Abstract
Solving a pickup and delivery problem with, e. g., multiple depots, time windows, and heterogeneous vehicles is a challenging routing task. Due to the complexity, a meta-heuristic approach (e. g., a genetic algorithm) with sufficiently good solution quality is recommended. Genetic algorithms contain multiple operators such as the crossover and mutation operators that are called with certain probabilities. However, selecting appropriate probability values (parameters) for these operators strongly depend on the data structure of the given instances. For each new instance, the best parameter configuration must be found to enhance the overall solution quality. In this paper, an a-priori parameter selection approach based on classifying new instances to clusters is presented. Beforehand, a bayesian optimization approach with gaussian processes is used to find the best parameters for each cluster. The a-priori parameter selection is evaluated on four well-known pickup and delivery problem data sets, each with 60 instances and different number of depots.
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
Cooray, P., Rupasinghe, T.: Machine learning-based parameter tuned genetic algorithm for energy minimizing vehicle routing problem. J. Ind. Eng. 2017, 1–13, 100087 (2017). https://doi.org/10.1155/2017/3019523
Florian, A., Sörensen, K.: What makes a VRP solution good? The generation of problem-specific knowledge for heuristics. Comput Oper Res 106, 280–288 (2019)
Gutiérrez-Rodríguez, A.E., Conant-Pablos, S.E., Ortiz-Bayliss, J.C., Terashima-Marín, H.: Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning. Expert Syst. Appl. 118, 470–481 (2019)
Huang, C., Li, Y., Yao, X.: A survey of automatic parameter tuning methods for metaheuristics. IEEE Trans. Evol Comput. 24(2), 201–216 (2019)
Rüther, C., Rieck, J.: A grouping genetic algorithm for multi depot pickup and delivery problems with time windows and heterogeneous vehicle fleets. In: Paquete, L., Zarges, C. (eds.) EvoCOP 2020. LNCS, vol. 12102, pp. 148–163. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43680-3_10
Rüther, C., Rieck, J.: A bayesian optimization approach for tuning a genetic algorithm solving practical-oriented pickup and delivery problems, 1–11 (2022). https://www.uni-hildesheim.de/fb4/institute/bwl/betriebswirtschaft-und-operations-research/forschungprojekte/working-papers/
Rüther, C., Rieck, J.: Bundle selection approaches for collaborative practical-oriented Pickup and Delivery Problems. EURO J. Transp. Logist. 11, 100087 (2022). https://doi.org/10.1016/j.ejtl.2022.100087
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rüther, C., Chamurally, S., Rieck, J. (2022). An a-priori Parameter Selection Approach to Enhance the Performance of Genetic Algorithms Solving Pickup and Delivery Problems. In: Trautmann, N., Gnägi, M. (eds) Operations Research Proceedings 2021. OR 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-08623-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-08623-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08622-9
Online ISBN: 978-3-031-08623-6
eBook Packages: Business and ManagementBusiness and Management (R0)