Abstract
Krill Herd algorithm is a powerful and relatively new Swarm Intelligence Algorithm that has been applied in a number of different kind of optimization problems since the time that it was published. In recent years there is a growing number of optimization models that are trying to reduce the energy consumption in routing problems. In this paper, a new variant of Krill Herd algorithm, the Parallel Multi-Start Non-dominated Sorting Krill Herd algorithm (PMS-KH), is proposed for the solution of a Vehicle Routing Problem variant, the Multiobjective Energy Reduction Multi-Depot Vehicle Routing Problem (MERMDVRP). Four different models are proposed where the distances between the customers and between the customers and the depots are either symmetric or asymmetric and the customers have either demand or pickup. The algorithm is compared with four other multiobjective algorithms, the Parallel Multi-Start Non-dominated Sorting Artificial Bee Colony (PMS-ABC), the Parallel Multi-Start Non-dominated Sorting Differential Evolution (PMS-NSDE), the Parallel Multi-Start Non-dominated Sorting Particle Swarm Optimization (PMS-NSPSO) and the Parallel Multi-Start Non-dominated Sorting Genetic Algorithm II (PMS-NSGA II) in a number of benchmark instances, giving very satisfactory results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Demir, E., Bektaş, T., Laporte, G.: A review of recent research on green road freight transportation. Eur. J. Oper. Res. 237(3), 775–793 (2014)
Gandomi, A.H., Alavi, A.H.: Krill Herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)
Li, J., Wang, R., Li, T., Lu, Z., Pardalos, P.: Benefit analysis of shared depot resources for multi-depot vehicle routing problem with fuel consumption. Transp. Res. Part D: Transp. Environ. 59, 417–432 (2018)
Lin, C., Choy, K.L., Ho, G.T.S., Chung, S.H., Lam, H.Y.: Survey of green vehicle routing problem: past and future trends. Expert Syst. Appl. 41(4), 1118–1138 (2014)
Marti, R., Pardalos, P.M., Resende, M.G.: Handbook of Heuristics. Springer (2018). ISBN 978-3-319-07123-7
Montoya-Torres, J.R., Franco, J.L., Isaza, S.N., Jimenez, H.F., Herazo-Padilla, N.: A literature review on the vehicle routing problem with multiple depots. Comput. Ind. Eng. 79, 115–129 (2015)
Psychas, I.-D., Marinaki, M., Marinakis, Y.: A parallel multi-start NSGA II algorithm for multiobjective energy reduction vehicle routing problem. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9018, pp. 336–350. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15934-8_23
Psychas, I.D., Marinaki, M., Marinakis, Y., Migdalas, A.: Non-dominated sorting differential evolution algorithm for the minimization of route based fuel consumption multiobjective vehicle routing problems. Energy Syst. 8, 785–814 (2016)
Psychas, I.-D., Marinaki, M., Marinakis, Y., Migdalas, A.: Minimizing the fuel consumption of a multiobjective vehicle routing problem using the parallel multi-start NSGA II algorithm. In: Kalyagin, V.A., Koldanov, P.A., Pardalos, P.M. (eds.) Models, Algorithms and Technologies for Network Analysis. SPMS, vol. 156, pp. 69–88. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29608-1_5
Psychas, I.-D., Marinaki, M., Marinakis, Y., Migdalas, A.: Parallel multi-start non-dominated sorting particle swarm optimization algorithms for the minimization of the route-based fuel consumption of multiobjective vehicle routing problems. In: Butenko, S., Pardalos, P.M., Shylo, V. (eds.) Optimization Methods and Applications. SOIA, vol. 130, pp. 425–456. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68640-0_20
Rapanaki, E., Psychas, I.D., Marinaki, M., Marinakis, Y., Migdalas, A.: A clonal selection algorithm for multiobjective energy reduction multi-depot vehicle routing problem. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds.) Machine Learning, Optimization, and Data Science LOD 2018. LNCS, vol. 11331, pp. 381–393. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13709-0_32
Rapanaki, E., Psychas, I.-D., Marinaki, M., Marinakis, Y.: An artificial bee colony algorithm for the multiobjective energy reduction multi-depot vehicle routing problem. In: Matsatsinis, N.F., Marinakis, Y., Pardalos, P. (eds.) LION 2019. LNCS, vol. 11968, pp. 208–223. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38629-0_17
Toth, P., Vigo, D.: Vehicle Routing: Problems, Methods and Applications, 2nd edn. MOS-SIAM Series on Optimization, SIAM, Philadelphia (2014)
Wang, G.-G., Guo, L., Gandomi, A.H., Hao, G.-S., Wang, H.: Chaotic Krill Herd algorithm. Inf. Sci. 274, 17–34 (2014)
Wang, G.-G., Gandomi, A.H., Alavi, A.H.: Stud krill herd algorithm. Neurocomputing 128, 363–370 (2014)
Wang, G.-G., Gandomi, A.H., Alavi, A.H., Gong, D.: A comprehensive review of krill herd algorithm: variants, hybrids and applications. Artif. Intell. Rev. 51(1), 119–148 (2017). https://doi.org/10.1007/s10462-017-9559-1
Xiao, Y., Zhao, Q., Kaku, I., Xu, Y.: Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Comput. Oper. Res. 39(7), 1419–1431 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Rapanaki, E., Psychas, I..D., Marinaki, M., Matsatsinis, N., Marinakis, Y. (2020). A Krill Herd Algorithm for the Multiobjective Energy Reduction Multi-Depot Vehicle Routing Problem. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-64583-0_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64582-3
Online ISBN: 978-3-030-64583-0
eBook Packages: Computer ScienceComputer Science (R0)