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Artificial intelligence hybrid heuristic based on tabu search for the dynamic berth allocation problem

机译:基于禁忌搜索的人工智能混合启发式动态泊位分配问题

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摘要

This paper considers the Dynamic Berth Allocation Problem, in which vessels are assigned to discrete positions in berths. This problem, whose goal is to minimize the total time the vessels stay at the port, constitutes one of the most important processes at any containers terminal. We propose a hybrid metaheuristic that combines Tabu Search with Path Relinking, T~2S~*+PR. The results reached by this hybrid algorithm are compared with the optimal values given by the best mathematical model that appears in the literature for this problem, GSPP, and with a tabu search algorithm from the literature, t~2s. For small instances, the algorithm T~2S~* + PR is able to obtain most of the optimal solutions in an amount of computational time that is lower than the time required to solve the GSPP model. For medium and large size instances, GSPP cannot be solved to optimality, whereas the proposed hybrid algorithm outperforms t~2s. Moreover, the computational experiments carried out in this paper confirm the robustness of the proposed algorithm with respect to both the parameters governing the procedure and the problem size.
机译:本文考虑了动态泊位分配问题,其中船只被分配到泊位中的离散位置。这个问题的目标是使船只在港口停留的总时间最少,这是任何集装箱码头最重要的过程之一。我们提出了一种混合元启发式算法,该方法结合了禁忌搜索和路径重新链接T〜2S〜* + PR。将该混合算法获得的结果与文献中针对该问题的最佳数学模型GSPP给出的最佳值进行比较,并与文献中的禁忌搜索算法t〜2s进行比较。对于小实例,算法T〜2S〜* + PR能够以比解决GSPP模型所需的时间短的计算时间来获得大多数最优解。对于中型和大型实例,无法将GSPP求解到最优,而所提出的混合算法的性能优于t〜2s。此外,本文进行的计算实验证实了该算法在控制程序参数和问题大小方面的鲁棒性。

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