Quarantine Vehicle Scheduling for Transferring High-Risk Individuals in Epidemic Areas

Min-Xia Zhang, Hong-Fan Yan, Jia-Yu Wu and Yu-Jun Zheng
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Min-Xia Zhang: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Hong-Fan Yan: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Jia-Yu Wu: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Yu-Jun Zheng: School of Information Science and Engineering, Hangzhou Normal University, Hangzhou 311121, China

IJERPH, 2020, vol. 17, issue 7, 1-17

Abstract: In a large-scale epidemic outbreak, there can be many high-risk individuals to be transferred for medical isolation in epidemic areas. Typically, the individuals are scattered across different locations, and available quarantine vehicles are limited. Therefore, it is challenging to efficiently schedule the vehicles to transfer the individuals to isolated regions to control the spread of the epidemic. In this paper, we formulate such a quarantine vehicle scheduling problem for high-risk individual transfer, which is more difficult than most well-known vehicle routing problems. To efficiently solve this problem, we propose a hybrid algorithm based on the water wave optimization (WWO) metaheuristic and neighborhood search. The metaheuristic uses a small population to rapidly explore the solution space, and the neighborhood search uses a gradual strategy to improve the solution accuracy. Computational results demonstrate that the proposed algorithm significantly outperforms several existing algorithms and obtains high-quality solutions on real-world problem instances for high-risk individual transfer in Hangzhou, China, during the peak period of the novel coronavirus pneumonia (COVID-19).

Keywords: public health emergencies; epidemics; medical isolation; vehicle scheduling; optimization; water wave optimization (WWO) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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