Zhao Tianhong, Tu Wei, Fang Zhixiang, Wang Xiaofan, Huang Zhengdong, Xiong Shengwu, Zheng Meng
Guangdong Key Laboratory of Urban InformaticsShenzhen University Shenzhen 518060 China.
Shenzhen Key Laboratory of Spatial Smart Sensing and ServiceShenzhen University Shenzhen 518060 China.
IEEE trans Intell Transp Syst. 2021 Mar 17;23(7):6709-6719. doi: 10.1109/TITS.2021.3061076. eCollection 2022 Jul.
The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide, posing a great threat to human beings. The stay-home quarantine is an effective way to reduce physical contacts and the associated COVID-19 transmission risk, which requires the support of efficient living materials (such as meats, vegetables, grain, and oil) delivery. Notably, the presence of potential infected individuals increases the COVID-19 transmission risk during the delivery. The deliveryman may be the medium through which the virus spreads among urban residents. However, traditional delivery route optimization methods don't take the virus transmission risk into account. Here, we propose a novel living material delivery route approach considering the possible COVID-19 transmission during the delivery. A complex network-based virus transmission model is developed to simulate the possible COVID-19 infection between urban residents and the deliverymen. A bi-objective model considering the COVID-19 transmission risk and the total route length is proposed and solved by the hybrid meta-heuristics integrating the adaptive large neighborhood search and simulated annealing. The experiment was conducted in Wuhan, China to assess the performance of the proposed approach. The results demonstrate that 935 vehicles will totally travel 56,424.55 km to deliver necessary living materials to 3,154 neighborhoods, with total risk [Formula: see text]. The presented approach reduces the risk of COVID-19 transmission by 67.55% compared to traditional distance-based optimization methods. The presented approach can facilitate a well response to the COVID-19 in the transportation sector.
2019年冠状病毒病（COVID-19）疫情已在全球蔓延，对人类构成了巨大威胁。居家隔离是减少身体接触以及相关COVID-19传播风险的有效方式，这需要高效的生活物资（如肉类、蔬菜、谷物和食用油）配送的支持。值得注意的是，潜在感染者的存在增加了配送过程中的COVID-19传播风险。送货员可能是病毒在城市居民中传播的媒介。然而，传统的配送路线优化方法并未考虑病毒传播风险。在此，我们提出一种新颖的生活物资配送路线方法，该方法考虑了配送过程中可能的COVID-19传播情况。我们开发了一种基于复杂网络的病毒传播模型，以模拟城市居民与送货员之间可能的COVID-19感染情况。提出了一个考虑COVID-19传播风险和总路线长度的双目标模型，并通过结合自适应大邻域搜索和模拟退火的混合元启发式算法进行求解。在中国武汉进行了实验，以评估所提方法的性能。结果表明，935辆车将总共行驶56,424.55公里，为3154个社区配送必要的生活物资，总风险为[公式：见原文]。与传统的基于距离的优化方法相比，所提方法将COVID-19传播风险降低了67.55%。所提方法能够促进交通运输部门对COVID-19做出良好应对。
