QMOEA: A Q-learning-based multiobjective evolutionary algorithm for solving time-dependent green vehicle routing problems with time windows

• Published in: Information sciences Vol. 608; pp. 178 - 201
• Main Authors: Qi, Rui, Li, Jun-qing, Wang, Juan, Jin, Hui, Han, Yu-yan
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.08.2022
• Subjects: customer satisfaction; multiobjective optimization; Q-learning; time dependent; vehicle routing problem with time windows; multiobjective optimization; Q-learning; customer satisfaction; vehicle routing problem with time windows; time dependent
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2022.06.056

The vehicle routing problem with time windows (VRPTW) is critical in the fields of operations research and combinatorial optimization. To promote research on the multiobjective VRPTW, a time-dependent green VRPTW (TDGVRPTW) is introduced in this study. Subsequently, a Q-learning-based multiobjective evolutionary algorithm (QMOEA) is proposed to solve the TDGVRPTW, where three objectives are simultaneously considered: total duration of vehicles, energy consumption, and customer satisfaction. In QMOEA, a hybrid initial method is devised that includes four problem-specific heuristics, to generate initial solutions with a high level of quality and diversity. Then, considering the problem features, two Pareto-front-based crossover strategies are designed to learn from the approximate Pareto front to explore the search space and accelerate the convergence process. Moreover, five local search operators are selected by a Q-learning agent at the decision point, to enhance local search abilities. Finally, a set of instances based on a realistic logistic system is presented to verify the effectiveness and superiority of QMOEA.