Optimizing quayside truck allocation: expert system for automating discharging operations planning

Abstract

This research investigates optimizing discharging truck allocation at container terminals, crucial hubs in global maritime logistics, by using a fuzzy logic approach to enhance container movements from ship to shore. Traditionally managed manually by ground handling staff, the truck allocation process is automated in this model to address the complexities of quayside operations. This study proposes a model that adapts to operational variables, reducing bottlenecks and increasing terminal throughput. By employing fuzzy logic for its adaptability and interpretability, the research provides a computational methodology suitable for complex quayside operations, involving fuzzification, inference, and defuzzification to transform raw data into actionable insights. Data were collected from two container terminals at a leading South Asian port, ranked among the top 30 global ports. The study used the Fuzzy Logic Toolbox in MATLAB and Python to effectively integrate a rule-based structure. The findings highlight the critical role of discharging truck allocation in enhancing terminal efficiency and operational integration, with the model demonstrating compatibility with the Terminal Operating System (TOS). Future research should focus on more dynamic and integrated operational planning systems to further improve efficiency in container terminal operations.