Risk control of epidemic in urban cold-chain transportation

Abstract

The COVID-19 pandemic has severely disrupted the daily running of urban logistics system, thereby increasing the resilience and health requirements in the design of sustainable logistics system. Most scientific contributions have enriched the knowledge of how to deal with viral infection threats from the external environment but ignored the virus transmission from fomites within the logistics system. This study designs an urban cold-chain logistic network with control strategies to reduce the epidemic risk caused by cold-chain fomites while concurrently balancing the total cost and customer satisfaction, which is modeled as a multi-objective vehicle routing problem (MOVRP). The control strategies are modeled by dividing customer points into small groups, and the virus transmission in populations is considered when evaluating epidemic risk. A multi-objective evolutionary algorithm with special initialization (MOEA-SI) is proposed to solve this problem. Simulation results show that all the strategies can efficiently reduce the epidemic risk with reasonable expense of total cost and customer satisfaction. The introduction of special initialization strategy enables the MOEA-SI to have good performance in balancing the three objectives. The above results can enrich the knowledge of how to deal with unexpected epidemics and help the scientific decision-making in sustainable urban management.