Research on Emergency Dispatch Optimization of E-commerce Logistics Based on Multi Objective Optimization Model

Logistics networks are closely related to daily life, and ensuring network circulation and responding to emergencies are crucial. This article delves into the logistics network adjustment strategy, aiming to improve the ability of the logistics network to respond to emergencies. The study begins by analysing the daily delivery volume and revealing the cyclical and trending characteristics of logistics demand. Subsequently, the ARIMA model and the BP neural network model were used to predict the transportation data, which could take into account the autocorrelation and nonlinear characteristics of the time series data, so as to improve the accuracy of the prediction. Further, this paper considers the impact of specific situations, such as DC5 shutdowns, on logistics networks. By constructing a linear programming model and applying a simulated annealing algorithm, this paper optimizes the cargo distribution strategy to ensure that the load rate of some routes reaches 100% without overloading, while the overall average load rate is only 5.29%. This optimization not only improves the efficiency of the logistics network, but also enhances its robustness in the face of unexpected events. Through these studies, this paper not only enriches the theoretical basis of logistics network adjustment, but also provides practical tools and methods for the management and optimization of actual logistics network, which has important theoretical significance and practical value.