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Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm

机译:自适应遗传算法优化集装箱异构装载问题

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This paper studies an optimized container loading problem with the goal of maximizing the 3D space utilization. Based on the characteristics of the mathematical loading model, we develop a dedicated placement heuristic integrated with a novel dynamic space division method, which enables the design of the adaptive genetic algorithm in order to maximize the loading space utilization. We use both weakly and strongly heterogeneous loading data to test the proposed algorithm. By choosing 15 classic sets of test data given by Loh and Nee as weakly heterogeneous data, the average space utilization of our algorithm reaching 70.62% outperforms those of 13 algorithms from the related literature. Taking a set of test data given by George and Robinson as strongly heterogeneous data, the space utilization in this paper can be improved by 4.42% in comparison with their heuristic algorithm.
机译:本文研究了一个优化的集装箱装载问题,旨在最大程度地利用3D空间。根据数学加载模型的特点,我们开发了一种与新颖的动态空间划分方法相集成的专用布局启发式方法,该方法可以设计自适应遗传算法,以最大程度地利用加载空间。我们使用弱和强异构负载数据来测试所提出的算法。通过选择Loh和Nee给出的15个经典测试数据集作为弱异构数据,我们算法的平均空间利用率达到70.62%,优于相关文献中的13种算法。将George和Robinson提供的一组测试数据作为强异构数据,与启发式算法相比,本文的空间利用率可提高4.42%。

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