Evolutionary Approach for Optimizing 3D Heterogeneous Bin Packing Problems

This paper presents a combinational Hybrid Genetic Algorithm (HGA) with packing tuning approach for solving Three Dimensional (3D) Single container arbitrary sized heterogeneous bin packing optimization problem, by considering practical constraints in the shipment container loading industries. Aim of this paper is to (i) pack 3D arbitrary sized heterogeneous bins in to a container. (ii) Improve packing by optimizing the empty volume inside the container using genetic approach. (iii) obtain a feasible packing pattern, various practical constraints like box orientation, stack priority, container stability, weight constraint, overlapping constraint, and shipment placement constraint were also considered. (iv) Tuning algorithm was used for sequential packing without gap. 3D container loading problem consists of ‘n’ number of boxes to be packed in to a container of standard dimension in such a way as to maximize the volume utilization and in turn profit. Furthermore, Boxes to be packed are of various sizes and of heterogeneous shapes. In this research work, several heuristic GA operators were proposed to solve the container loading problem that significantly improve search efficiency and help to load most of the heterogeneous boxes into a container along with the optimal position of loaded boxes, and aid box orientation with less computational time. Tuning algorithm was used to make the genetic output in to packing pattern in an understandable format and without empty space in less computational time. In general, combination of Hybrid GA in conjunction with the tuning algorithm is substantially better and more satisfactory than those obtained by applying heuristics to the bin packing directly.