Online optimisation for ambulance routing in disaster response with partial or no information on victim conditions

Shiri, Davood; Akbari, Vahid; Tozan, Hakan

Abstract

In response to mass casualty incidents, medical aid must be provided to numerous victims synchronously under challenging circumstances including uncertainty about the condition of victims. Therefore, it is essential to have decision support tools which can generate fast solutions under uncertainty and utilise the available medical resources efficiently to provide victims with the needed treatments. We introduce an online optimisation problem for routing and scheduling of the ambulances under uncertainty about the triage levels and required treatment times of the victims in mass casualty incidents. Due to the lack of information in the initial emergency response phase, we assume that the triage level and treatment time of each victim can be disclosed online only once the condition of a victim is closely assessed by the medical team on one of the ambulances at the casualty location. We investigate this problem under two different scenarios with partial and no information about the conditions of victims. We follow the theoretical competitive analysis framework for online optimisation and prove the lower bounds on the competitive ratio of deterministic and randomised online solutions for both cases of partial and no prior information. Next, we introduce three novel online heuristics to solve this problem. We verify the quality of our online solutions against the offline optimal solutions that are provided under complete information on a comprehensive set of 1296 instances from the literature. Finally, we draw our conclusions in regard to the suitability of each of our solutions in various scenarios of information availability with different numbers of victims.

Citation

Shiri, D., Akbari, V., & Tozan, H. (2023). Online optimisation for ambulance routing in disaster response with partial or no information on victim conditions. Computers and Operations Research, 159, Article 106314. https://doi.org/10.1016/j.cor.2023.106314

• Journal Article Type: Article
• Acceptance Date: Jun 14, 2023
• Online Publication Date: Jun 24, 2023
• Publication Date: 2023-11
• Deposit Date: Jul 25, 2023
• Publicly Available Date: Jul 25, 2023
• Journal: Computers and Operations Research
• Print ISSN: 0305-0548
• Electronic ISSN: 1873-765X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 159
• Article Number: 106314
• DOI: https://doi.org/10.1016/j.cor.2023.106314
• Keywords: Ambulance routing; Mass emergency incident; Online optimisation; Competitive ratio; Partial information; Disaster relief
• Public URL: https://nottingham-repository.worktribe.com/output/22186872
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0305054823001788?via%3Dihub