将嵌入池田地图的遗传算法应用于多通道仓库的拣货问题

2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS) Pub Date : 2014-12-01 DOI:10.1109/CIPLS.2014.7007161

Michael Stauffer, Remo Ryter, D. Davendra, Rolf Dornberger, T. Hanne

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引用次数: 4

摘要

为了解决排序问题，本研究引入了一种嵌入池田地图的遗传算法。通过30个不同复杂度的测试实例，将基于混沌的算法与基于正则伪随机数的遗传算法进行了比较。结果表明，基于混沌的遗传算法具有较好的综合性能，特别是对于较大规模的问题实例。结果的统计配对t检验比较进一步强化了基于混沌的遗传算法明显更好的事实。

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A genetic algorithm with an embedded Ikeda map applied to an order picking problem in a multi-aisle warehouse

An Ikeda map embedded genetic algorithm is introduced in this research in order to solve the order picking problem. The chaos based algorithm is compared against the canonical pseudo-random number based genetic algorithm over thirty test instances of varying complexity. From the results, the chaos based genetic algorithm is shown to have better overall performance, especially for larger sized problem instances. The statistical paired t-test comparison of the results further reinforces the fact that the chaos based genetic algorithm is significantly better performing.

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2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS)

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