Identifying key factors influencing import container dwell time using eXplainable Artificial Intelligence

Abstract

In a container terminal, the length of time that containers remain in the yard, known as Container Dwell Time (CDT), is considered one of the significant operational indicators due to its direct correlation with terminal productivity and efficiency. However, due to complex processing procedure and the involvement of various logistics stakeholders, CDT is subject to high uncertainty, making it more difficult for the terminal to manage. To address this issue, this paper presents a comprehensive framework to identify the Key Factors (KFs) influencing prolongation of CDT for import containers. In order to elucidate abnormal cases from dataset which contains yard loading information, the Process Mining (PM) method is utilized. Subsequently, XAI has been utilized to identify the KFs of import CDT. To reflect reality as closely as possible, we collected event data from a container terminal in Busan, Korea. Based on experiments, the KFs thus identified were: 1) Temperature, 2) Weight of container, 3) Voyage number of container 4) Block, 5) Shipping company, and 6) Month of discharging. To conclude, we formulated domain knowledge-based interpretations of the six most influential KFs.