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

TL;DR

This paper introduces a parallel genetic algorithm implemented on Hadoop Map-Reduce to efficiently solve the NP-hard three-dimensional bin packing problem with heterogeneous bins and box rotations.

Contribution

It presents a novel parallel genetic algorithm framework for complex 3D bin packing with heterogeneous bins using distributed computing.

Findings

The algorithm efficiently finds solutions for large instances.

Parallel implementation reduces computation time significantly.

Effective handling of heterogeneous bins and rotations.

Abstract

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.