewer than 100 nodes from an Internet website were utilized to confirm the feasibility of the proposed HGA. Two real cases one for locally active distribution and another for arms part transportation at a combined maintenance facility, both involving the Taiwanese armed forces are used to detail the analytical process and demonstrate the practicability of the proposed HGA for optimizing the CVRP.