Abstract
Based on the description of the vehicle routing problem, an improved tabu algorithm is proposed. In the solution process, a double-layer operation is used to change the neighborhood structure; a dynamic tabu table is constructed, so that when the tabu object enters the tabu table, it is based on where The tabu length varies during the search phase; set some variable parameters, verify the effect of the parameters on the solution through simulation, and control the degree of convergence of the parameters by the parameters. Through the above improvements, the stability of the solution and the global search ability are improved.
Keywords
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Dantzig, G., Ramser, J.: The truck dispatching problem. Manag. Sci. 6, 80–91 (1959)
Chao, I.M.: A tabu search method for the truck and trailer routing problem. J. Comput. Oper. Res. 29(1), 33–51 (2002)
Scheuerer, S.: A tabu search heuristic for the truck and trailer routing problem. Comput. Oper. Res. 33(4), 894–909 (2006)
Brandao, J.: A tabu search algorithm for the open vehicle routing problem. Eur. J. Oper. 157(3), 552–564 (2004)
Li, J., Binglei, X., Guo, Y.: Genetic algorithm for vehicle scheduling problem with non-full load. Syst. Eng. Theory Method. Appl. 93, 235–239 (2000)
Zhao, Y., Wu, B., Jiang, L., et al.: Double populations genetic algorithm for vehicle routing problem. Comput. Integr. Manuf. Syst. 103, 303–306 (2004). (in Chinese)
Xiao, P., Li, M., Zhang, J.: Partheno-genetic algorithmfor vehicle routing problem. Comput. Technol. Autom. 19(1), 26–30 (2000). (in Chinese)
Zhang, L., Chai, Y.: Improved genetic algorithm for vehicle routing problem. Syst. Eng. Theory Pract. 22(8), 79–84 (2002). (in Chinese)
Luo, X., Shi, H.-B.: Improved particle swarm optimization for vehicle routing problem with non-full load. J. East China Univ. Sci. Technol. Nat. Sci. Ed. 32(7), 767 (2006)
Baker, B.M., Ayechew, M.A.: A genetic algorithm for the vehicle routing problem. Comput. Oper. Res. 30(5), 787–8001 (2003)
Jiang, D., Yang, S., Du, W.: A study on the genetic algorithm for vehicle routing problem. Syst. Eng. Theory Pract. 196, 44–45 (1999). in Chinese
Xian-sheng, L., Hua, Z., Fei, L., Naixiu, G., Lu, Y.: City delivery vehicle dispatching model and its algorithm. J. Jilin Univ. (Eng. Technol. Ed.) 36(4), 618–621 (2006)
Zhang, X.-N., Fan, H.-M.: Hybrid scatter search algorithm for capacitated vehicle routing problem. Control Decis. 13, 1937–1944 (2015)
Pang, Y., Luo, H., Xing, L., Ren, T.: A survey of vehicle routing optimization problems and solution methods. Control Theory Appl. 36(10), 1574–1582 (2019)
Zhang, C., Zhao, Y., Zhang, J., et al.: Location and routing problem with minimizing carbon. Comput. Integr. Manuf. Syst. 23(12), 2768–2777 (2017)
Chen, Y., Shan, M., Wang, Q.: Research on heterogeneous fixed fleet vehicle routing problem with pick-up and delivering. J. Cent. S. Univ. (Sci. Technol.) 46(5), 1938–1945 (2015)
Sun, L., Ge, C., Huang, X., Wu, Y., Gao, Y.: Differentially private real-time streaming data publication based on sliding window under exponential decay. Comput. Mater. Continua 58(1), 61–78 (2019)
Jiang, W., et al.: A new time-aware collaborative filtering intelligent recommendation system. Comput. Mater. Continua 61(2), 849–859 (2019)
Liu, Y., Yang, Z., Yan, X., Liu, G., Hu, B.: A novel multi-hop algorithm for wireless network with unevenly distributed nodes. Comput. Mater. Continua 58(1), 79–100 (2019)
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Ge, J., Liu, X. (2020). Research on Vehicle Routing Problem Based on Tabu Search Algorithm. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_44
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DOI: https://doi.org/10.1007/978-3-030-57884-8_44
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