Abstract
Vehicle Routing Problem (VRP) is widely studied under the real logistics environments. For the reason that customers’ demands could appear dynamically and need to be served within fuzzy time windows, the dynamic vehicle routing problem with soft time windows (DVRPSTW) is studied in this paper. We use the improved large neighborhood search algorithm(iLNS) and the hybrid Particle Swarm Optimization(hPSO) to solve the problem. The performance of both algorithms comparing with benchmarks shows that our methods can solve DVRPSTW efficiently with more customers.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ramser, D.G.B.: Greedy randomized adaptive search procedures. J. Glob. Optim. 6, 109–133 (1995)
Larsen, A., Madsen, O., Solomon, M.: Partially dynamic vehicle routing-models and algorithms. J. Oper. Res. Soc. 53, 637–646 (2002)
Hashimoto, H., Ibaraki, T., Imahori, S., Yagiura, M.: The vehicle routing problem with flexible time windows and traveling times. Discrete Appl. Math. 154, 2271–2290 (2006)
Li, J., Mirchandani, P., Borenstein, D.: Real-time vehicle rerouting problems with timewindows. Eur. J. Oper. Res. 194, 711–727 (2009)
Hong, L.: An improved LNS algorithm for real-time vehicle routing problem with time windows. Comput. Oper. Res. 39, 151–163 (2012)
Yang, D., et al.: A hybrid large neighborhood search for dynamic vehicle routing problem with time deadline. In: The 9th Annual International Conference on Combinatorial Optimization and Applications (2015)
Calvete, H., Galé, C., Sánchez-Valverde, B., Oliveros, M.: Vehicle routing problems with soft time windows: an optimization based approach. Monografias del Seminario Matematico Garcia de Galdeano 31, 295–304 (2004)
Figliozzi, M.: An iterative route construction and improvement algorithm for the vehicle routing problem with soft time windows. Transp. Res. Part C 18, 668–679 (2010)
Qureshi, A., Taniguchi, E., Yamada, T.: Exact solution for the vehicle routing problem with semi soft time windows and its application. Proc.Soc. Behav. Sci. 2, 5931–5943 (2010)
Fan, X., Li, N., Zhang, B., Liu, Z.: Research on vehicle routing problem with soft time windows based on tabu search algorithm. In: IEEE International Conference on Industrial Engineering and Engineering Management (2011)
Xu, J., Yan, F., Li, S.: Vehicle routing optimization with soft time windows in a fuzzy random environment. Transp. Res. Part E 47, 1075–1091 (2011)
Iqbal, S., Rahman, M.: Vehicle routing problems with soft time windows. In: 7th International Conference on Electrical and Computer Engineering (2012)
Marinakis, Y., Marinaki, M.: A hybrid genetic-particle swarm optimization algorithm for the vehicle routing problem. Expert Syst. Appl. 37, 1446–1455 (2010)
Khouadjia, M., Sarasola, B., Alba, E., Jourdan, L., Talbi, E.: A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests. Appl. Soft Comput. 12, 1426–1439 (2012)
Feo, T.: Greedy randomized adaptive search procedures. J. Glob. Optim. 6, 109–133 (1995)
Acknowledgment
This work was financially supported by National Natural Science Foundation of China with Grant No.11371004 and No. 61672195, Shenzhen Science and Technology Plan with Grant No. JCYJ20160318094336513 and No. JCYJ20160318094101317, and Shenzhen Overseas High Level Talent Innovation and Entrepreneurship Special Fund with Grant No. KQCX20150326141251370.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
He, X., Zeng, X., Song, L., Huang, H., Du, H. (2016). Solving Dynamic Vehicle Routing Problem with Soft Time Window by iLNS and hPSO. In: Chan, TH., Li, M., Wang, L. (eds) Combinatorial Optimization and Applications. COCOA 2016. Lecture Notes in Computer Science(), vol 10043. Springer, Cham. https://doi.org/10.1007/978-3-319-48749-6_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-48749-6_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48748-9
Online ISBN: 978-3-319-48749-6
eBook Packages: Computer ScienceComputer Science (R0)