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3222. Multi-modal routing and scheduling of drones, cargo bikes and vans in the presence of weather uncertainties for pathology collections in the Solent Area
Invited abstract in session MD-10: Healthcare logistics and routing, stream OR in Health Services (ORAHS).
Monday, 14:30-16:00Room: 11 (building: 116)
Authors (first author is the speaker)
| 1. | Asrul Harun Ismail
|
| Centre of Operational Research and Logistics, University of Portsmouth | |
| 2. | Djamila Ouelhadj
|
| Maths, University of Portsmouth | |
| 3. | Xiang Song
|
| School of Mathematics, University of Portsmouth | |
| 4. | Andy Oakey
|
| Transportation Research Group, University of Southampton | |
| 5. | Antonio Martinez Sykora
|
| Management School, University of Southampton | |
| 6. | Tom Cherrett
|
| University of Southampton |
Abstract
This research explores the impact of weather uncertainties on the ability of drones to improve pathology collections over the business-as-usual road-based logistics system in the Solent area of the UK. Weather uncertainties impact on the manoeuvrability and speed of drones, which can lead to delays and incidents. In this paper, we present a novel framework that combines a machine learning drone flight duration prediction model with a meta-heuristic Bees optimisation algorithm to solve the drone-cargo bike-van multimodal medical logistics problem in the presence of variable weather conditions.
Experiments have been conducted to evaluate how varying wind conditions affect drone schedule efficiency when used in combination with other transportation modes (cargo bikes and vans), and quantify the logistics system's overall economic and environmental impacts as a result. The case studies used concern the routine collection of pathology samples from doctors surgeries across the Southampton and Portsmouth area of the UK for delivery to pathology laboratories.
This study is part of the Solent Future Transport Zone project (FTZ) funded by the UK’s Department for Transport and led by Solent Transport.
Keywords
- Health Care
- Logistics
Status: accepted
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