Optimization of Empty Container Operations by Demand Prediction and Simulation (Case Study: Shahid Rajaee Port)

With the increasing trend of containerization in large ports, empty container provision and management have become a major logistics service at ports. Every day, a large number of empty containers should be available to be sent to different logistics yards for stuffing and export purposes. The empty container demand forecast can highly improve the time and resource management by both terminal operators and port authorities. In this paper, Shahid Rajaee Port, as the largest and most important commercial port in Iran is investigated. A private terminal operator is the main provider and responsible party for the majority of empty container operations. The optimization process of empty container operations using machine learning and artificial intelligence methods is investigated in this paper. Given the possibility of predicting the demand for empty containers, it is possible to reduce the daily operation volume through advance planning and consider necessary measures regarding the appropriate spatial distribution of empty containers before the demand arises. A simulation software is also used for different managerial scenarios. The results show that by sending half of the monthly predicted demand of only six out of ۷۵ depot yards as well as the container yard in off-peak times, the total empty container delivery time can decrease up to ۳۰ %. Besides that, planning a schedule for an asynchronous call of trucks to pick up the requested empty containers has proved to reduce the queue lengths and average waiting times up to ۹۰%.

کلیدواژه ها:

Empty Containers ، Deep Learning ، Simulation ، optimization ، Shahid Rajaee Port