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QMOEA: A Q-learning-based multiobjective evolutionary algorithm for solving time-dependent green vehicle routing problems with time windows.
- Source :
-
Information Sciences . Aug2022, Vol. 608, p178-201. 24p. - Publication Year :
- 2022
-
Abstract
- 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]
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 608
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 159234409
- Full Text :
- https://doi.org/10.1016/j.ins.2022.06.056