Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows

Curtois, Timothy; Landa-Silva, Dario; Qu, Yi; Laesanklang, Wasakorn

Abstract

© 2018, The Author(s). An effective and fast hybrid metaheuristic is proposed for solving the pickup and delivery problem with time windows. The proposed approach combines local search, large neighbourhood search and guided ejection search in a novel way to exploit the benefits of each method. The local search component uses a novel neighbourhood operator. A streamlined implementation of large neighbourhood search is used to achieve an effective balance between intensification and diversification. The adaptive ejection chain component perturbs the solution and uses increased or decreased computation time according to the progress of the search. While the local search and large neighbourhood search focus on minimising travel distance, the adaptive ejection chain seeks to reduce the number of routes. The proposed algorithm design results in an effective and fast solution method that finds a large number of new best-known solutions on a well-known benchmark dataset. Experiments are also performed to analyse the benefits of the components and heuristics and their combined use to achieve a better understanding of how to better tackle the subject problem.

Citation

Curtois, T., Landa-Silva, D., Qu, Y., & Laesanklang, W. (2018). Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows. Euro Journal of Transportation and Logistics, 7(2), 151-192. https://doi.org/10.1007/s13676-017-0115-6

• Journal Article Type: Article
• Acceptance Date: Dec 26, 2017
• Online Publication Date: Jan 12, 2018
• Publication Date: 2018-06
• Deposit Date: Jan 9, 2018
• Publicly Available Date: Jan 12, 2018
• Journal: EURO Journal on Transportation and Logistics
• Electronic ISSN: 2192-4376
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 2
• Pages: 151-192
• DOI: https://doi.org/10.1007/s13676-017-0115-6
• Public URL: https://nottingham-repository.worktribe.com/output/943894
• Publisher URL: https://link.springer.com/article/10.1007/s13676-017-0115-6
• Contract Date: Jan 9, 2018