Score-Based Dispatching Strategy for Twin Rubber-Tired Gantry Cranes Leveraging Spring Elasticity
Abstract
Yard crane (YC) operations are critical to the overall productivity of container terminals, especially as terminals move toward higher levels of automation. This study proposes a score-based dispatching strategy for twin RTGCs operating within a single yard block. The proposed logic evaluates each job using four factors—distance between crane and job, job waiting time, estimated processing time, and an elasticity term inspired by spring mechanics that reflects the tendency of each crane to stay within its preferred working zone. These factors are normalized and combined into a single score, and the corresponding weights are optimized by a genetic algorithm (GA). Jobs with lower scores are given higher priority for assignment. A discrete-event simulation model of a twin RTGC system is developed using AutoMod® to assess the performance of the proposed strategy. The score-based rule is compared with conventional dispatching policies such as First-Come-First-Served (FCFS), Nearest-First-Served (NFS), and their weighted combination under various workload scenarios. Relative to the score-based strategy without elasticity, the inclusion of the elasticity term reduces average and maximum truck turnaround time by 7.51% and 7.79%, respectively; these improvements translate into higher yard throughput and strengthen the advantage over the benchmark dispatching rules. In particular, the elasticity term effectively mitigates crane interference while maintaining a balanced spatial distribution of work between the two cranes. These findings indicate that the proposed dispatching logic provides a practical and implementable control strategy for retrofitting existing RTGC systems and integrating them into terminal operating systems.
Fields of Study & Subjects
Maritime Ports and Logistics
Advanced Manufacturing and Logistics Optimization
Vehicle Routing Optimization Methods
Publication details
- Journal / source
- Applied Sciences
- Volume
- 16
- Issue
- 1
- Pages
- 463–463
- Publication date
- 1 January 2026
- Language
- EN