Integrated scheduling of drone-based medical sample delivery and testing

Qiuchen Gu; Jingyi Chen; Bin Hu; Qi Zheng; Tijun Fan; Qiuchen Gu; Jingyi Chen; Bin Hu; Qi Zheng; Tijun Fan

doi:10.3934/jimo.2026106

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 6: 2878-2911. doi: 10.3934/jimo.2026106

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Research article Special Issues

Integrated scheduling of drone-based medical sample delivery and testing

1.
School of Management, Shanghai University of Engineering Science, Shanghai, China
2.
School of Business, Minjiang University, Fuzhou, China
3.
School of Business, East China University of Science and Technology, Shanghai, China

Received: 14 January 2026 Revised: 30 April 2026 Accepted: 11 May 2026 Published: 25 May 2026
90B06, 90B35, 90C27

The timeliness of medical sample delivery and testing is critical, particularly within hub-and-spoke networks. Because of their high speed and dispatch flexibility, drones can reduce the total testing completion time (TTCT), defined here as the system-wide makespan, when delivery is coordinated jointly with downstream testing. In this study, the integrated scheduling problem of drone-based medical sample delivery and testing was formulated to minimize the TTCT by synchronizing multi-trip drone scheduling with continuous upstream sample collection and downstream deterministic first-in-first-out (FIFO) queueing at testing institutions. A mixed integer linear programming (MILP) model was developed in standard makespan form to capture the coupling between spatial decisions, including the assignment of the collection point and the testing institution, and temporal decisions, including the dispatch timing, trip frequency, and effective batch size, for an uncertain number of drone trips. A three-stage heuristic framework consisting of the initialized spatial decision, restricted temporal decision, and refined spatial decision was then proposed. The experimental results showed that the heuristic returns solutions within 1.23 s, on average, and reduces the TTCT by 37.60%, on average, relative to a spatial-only comparator.
- drone delivery,
- medical sample delivery,
- integrated scheduling,
- multi-trip scheduling
Citation: Qiuchen Gu, Jingyi Chen, Bin Hu, Qi Zheng, Tijun Fan. Integrated scheduling of drone-based medical sample delivery and testing[J]. Journal of Industrial and Management Optimization, 2026, 22(6): 2878-2911. doi: 10.3934/jimo.2026106

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Abstract

The timeliness of medical sample delivery and testing is critical, particularly within hub-and-spoke networks. Because of their high speed and dispatch flexibility, drones can reduce the total testing completion time (TTCT), defined here as the system-wide makespan, when delivery is coordinated jointly with downstream testing. In this study, the integrated scheduling problem of drone-based medical sample delivery and testing was formulated to minimize the TTCT by synchronizing multi-trip drone scheduling with continuous upstream sample collection and downstream deterministic first-in-first-out (FIFO) queueing at testing institutions. A mixed integer linear programming (MILP) model was developed in standard makespan form to capture the coupling between spatial decisions, including the assignment of the collection point and the testing institution, and temporal decisions, including the dispatch timing, trip frequency, and effective batch size, for an uncertain number of drone trips. A three-stage heuristic framework consisting of the initialized spatial decision, restricted temporal decision, and refined spatial decision was then proposed. The experimental results showed that the heuristic returns solutions within 1.23 s, on average, and reduces the TTCT by 37.60%, on average, relative to a spatial-only comparator.

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