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

The vehicle routing problem with time windows (VRPTW) is critical in the fields of opera-tions 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 pro-posed 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.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This study introduces a time-dependent green vehicle routing problem and proposes a Q-learning-based multiobjective evolutionary algorithm to solve the problem. The algorithm considers three objectives: total duration of vehicles, energy consumption, and customer satisfaction. It utilizes a hybrid initial method and Pareto-front-based crossover strategies to improve search efficiency.