Optimizing task assignment and routing operations with a heterogeneous fleet of unmanned aerial vehicles for emergency healthcare services

Lin, Ziru, Xu, Xiaofeng, Demir, Emrah ORCID: https://orcid.org/0000-0002-4726-2556 and Laporte, Gilbert 2025. Optimizing task assignment and routing operations with a heterogeneous fleet of unmanned aerial vehicles for emergency healthcare services. Computers and Operations Research 174 , 106890. 10.1016/j.cor.2024.106890

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Abstract

This paper studies the optimization of task assignment and pickup and delivery operations using a heterogeneous fleet of unmanned aerial vehicles (UAVs). We specifically address the distribution of emergency medical supplies, including medications, vaccines, and essential medical aid, as well as the collection of biological blood samples for testing and analysis. Unique challenges, such as supply shortages, time windows, and geographical considerations, are explicitly taken into account. The problem is first formulated as a mixed-integer linear programming model aimed at maximizing the total profit derived from the execution of a set of emergency healthcare pickup and delivery tasks. An enhanced Q-learning-based adaptive large neighborhood search (QALNS) is proposed for large-scale benchmark instances. QALNS exhibits a superior performance on benchmark instances. It also improves the quality of the solutions on average by 5.49% and 6.86% compared to the Gurobi solver and a state-of-the-art adaptive large neighborhood search algorithm, respectively. Sensitivity analyses are performed on critical factors contributing to the performance of the QALNS algorithm, such as the learning rate and the discount indicator. Finally, we provide managerial insights on the use of the fleet of UAVs and the design of the network.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Start Date: 2024-11-06
Publisher:	Elsevier
ISSN:	0305-0548
Date of First Compliant Deposit:	11 November 2024
Date of Acceptance:	28 October 2024
Last Modified:	04 Dec 2024 10:36
URI:	https://orca.cardiff.ac.uk/id/eprint/173805

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