| Id |
Subject |
Object |
Predicate |
Lexical cue |
| TextSentencer_T1 |
0-87 |
Sentence |
denotes |
Quarantine Vehicle Scheduling for Transferring High-Risk Individuals in Epidemic Areas. |
| TextSentencer_T2 |
88-222 |
Sentence |
denotes |
In a large-scale epidemic outbreak, there can be many high-risk individuals to be transferred for medical isolation in epidemic areas. |
| TextSentencer_T3 |
223-338 |
Sentence |
denotes |
Typically, the individuals are scattered across different locations, and available quarantine vehicles are limited. |
| TextSentencer_T4 |
339-491 |
Sentence |
denotes |
Therefore, it is challenging to efficiently schedule the vehicles to transfer the individuals to isolated regions to control the spread of the epidemic. |
| TextSentencer_T5 |
492-670 |
Sentence |
denotes |
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. |
| TextSentencer_T6 |
671-817 |
Sentence |
denotes |
To efficiently solve this problem, we propose a hybrid algorithm based on the water wave optimization (WWO) metaheuristic and neighborhood search. |
| TextSentencer_T7 |
818-984 |
Sentence |
denotes |
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. |
| TextSentencer_T8 |
985-1291 |
Sentence |
denotes |
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). |
