A Parallel Genetic Algorithm for Three Dimensional Bin Packing with Heterogeneous Bins
This work addresses a computationally harder variant of bin packing for logistics and resource allocation, but it is incremental as it applies an existing parallel framework to a known problem.
The paper tackles the NP-hard three-dimensional bin packing problem with heterogeneous bins and box rotation by proposing a parallel genetic algorithm using Hadoop Map-Reduce, which computes solutions in relatively short time on multiple machines.
This paper presents a parallel genetic algorithm for three dimensional bin packing with heterogeneous bins using Hadoop Map-Reduce 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 Map-Reduce 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.