Fourth Party Logistics Routing Optimization Problem Based on Conditional Value-at-Risk Under Uncertain Environment

Abstract

In order to improve the level of logistics service and considering the impact of uncertainties such as bad weather and highway collapse on fourth party logistics routing optimization problem, this paper adopts Conditional Value-at-Risk (CVaR) to measure the tardiness risk, which is caused by the uncertainties, and proposes a nonlinear programming mathematical model with minimized CVaR. Furthermore, the proposed model is compared with the VaR model, and an improved Q-learning algorithm is designed to solve two models with different node sizes. The experimental results indicate that the proposed model can reflect the mean value of tardiness risk caused by time uncertainty in transportation tasks and better compensate for the shortcomings of the VaR model in measuring tardiness risk. Comparative analysis also shows that the effectiveness of the proposed improved Q-learning algorithm.