Optimization of Fourth Party Logistics Routing Considering Infection Risk and Delay Risk

In the context of the rapid development of ecommerce and the increasing demands for logistics services, particularly in the face of challenges posed by public health emergencies, this paper explores how to integrate supply chain resources and optimize delivery processes. It provides an indepth analysis of the characteristics of the Fourth Party Logistics Routing Optimization Problem (4PLROP) in complex environments, specifically focusing on the impacts of infection risk and delay risk, and proposes a new risk measurement tool. By constructing a mathematical model aimed at minimizing Conditional Value-at-Risk (CVaR) and improved Q-learning algorithm, the study addresses the 4PLROP while considering cost and risk constraints. This approach enhances the efficiency and service quality of the logistics industry, offers effective strategies for 4PL companies in the face of uncertainty, and provides customers with safer and more reliable logistics solutions, contributing to sustainable development.