Enhancing Container Handling Operations in Maritime Terminals Using the Ant Colony Optimization

Numerous studies have underscored the significance of scheduling and optimization challenges within maritime terminals. This dissertation examines how to optimize container movements specifically for export operations, simultaneously taking into account the operating sequences of yard cranes and trucks. It also considers any potential interference that may arise among yard cranes. A survey of existing literature on yard crane scheduling indicates a lack of work addressing both unproductive crane moves and possible crane-to-crane interferences at the same time, which constitutes an innovative element in our study. Initially, the container loading scheduling task is formulated as a mixed-integer linear program, where the objective function aims to minimize the overall handling time required by the yard cranes. The mathematical model incorporates various assumptions that address interference effects and non-productive movements. In order to tackle this problem, an Adaptive Large Neighborhood Search (ALNS) heuristic is introduced. This strategy proves effective in managing optimization issues in container terminals, regardless of the size of the problem—whether it involves 10, 20, or even 100 containers. The data utilized for validating the method are intentionally generated, allowing for differences in both the number of containers and the amount of accessible handling equipment. Extensive testing verified the ALNS algorithm’s usefulness. Various situations were tested by combining various removal and insertion strategies, and the results demonstrated the ALNS method’s robustness.