College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
School of Information Science and Engineering, Hangzhou Normal University, Hangzhou 311121, China.
Int J Environ Res Public Health. 2020 Mar 27;17(7):2275. doi: 10.3390/ijerph17072275.
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).
在大规模疫情爆发时，疫区可能有许多高危人员需要转移进行医学隔离。通常，这些人员分散在不同的地点，而可用的隔离车辆有限。因此，高效调度车辆将这些人员转移到隔离区域以控制疫情的传播是具有挑战性的。在本文中，我们针对高危人员转移制定了这样的隔离车辆调度问题，这比大多数著名的车辆路径问题都更加困难。为了有效地解决这个问题，我们提出了一种基于水波优化（WWO）元启发式算法和邻域搜索的混合算法。元启发式算法使用小种群快速探索解空间，而邻域搜索则采用逐步策略来提高解的准确性。计算结果表明，所提出的算法在处理中国杭州新冠疫情高峰期高危人员转移的实际问题实例时，明显优于几种现有算法，并获得了高质量的解决方案。
