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A Parallel Genetic Algorithm for Three Dimensional Bin Packing with Heterogeneous Bins

机译:异类垃圾箱三维装箱的并行遗传算法

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This paper presents a parallel genetic algorithm for three dimensional bin packing with heterogeneous bins using Hadoop MapReduce framework. The most common three dimensional bin packing problem which packs given set of boxes into minimum number of equal sized bins is proven to be NP Hard. The variation of three dimensional bin packing problem that allows heterogeneous bin sizes and rotation of boxes is computationally more harder than common three dimensional bin packing problem. The proposed MapReduce implementation helps to run the genetic algorithm for three dimensional bin packing with heterogeneous bins on multiple machines parallely and computes the solution in relatively short time.
机译:本文提出了一种使用Hadoop MapReduce框架针对异类垃圾箱进行三维垃圾箱打包的并行遗传算法。事实证明,最常见的三维垃圾箱包装问题是将给定的一组箱子包装到最小数量的相等大小的垃圾箱中,这被证明是NP Hard。允许异类容器尺寸和盒子旋转的三维容器填充问题的变化比普通的三维容器填充问题在计算上更加困难。提出的MapReduce实现有助于在多台机器上并行运行带有异类垃圾箱的三维垃圾箱遗传算法,并在较短的时间内计算出解决方案。

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