A Decomposition-Based Hybrid Algorithm for Multi-objective Vehicle Routing Problem with Time Windows

Yiwen Wang, Fei Liu, Qingfu Zhang

Abstract: The Vehicle routing problems (VRP) are one of the most studied combinatorial optimization problems. This paper targets an important and challenging VRP variant, named multi-objective vehicle routing problems with time windows. We propose to use a multi-objective evolutionary algorithm based on decomposition (MOEA/D) to decompose the problem into a set of single-objective sub-problems. For each sub-problem, efficient crossover and local search heuristics are adopted to generate and improve new solutions. We design two new strategies to decrease the number of vehicles, a population management method with infeasible solutions and a multi-split strategy. Experimental studies are carried out on the well-known Solomon's dataset. Results suggest that our proposed algorithm is very competitive compared to two state-of-the-art algorithms. It generates better solutions on 90% of the test instances.