This study investigates the Container Consolidation Problem (CCP), a critical operational challenge in container terminals where containers with specific attributes must be relocated during yard crane idle periods. The primary objective is to maximize yard space availability for incoming vessels by strategically grouping containers, thereby alleviating storage pressure and enhancing throughput. A mixed-integer programming model is formulated to minimize the total handling time, incorporating complex constraints related to crane availability, relocation sequencing, and slot assignment. Due to the combinatorial complexity inherent in large-scale yard operations, a comprehensive optimization framework is proposed. This framework balances computational efficiency with solution quality, offering a robust approach to solve large-scale instances within practical time limits. Computational experiments demonstrate that the proposed methodology consistently yields high-quality solutions, effectively resolving the trade-off between solution speed and optimality. The research provides not only a novel methodological perspective for solving this NP-hard problem but also offers significant practical value. By optimizing crane scheduling, the model directly contributes to reducing operational costs, improving the turnover rate of yard space, and strengthening the overall efficiency of the maritime supply chain.
Zhao et al. (Mon,) studied this question.
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