WTPST: Waiting Time Prediction for Steel Logistical Queuing Trucks

Abstract

In the absence of reasonable queuing rules for trucks transporting steel raw materials, the trucks have to wait in long queues inside and outside the steel mill. It necessitates effective waiting time prediction method to help the managers to make better queuing rules and enhance the drivers’ satisfaction. However, due to the particularity of steel logistic industry, few researches have conducted to tackle this issue. In transforming process of steel logistical informationization, huge amount of data has been generated in steel logistics platform, which offers us an opportunity to address this issue. This paper presents a waiting time prediction framework, called WTPST. Through analyzing the data from multiple sources including the in-plant and off-plant queuing information, in-plant trucks’ unloading logs and cargo discharging operation capability data, some meaningful features related to the queuing waiting time are extracted. Based upon extracted features, a Game-based modeling mechanism is designed to proliferate predicting precision. We demonstrate that WTPST is capable of predicting the waiting time for each queuing truck, which enhances the efficiency of unloading in steel logistics. In addition, the comparison experimental results proves the prediction accuracy of WTPST outperforms the baseline approaches.