A Two-Stage Multiobjective Evolutionary Algorithm for Multiobjective Multidepot Vehicle Routing Problem With Time Windows

This paper proposes a multiobjective multidepot vehicle routing problem with time windows and designs some real-world test instances. It develops a two-stage multiobjective evolutionary algorithm (TS-MOEA) for dealing with the problem. Stage I of our proposed algorithm focuses on finding extreme solutions, and forms a coarse Pareto front, while stage II extends the found extreme solutions for approximating the whole Pareto front. The two-stage strategy provides a new method to balance convergence and diversity. Moreover, a hybrid neighborhood structure is designed for solution improvement. Experimental result shows that TS-MOEA significantly outperforms two other representative algorithms.