Optimizing Container Loading and Unloading through Dual-Cycling and Dockyard Rehandle Reduction Using a Hybrid Genetic Algorithm

TL;DR

The paper tackles the NP-hard problem of port container handling by jointly optimizing Quay Crane Dual-Cycling (QCDC) and dockyard rehandles. It introduces QCDC-DR-GA, a hybrid Genetic Algorithm with mixed 1D unloading-sequence and 2D dockyard-plan chromosomes and specialized crossover/mutation operators, to minimize total turnaround time via the objective $T = \alpha w_s + \beta w_d + \gamma R$. Across six datasets, the approach achieves 15–20% reductions in total operation time for large ships and is statistically significant at the 5% level, outperforming four baselines that optimize components in isolation. The results validate the benefits of integrated optimization for port operations, improving resource utilization and throughput without new infrastructure. The work contributes a novel hybrid GA framework, tailored 1D/2D genetic operators, and empirical evidence supporting holistic port scheduling and relocation planning.

Abstract

This paper addresses the NP-hard problem of optimizing container handling at ports by integrating Quay Crane Dual-Cycling (QCDC) and dockyard rehandle minimization. We realized that there are interdependencies between the unloading sequence of QCDC and the dockyard plan and propose the Quay Crane Dual Cycle - Dockyard Rehandle Genetic Algorithm (QCDC-DR-GA), a hybrid Genetic Algorithm (GA) that holistically optimizes both aspects: maximizing the number of Dual Cycles (DCs) and minimizing the number of dockyard rehandles. QCDC-DR-GA employs specialized crossover and mutation strategies. Extensive experiments on various ship sizes demonstrate that QCDC-DR-GA reduces total operation time by 15-20% for large ships compared to existing methods. Statistical validation via two-tailed paired t-tests confirms significant improvements at a 5% significance level. The results underscore the inefficiency of isolated optimization and highlight the critical need for integrated algorithms in port operations. This approach increases resource utilization and operational efficiency, offering a cost-effective solution for ports to decrease turnaround times without infrastructure investments.

Figures (9)

• Figure 1: (a) Unloading using single cycling; (b) Simultaneous unloading and loading using double cycling.
• Figure 2: Container arrangement on a ship, illustrating the three-dimensional storage structure. (a) Side view showing the distribution of containers across rows and tiers, and (b) front view showing bays and tiers. This schematic helps clarify the spatial dimensions and structural layout considered in the container loading and stowage optimization problem.
• Figure 3: Illustration of unloading and loading plan of a ship row.
• Figure 4: 1D Two-Point Crossover technique.
• Figure 5: 2D Two-Point Substring Crossover technique.