|
1. 李美儀、韓復華 (2015), 車輛路線相關問題之回顧與國內發展之分析,國 立交通大學運輸與物流管理學系,碩士論文。 2. 郭佳林、卓訓榮、林貴璽 (2004),求解具時間窗之多趟次車輛途程問題,國 立交通大學運輸與物流管理學系,碩士論文。 3. Babaee Tirkolaee, E., Abbasian, P., Soltani, M., & Ghaffarian, S. A. (2019). Developing an applied algorithm for multi-trip vehicle routing problem with time windows in urban waste collection: A case study. Waste Management & Research, 37(1_suppl), 4-13. 4. Ball, M. O., Golden, B. L., Assad, A. A., & Bodin, L. D. (1983). Planning for truck fleet size in the presence of a common‐carrier option. Decision Sciences, 14(1), 103-120. 5. Battarra, M., Monaci, M., & Vigo, D. (2009). An adaptive guidance approach for the heuristic solution of a minimum multiple trip vehicle routing problem. Computers & Operations Research, 36(11), 3041-3050. 6. Brandao, J., & Mercer, A. (1997). A tabu search algorithm for the multi-trip vehicle routing and scheduling problem. European journal of operational research, 100(1), 180-191. 7. Brown, G. G., & Graves, G. W. (1981). Real-time dispatch of petroleum tank trucks. Management science, 27(1), 19-32. 8. Browne, M., Allen, J., & Leonardi, J. (2011). Evaluating the use of an urban consolidation centre and electric vehicles in central London. IATSS research, 35(1), 1-6. 9. Caruana, R. A., Eshelman, L. J., & Schaffer, J. D. (1989, August). Representation and hidden bias II: Eliminating defining length bias in genetic search via shuffle crossover. In Proceedings of the 11th international joint conference on Artificial intelligence-Volume 1 (pp. 750-755). 10. Chen, J., & Shi, J. (2019). A multi-compartment vehicle routing problem with time windows for urban distribution–A comparison study on particle swarm optimization algorithms. Computers & Industrial Engineering, 133, 95-106. 11. Chen, J., Dan, B., & Shi, J. (2020). A variable neighborhood search approach for the multi-compartment vehicle routing problem with time windows considering carbon emission. Journal of Cleaner Production, 277, 123932. carbon emission. Journal of Cleaner Production, 277, 123932. 12. Christofides, N. (1976). Worst-case analysis of a new heuristic for the travelling salesman problem. Carnegie-Mellon Univ Pittsburgh Pa Management Sciences Research Group. 13. Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management science, 6(1), 80-91. 14. Desrochers, M., & Soumis, F. (1988). A generalized permanent labelling algorithm for the shortest path problem with time windows. INFOR: Information Systems and Operational Research, 26(3), 191-212. 15. Dumitrescu, D., Lazzerini, B., Jain, L. C., & Dumitrescu, A. (2000). Evolutionary computation. CRC press. 16. Eshtehadi, R., Demir, E., & Huang, Y. (2020). Solving the vehicle routing problem with multi-compartment vehicles for city logistics. Computers & Operations Research, 115, 104859. 17. Fermín Cueto, P., Gjeroska, I., Solà Vilalta, A., & Anjos, M. F. (2021). A solution approach for multi‐trip vehicle routing problems with time windows, fleet sizing, and depot location. Networks. 18. Fleischmann, B. (1990). The vehicle routing problem with multiple use of vehicles. Fachbereich Wirtschaftswissenschaften, Universität Hamburg. 19. Ghannadpour, S. F., & Zandiyeh, F. (2020). An adapted multi-objective genetic algorithm for solving the cash in transit vehicle routing problem with vulnerability estimation for risk quantification. Engineering applications of artificial intelligence, 96, 103964. 20. Goldberg, D. E., Korb, B., & Deb, K. (1989). Messy genetic algorithms: Motivation, analysis, and first results. Complex systems, 3(5), 493-530. 21. Holland, J. H. (1984). Genetic algorithms and adaptation. In Adaptive control of ill-defined systems (pp. 317-333). Springer, Boston, MA. 22. Jakobović, D., & Golub, M. (1999). Adaptive genetic algorithm. Journal of computing and information technology, 7(3), 229-235. 23. Jebari, K., & Madiafi, M. (2013). Selection methods for genetic algorithms. International Journal of Emerging Sciences, 3(4), 333-344. 24. Khoukhi, S., El Yaakoubi, O., Bojji, C., & Bensouda, Y. (2019, January). A genetic algorithm for solving a multi-trip vehicle routing problem with time windows and simultaneous pick-up and delivery in a hospital complex. In Proceedings of the 3rd International Conference On Machine Learning and Soft Computing (pp. 76- 80). 25. Li, J., Wang, F., & He, Y. (2020). Electric vehicle routing problem with battery swapping considering energy consumption and carbon emissions. Sustainability, 12(24), 10537. 26. Li, X., Tian, P., & Leung, S. C. (2010). Vehicle routing problems with time windows and stochastic travel and service times: Models and algorithm. International Journal of Production Economics, 125(1), 137-145. 27. Mahjoob, M., Fazeli, S. S., Milanlouei, S., Tavassoli, L. S., & Mirmozaffari, M. (2022). A modified adaptive genetic algorithm for multi-product multi-period inventory routing problem. Sustainable Operations and Computers, 3, 1-9. 28. Martins, S., Ostermeier, M., Amorim, P., Hübner, A., & Almada-Lobo, B. (2019). Product-oriented time window assignment for a multi-compartment vehicle routing problem. European Journal of Operational Research, 276(3), 893-909. 29. Melechovský, J. (2013). A variable neighborhood search for the selective multicompartment vehicle routing problem with time windows. Lecture notes in management science, 5, 159-166. 30. Mirjalili, S. (2019). Genetic algorithm. In Evolutionary. algorithms and neural networks (pp. 43-55). Springer, Cham. 31. Ostermeier, M., Henke, T., Hübner, A., & Wäscher, G. (2021). Multi-compartment vehicle routing problems: State-of-the-art, modeling framework and future directions. European Journal of Operational Research, 292(3), 799-817. 32. Pan, B., Zhang, Z., & Lim, A. (2021). Multi-trip time-dependent vehicle routing problem with time windows. European Journal of Operational Research, 291(1), 218-231. 33. Rabbouch, B., Saâdaoui, F., & Mraihi, R. (2019, September). Constraint programming based algorithm for solving large-scale vehicle routing problems. In International Conference on Hybrid Artificial Intelligence Systems (pp. 526- 539). Springer, Cham. 34. Sastry, K., Goldberg, D., & Kendall, G. (2005). Genetic algorithms. In Search methodologies (pp. 97-125). Springer, Boston, MA. 35. Solomon, M. M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations research, 35(2), 254-265. 36. Toth, P., & Vigo, D. (Eds.). (2002). The vehicle routing problem. Society for Industrial and Applied Mathematics. 37. Wu, Q. H., Cao, Y. J., & Wen, J. Y. (1998). Optimal reactive power dispatch using an adaptive genetic algorithm. International Journal of Electrical Power & Energy Systems, 20(8), 563-569. 38. Xu, Z., Elomri, A., Pokharel, S., & Mutlu, F. (2019). A model for capacitated green vehicle routing problem with the time-varying vehicle speed and soft time windows. Computers & Industrial Engineering, 137, 106011. 39. Zhang, J. (2020, November). An improved genetic algorithm for vehicle routing problem. In International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (pp. 163-169). Springer, Cham. 40. Zhen, L., Ma, C., Wang, K., Xiao, L., & Zhang, W. (2020). Multi-depot multi-trip vehicle routing problem with time windows and release dates. Transportation Research Part E: Logistics and Transportation Review, 135, 101866.
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