Abstract
In recent years, the impact of the epidemic has led to a gradual recovery of the logistics industry, bringing new opportunities to improve the country's economy. Therefore, the study of the vehicle routing problem with time windows (VRPTW) is of great practical importance. To the best of our knowledge, most studies only consider the distance in customer space and ignore the effect of time windows on objectives. In this paper, we propose an improved genetic algorithm (IGA) to solve the vehicle routing problem with time windows considering temporal-spatial distance (VRPTWTSD). In the proposed algorithm, a hybrid initialization method is first designed, which includes two problem-specific methods, namely the temporal-spatial distance insertion heuristic (TSDIH) and the earliest ready time heuristic (ERH). In addition, two knowledge-based crossover operators are designed for the encoding method to expand the search space. Finally, a series of problem-applicable instances are generated and the effectiveness of the algorithm is proved by statistical analysis through comparison with several well-known algorithms.
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Wang, J., Li, J.q. (2023). An Improved Genetic Algorithm for Vehicle Routing Problem with Time Windows Considering Temporal-Spatial Distance. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14086. Springer, Singapore. https://doi.org/10.1007/978-981-99-4755-3_35
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DOI: https://doi.org/10.1007/978-981-99-4755-3_35
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