ABSTRACT
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.
ABSTRACT
When contagious diseases hit a city, such as MERS, SARS, and COVID-19, the problem arises as how to assign the limited supermarket resources to urban residential communities for government measures. In this study, in order to solve the assignment problem from supermarket resources to urban residential communities under the situation of the epidemic control, the discrete multi-objective particle swarm algorithm can be improved by introducing some new strategies, and the probability matrix can be used to simulate the many-to-many assignment relationship between residential communities and supermarkets. The ultimate purpose of this research is to achieve an optimal way to balance the two conflicting objectives, i.e. minimization of the cross-infection risk and maximization of the service coverage rate. Also, the optimization considers the accessible distance limit and the service capacity constraints of supermarkets for the feasible scheme. For this aim, we redefine the subtraction operator, add operator and multiply operator to generate the Pareto optimal solutions, and introduce a new study strategy based on the idea of differential evolution in the particle swarm algorithm (PSO-DE). In this work, we take the COVID-19 epidemic outbreak in Wuhan city of China as an example in the experiment. The simulation results are compared with the Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Algorithm (ACO) and the Particle Swarm Optimization with Roulette Wheel Selection (PSO-R), and these results have been shown that the algorithm PSO-DE proposed in this work has a better optimization performance in both objectives.