Search

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. [ABSTRACT FROM AUTHOR]