Optimizing container loading and unloading through dual-cycling and dockyard rehandle reduction using a hybrid genetic algorithm

Abstract

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 identify critical inter-dependencies between the unloading sequence of QCDC and the dockyard container arrangement and propose the Quay Crane Dual Cycle-Dockyard Rehandle Genetic Algorithm (QCDC-DR-GA), a hybrid Genetic Algorithm (GA) that holistically optimizes both aspects jointly: maximizes the number of Dual Cycles (DCs) and minimizes the number of dockyard rehandles. QCDC-DR-GA employs a mixed 1D–2D chromosome representation with specialized crossover and mutation operators tailored to each component. Extensive experiments across six scenarios spanning small, medium, and large container ships demonstrate that QCDC-DR-GA reduces total operation time by up to 30.1% compared to existing methods (20.1% on average across large-ship scenarios). Statistical validation via two-tailed paired t -tests confirms significant improvements in 20 out of 24 pairwise comparisons at the 5% significance level ( $$\alpha = 0.05$$ α = 0.05 , $$t_{19}(0.05) = 2.093$$ t 19 ( 0.05 ) = 2.093 ). 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.