Abstract
In this paper, the carbon emission factor is taken into account in the Location Routing Problem (LRP), and a multi-objective LRP model combining carbon emission with total cost is established. Due to the complexity of the proposed problem, a generality-oriented and emerging Multi-Objective Hyper Heuristic algorithm (MOHH) is proposed. In the framework of MOHH, the LRP related operates are constructed as the low level heuristics, and the different high level strategies are designed. Compared with the NSGA-II algorithm, the MOHH can better solve the multi-objective problem of LRP, and can quickly find the better solution, and achieve higher search efficiency and stability of the algorithm.
Y. Zhao—The research direction is the intelligent distribution and optimal dispatch of logistics system, modern design theory and method of digital products.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jemai, J., Zekri, M., Mellouli, K.: An NSGA-II algorithm for the green vehicle routing problem. In: Hao, J.-K., Middendorf, M. (eds.) EvoCOP 2012. LNCS, vol. 7245, pp. 37–48. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29124-1_4
Tunga, H., Bhaumik, A.: A method for solving bi-objective green vehicle routing problem (G-VRP) through genetic algorithm. In: International Conference on Frontiers in Optimization, pp. 1–19. Springer, Berlin (2017)
Ghaffarinasab, N., Jabalameli, M., Saboury, A.: Multi-objective capacitated location-routing problem: modelling and a simulated annealing heuristic. Int. J. Serv. Oper. Manag. 15(2), 140–156 (2013)
Golmohammadi, A., Bonab, S., Parishani, A.: A multi-objective location routing problem using imperialist competitive algorithm. Int. J. Ind. Eng. Comput. 7(3), 481–488 (2016)
Yanwei, Z., Wen, L.: Low carbon for a multi-vehicle routing problem with simultaneous pickups and deliveries. J. Zhejiang Univ. Technol. 43(1), 18–23 (2015)
Kheiri, A., Özcan, E.: An iterated multi-stage selection hyper-heuristic. Eur. J. Oper. Res. 250(1), 77–90 (2016)
Burke, K., Gendreau, M., Hyde, M.: Hyper-heuristics: a survey of the state of the art. J. Oper. Res. Soc. 64(12), 1695–1724 (2016)
Marshall, R.J., Johnston, M., Zhang, M.: Hyper-heuristic operator selection and acceptance criteria. In: Ochoa, G., Chicano, F. (eds.) EvoCOP 2015. LNCS, vol. 9026, pp. 99–113. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16468-7_9
Zamli, Z., Alkazemi, B., Kendall, G.: A Tabu search hyper-heuristic strategy for t-way, test suite generation. Appl. Soft Comput. 44(1), 57–74 (2016)
Mehrjerdi, Z., Nadizadeh, A.: Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands. Eur. J. Oper. Res. 229(1), 75–84 (2013)
Zcan, E., Bilgin, B., KorkmazE, E.: A comprehensive analysis of hyper-heuristics. Intell. Data Anal. 12(1), 3–23 (2018)
Xu, J., Huang, D.: Research on multi-objective vehicle routing based on hybrid particle swarm optimization. Comput. Integr. Manuf. Syst. 573–579+584 (2007)
Acknowledgement
This work supported by the National Natural Science Foundation, China (No. 61572438).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Qian, Z., Zhao, Y., Wang, S., Leng, L., Wang, W. (2018). A Hyper Heuristic Algorithm for Low Carbon Location Routing Problem. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-92537-0_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92536-3
Online ISBN: 978-3-319-92537-0
eBook Packages: Computer ScienceComputer Science (R0)