Stas crossover with K-mean clustering for vehicle routing problem with time window
| Autor: | Ratchadakorn Poohoi, Kanate Puntusavase, Shunichi Ohmori |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Decision Science Letters, Vol 13, Iss 3, Pp 525-534 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1929-5804 1929-5812 |
| DOI: | 10.5267/j.dsl.2024.5.008 |
| Popis: | Vehicle Routing Problem (VRP) is important in the transportation and logistics industries. Vehicle Routing Problem with Time Window (VRPTW) is a kind of VRP with the additional time windows constraint in the model and is classified as an NP-hard problem. In this study, we proposed Stas crossover in Genetic Algorithm (GA) to solve VRPTW by developing the problem with K-mean clustering. The experiments use the standard Solomon’s benchmark problem instances for VRPTW. The results with K-mean clustering are shown to perform better for minimum distance and average distance than without K-mean clustering. In the case of location and dispersion characteristics of the customer, the paths with K-mean clustering are arranged into groups and are orderly, but the paths without K-mean clustering are disordered. After that, this paper shows the comparison of the crossover operator performance on instances of Solomon benchmark, and appropriate crossover operators are recommended for each type of problem. The results of the proposed algorithm are better than the best-known solutions from the previous studies for some instances. Moreover, our proposed research will serve as a guideline for a real-world case study. |
| Databáze: | Directory of Open Access Journals |
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