3D Arbitrary Sized Heterogeneous Bin Packing Using Hybrid Multi Constrained Optimization: Genetic Approach

Abstract

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 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 empty volume inside the container using genetic approach. (iii) obtain feasible packing pattern, various practical constraints like box orientation, stack priority, container stability, weight constraint, overlapping constraint, shipment placement constraint were also considered. (iv) Tuning algorithm used sequential packing without gap. 3D container loading problem consists of „n‟ number of boxes being to be packed in to a container of standard dimension in such a way to maximize volume utilization and inturn 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 container loading problem that significantly improves search efficiency and to load most of heterogeneous boxes into a container along with optimal position of loaded boxes, 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 conjunction with tuning algorithm being substantially better and satisfactory than those obtained by applying heuristics to the bin packing directly.