ARIMA-based Freight Forecasting and Network Optimization in E-commerce Logistics

Abstract

Considering the proliferation of e-commerce platforms exemplified by Taobao and JD, the paradigm of online shopping has evolved into an integral facet of contemporary societal existence. To enhance network transport performance, it is essential to predict freight volume, assess existing capacity, and establish new sites to alleviate network pressure. This study focuses on a logistics transportation network, utilizing daily freight turnover data from January 1, 2021, to December 31, 2022. The ARIMA time series method is employed to forecast freight volumes at different sites within the e-commerce logistics network. Taking three logistics site route combinations, namely DC14→DC10, DC20→DC35, and DC25→DC62, as examples, two differencing operations are applied to meet the stationarity requirement of the ARIMA model. Autocorrelation and partial autocorrelation coefficients are used for preliminary model order determination. By minimizing the Akaike Information Criterion (AIC) value, an ARIMA (5) model is established. Single and multiple-step predictions are conducted, resulting in forecast curves for future freight volumes. Subsequently, an evaluation of the transportation network is performed, considering the importance of nodes and routes. The entropy weight method is applied to determine the weights of evaluation indicators. Importance indices for nodes and routes are calculated, leading to a ranking. For the assessment and selection of new site capabilities, Fisher's discriminant function is employed to classify different sites. The valuation level for site selection is determined, providing a scientific basis for the systematic choice of new sites.