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A novel hybrid tabu search and Q-learning algorithm for optimizing complex container loading problems
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AI
DOI：10.1080/03155986.2026.2657134.png)
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Abstract
En 中文
This paper introduces a hybrid algorithm (TSQL) that combines tabu search and Q-learning to address the challenges of container loading problems (CLPs). Recognized as a crucial logistics optimization task, CLP aims to maximize volume utilization within containers. However, large-scale CLPs are computationally intensive, limiting the applicability of exact solutions. The proposed TSQL algorithm leverages Q-learning to dynamically guide the neighborhood exploration process within tabu search, achieving an effective balance between exploration and exploitation. Comparative analysis on benchmark datasets reveals that TSQL consistently surpasses traditional tabu search in both convergence speed and solution quality, especially in scenarios with high problem complexity. To further validate its effectiveness, TSQL was benchmarked against several state-of-the-art algorithms across seven problem classes and achieved the second-best average performance overall, outperforming many hybrid and metaheuristic alternatives. This hybrid approach offers significant potential for logistics applications that involve complex loading and packing decisions and can support more sustainable operations by improving space utilization.
Keywords:
Container loading problem
hybrid metaheuristics
tabu search
Q-learning
reinforcement learning
logistics optimization