3D heterogeneous bin packing framework for multi-constrained problems using hybrid genetic approach

This work presents distinct methodologies in using Genetic Algorithm (GA) for optimizing Three Dimensional (3D) packing of heterogeneous shaped bins with arbitrary sizes into a prismatic container, by considering the major real time packing constraints such as load bearing constraint, placement constraint, stability constraint, overlapping constraint, orientation constraint and weight constraint. The primary aim of this research is focused in optimizing the packing of heterogeneous prismatic bins of arbitrary sizes into standard rectangular commercial containers by obeying the above mentioned packaging constraints. Different genetic approaches adopted to achieve these goals are Binary coded GA, Decimal coded GA with and without penalty fitness function, Constrained GA with maximization and minimization fitness function, Heuristic GA and Hybrid GA. GA has been used to minimize the unused void space in the interior of the container by loading as much heterogeneous bins, by satisfying the packing constraints. Tweaking Algorithm (TA) is an application dependent heuristic algorithm applied in this research and has been used to enhance the genetic output by filling the remaining unused empty space inside the container. TA has also been enhanced in converting the obtained output into packer readable box packing sequence in tabular and diagrammatical format. In general, combination of GA and TA are considerably at par compared with the heuristic techniques for box packing.