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Research Article

Chemical reaction inspired approach for routing problems with hard time constraints

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Received 05 Jan 2023, Accepted 17 May 2024, Published online: 31 May 2024

References

  • Baldacci, R., Mingozzi, A., & Roberti, R. (2011). New route relaxation and pricing strategies for the vehicle routing problem. Operations Research, 59(5), 1269–1283. https://doi.org/10.1287/opre.1110.0975
  • Baldacci, R., Mingozzi, A., & Roberti, R. (2012). Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints. European Journal of Operational Research, 218(1), 1–6. https://doi.org/10.1016/j.ejor.2011.07.037
  • Boudali, I., & Ragmoun, M. (2022). A memetic approach for routing problem with capacity and time constraints. In C. Bădică, J. Treur, D. Benslimane, B. Hnatkowska, & M. Krótkiewicz (Eds.), Advances in computational collective intelligence, 14th International Conference, ICCCI 2022, Proceedings (pp. 612–626). http://doi.org/10.1007/978-3-031-16210-7_50
  • Cao, C., Zhang, X., & Guo, Z. (2020). Vehicle routing problem with time windows arising in urban delivery. Journal of Physics: Conference Series, 1626(1), 012097. https://doi.org/10.1088/1742-6596/1626/1/012097
  • Deng, Y., Zhu, W. H., Li, H. W., & Zheng, Y. H. (2018). Multi-type ant system algorithm for the time dependent vehicle routing problem with time windows. Journal of Systems Engineering and Electronics, 29(3), 625–638.
  • Desaulniers, G., Lessard, F., & Hadjar, A. (2008). Tabu search, generalized k-path inequalities, and partial elementarity for the vehicle routing problem with time windows. Transportation Science, 42(3), 387–404. https://doi.org/10.1287/trsc.1070.0223
  • Desaulniers, G., Madsen, O.-B. G., & Ropke, S. (2014). The vehicle routing problems with time windows. In P. Toth & D. Vigo (Eds.), Vehicle routing: Problems, methods, and applications (2nd ed., Chapter 5, pp. 119–159). MOS-SIAM Series on Optimization.
  • Dixit, A., Mishra, A., & Shukla, A. (2019). Vehicle routing problem with time windows using meta-heuristic algorithms: A survey. In N. Yadav, A. Yadav, J. Bansal, K. Deep, & J. Kim (Eds.), Harmony search and nature inspired optimization algorithms. Advances in intelligent systems and computing (Vol. 741, pp. 539–546). Springer. https://doi.org/10.1007/978-981-13-0761-4_52
  • Dornemann, J. (2023). Solving the capacitated vehicle routing problem with time windows via graph convolutional network assisted tree search and quantum-inspired computing. Frontiers in Applied Mathematics and Statistics, 9, 1155356. https://doi.org/10.3389/fams.2023.1155356
  • Fan, W. B., & Feng, W. (2018). Optimization of vehicle routing problem with time window cigarette logistics based on hybrid genetic algorithm. Journal of Modern Electronic Technology, 11, 119–123.
  • Ge, B., Han, J. H., Wei, W., Cheng, L., & Han, Y. (2015). Dynamic hybrid ant colony optimization algorithm for solving vehicle routing problem with time windows. Journal of Pattern Recognition and Artificial Intelligence, 28(7), 641–650.
  • Han, M., & Wang, Y. (2018). A survey for vehicle routing problems and its derivatives. IOP Conference Series: Materials Science and Engineering, 452(4), 042024. https://doi.org/10.1088/1757-899X/452/4/042024
  • Irnich, S., Toth, P., & Vigo, D. (2014). The family of vehicle routing problems. In P. Toth & D. Vigo (Eds.), Vehicle routing: Problems, methods, and applications (2nd ed., Chapter 1, pp. 1–33). MOS-SIAM Series on Optimization.
  • Jepsen, M., Petersen, B., Spoorendonk, S., & Pisinger, D. (2008). Subset-row inequalities applied to the vehicle routing problem with time windows. Operations Research, 56(2), 497–511. https://doi.org/10.1287/opre.1070.0449
  • Kallehauge, B. (2008). Formulations and exact algorithms for the vehicle routing problem with time windows. Computers & Operations Research, 35(7), 2307–2330. https://doi.org/10.1016/j.cor.2006.11.006
  • Kirci, P. (2016). An optimization algorithm for a capacitated vehicle routing problem with time windows. Sādhanā, 41(5), 519–529. https://doi.org/10.1007/s12046-016-0488-5
  • Lam, A. Y. S., Li, V. O. K., & Xu, J. (2013). On the convergence of chemical-reaction optimization for combinatorial optimization. IEEE Transactions on Evolutionary Computation, 17(5), 605–620. https://doi.org/10.1109/TEVC.2012.2227973
  • Lam, A. Y. S., & Li, V. O. K. (2010). Chemical-reaction-inspired metaheuristic for optimization. IEEE Transactions on Evolutionary Computation, 14(3), 381–399. https://doi.org/10.1109/TEVC.2009.2033580
  • Lam, A. Y. S., & Li, V. O. K. (2012). Chemical reaction optimization: A tutorial. Memetic Computing, 4(1), 3–17. https://doi.org/10.1007/s12293-012-0075-1
  • Lam, A. Y. S., Li, V. O. K., & Yu, J. J. Q. (2012). Real-coded chemical reaction optimization. IEEE Transactions on Evolutionary Computation, 16(3), 339–353. https://doi.org/10.1109/TEVC.2011.2161091
  • Laporte, G., Ropke, S., & Vidal, T. (2014). Heuristics for the vehicle routing problems. In P. Toth & D. Vigo (Eds.), Vehicle routing: Problems, methods, and applications (2nd ed., Chapter 4, pp. 87–116). MOS-SIAM Series on Optimization.
  • Osaba, E., Carballedo, R., Yang, X. S., Fister Jr., I., Lopez-Garcia, P., & Del Ser, J. (2017). On efficiently solving the vehicle routing problem with time windows using the bat algorithm with random reinsertion operators. In X. S. Yang (Ed.), Nature-inspired algorithms and applied optimization. Studies in computational intelligence (Vol. 744, pp. 69–89). Springer. https://doi.org/10.1007/978-3-319-67669-2_4
  • Poggi, M., & Uchoa, E. (2014). New exact algorithms for the capacitated vehicle routing problem. In P. Toth & D. Vigo (Eds.), Vehicle routing: Problems, methods, and applications, vehicle routing: Problems, methods, and applications (2nd ed., Chapter 3, pp. 59–86). MOS-SIAM Series on Optimization.
  • Ramesh, D., Rizvi, N., Rao, P. C. S., Sundararajan, E. A., Mondal, K., Srivastava, G., & Qi, L. (2024). Improved chemical reaction optimization with fitness-based quasi-reflection method for scheduling in hybrid cloud-fog environment. IEEE Transactions on Network and Service Management, 21(1), 653–669. https://doi.org/10.1109/TNSM.2023.3299358
  • Resende, M. G. C., & Ribeiro, C. C. (2008). Greedy randomized adaptive search procedures: Advances and applications. In M. Gendreau & J. Y. Potvin (Eds.), Handbook of metaheuristics (2nd ed., Chapter 10, pp. 283–318). International Series in Operations Research & Management Science 146. Springer. https://doi.org/10.1007/978-1-4419-1665-5_10
  • Saksuriya, P., & Likasiri, C. (2022). Hybrid heuristic for vehicle routing problem with time windows and compatibility constraints in home healthcare system. Applied Sciences, 12(13), 6486. https://doi.org/10.3390/app12136486
  • Semet, F., Toth, P., & Vigo, D. (2014). Classical exact algorithms for the capacitated vehicle routing problem. In P. Toth & D. Vigo (Eds.), Vehicle routing: Problems, methods, and applications (2nd ed., Chapter 2, pp. 37–57). MOS-SIAM Series on Optimization.
  • Siddique, N., & Adeli, H. (2017). Nature-Inspired chemical reaction optimisation algorithms. Cognitive Computation, 9(4), 411–422. https://doi.org/10.1007/s12559-017-9485-1
  • Solomon, M. M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35(2), 254–265. https://doi.org/10.1287/opre.35.2.254
  • Swan, J., Adriaensen, S., Brownlee, A. E.-I., Hammond, K., Johnson, C. G., Kheiri, A., Krawiec, F., Merelo, J.J., Minku, L. L., Özcan Ender, P., G. L., García-Sánchez, P., Sörensen, K., Voß, S., Wagner, M., & White, D. R. (2022). Metaheuristics ‘in the large’. European Journal of Operational Research, 297(2), 393–406. https://doi.org/10.1016/j.ejor.2021.05.042
  • Toth, P., & Vigo, D. (2014). Vehicle routing: Problems, methods and applications (2nd ed.). MOS-SIAM Series on Optimization, SIAM.
  • Xie, Y., Hu, R., Qian, B., Chen, S. F., Zhang, G. l., & Zhang, X. D. (2016). An improved population incremental learning algorithm for solving vehicle routing problem with soft time windows. Journal of Nanjing University of Science and Technology (Natural Science), 1, 110–116.
  • Xu, J., Lam, A. Y. S., & Li, V. O. K. (2011). Chemical reaction optimization for task scheduling in grid computing. IEEE Transactions on Parallel and Distributed Systems, 22(10), 1624–1631. https://doi.org/10.1109/TPDS.2011.35
  • Yao, B., Yan, Q., Zhang, M., & Yang, Y. (2017). Improved artificial bee colony algorithm for vehicle routing problem with time windows. PLoS ONE, 12(9), e0181275.
  • Zain, A. M., & Yousif, A. (2020). Chemical reaction optimization (CRO) for cloud job scheduling. SN Applied Sciences, 2(1), 53. https://doi.org/10.1007/s42452-019-1758-8