A Bi-Level Approach to Vehicle Fleet Reduction: Successful Case Study in Community Healthcare

Brownlee, Alexander; Thomson, Sarah; Oladapo, Rachael

Abstract

We report on a case study application of metaheuristics with Argyll and Bute Health and Social Care Partnership in the West of Scotland. The Partnership maintains a fleet of pool vehicles that are available to service visits of staff to locations across a largely rural area. Maintaining such a fleet is important but costly: we show how the allocation of fleet vehicles can be formulated as a bilevel optimisation problem. At the upper level, vehicles are allocated to ‘base’ locations such as hospitals. At the lower level, vehicles are allocated to specific jobs. We explore local-search approaches to solving this problem. We show that some blurring of the distinction between upper and lower levels can be helpful for this problem. We also demonstrate, for our case study, a 7.1% reduction in the vehicle fleet while still being able to meet all demand.

Citation

Brownlee, A., Thomson, S., & Oladapo, R. (2024, July). A Bi-Level Approach to Vehicle Fleet Reduction: Successful Case Study in Community Healthcare. Presented at Proceedings of the Genetic and Evolutionary Computation Conference Companion, Melbourne, Australia

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Proceedings of the Genetic and Evolutionary Computation Conference Companion
• Start Date: Jul 14, 2024
• End Date: Jul 18, 2024
• Online Publication Date: Jul 14, 2024
• Publication Date: 2024
• Deposit Date: May 13, 2024
• Publicly Available Date: Jul 14, 2024
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Pages: 1695-1701
• Book Title: GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion
• ISBN: 9798400704949
• DOI: https://doi.org/10.1145/3638530.3664137
• Keywords: Optimal job scheduling, evolutionary computation, bilevel optimization
• Publisher URL: https://dl.acm.org/conference/gecco
• External URL: https://gecco-2024.sigevo.org/