Quay Crane Scheduling in Container Terminals Using a Hybrid Genetic Algorithm

Abstract

Container terminals are crucial nodes within the global supply chain, playing a vital role in the efficient movement of goods. Effective scheduling of Quay Cranes (QCs) is a key factor in maximizing port productivity and minimizing delays. This research investigates the Quay Crane Scheduling Problem (QCSP) using a Hybrid Genetic Algorithm (HGA). The proposed HGA method combines the exploratory power of genetic algorithms with refined local search strategies to boost both solution quality and convergence speed. Extensive computational experiments using established benchmark datasets confirm the effectiveness of the hybrid algorithm, revealing a significant reduction in the make span and enhanced utilization of quay crane resources. The findings of this study contribute to the broader understanding of algorithmic optimization for QCSP, providing valuable insights for improving operational efficiency in real-world container terminal environments.