Casualty transport scheduling considering survival probability and injury classification. (November 2021)

• Record Type: Journal Article
• Authors: Feng, Yi; Liu, Tingwei; Hu, Zhineng; Wang, Dujuan; Cheng, T.C.E; Yin, Yunqiang
• Is Part Of: Computers & industrial engineering. Volume 161(2021)
• Publication Date: 2021-11
• Subjects: Disaster scenarios -- Injury classification -- Transport strategy -- Ant colony algorithm -- Emergency management; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107655
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Casualty transportation with injury classification and survival function is studied. Transport strategies for different disaster scales and scenarios are discussed. An improved ant colony algorithm is proposed to solve casualty transportation. Numerical studies are performed to assess performance of algorithm. Several emergency management recommendations are proposed. Abstract: The occurrence of disasters often causes a large number of wounded persons. The rapid rescue and transport of wounded persons for medical care is a key concern of emergency management. To maximize the number of survivors, we propose a new transport strategy based on injury classification, called the Red-Yellow-Green-Sequential (RYGS) transport strategy, which prioritizes the transport order by the degree of injury of the wounded persons. To solve this intractable problem, we propose an improved ant colony optimization algorithm (IACO) to solve it approximately. IACO not only involves a pheromone updating method according to the characteristics of the problem itself, but also combines the particle swarm optimization algorithm and the genetic algorithm to avoid local convergence. Through numerical studies, we demonstrate that IACO outperforms ACO under different transport strategies in different scales and scenarios. In addition, we demonstrate through numerical studies the superiority of RYGS among three transport strategies in different scenarios and scales. We also demonstrate that the combination of IACO and RYGS performs best in different scenarios and scales. Finally, we conduct a sensitivity analysis to analyze the impacts of the different medical resources on the objective for the problems with different combinations of scales and scenarios based on IACO. We also discuss the practical implications of the findings for emergency management.