A hybrid Genetic Algorithm and Simulated Annealing approach for multi-level 3D bin packing problem

The 3D-bin packing problem consisted of making the suitable decision on how to arrange a set of rectangular objects in their bins. It entails optimizing the allocation of a set of items into a minimal number of bins, each with a fixed capacity. In our paper, we are interested to a three-dimension bin-packing problem, which is a combinatorial optimization problem and is known to be NP-Hard problem. We refer to the recent research work to create our hierarchical hybrid optimization named GenSA-3DBPP to solve the three-Dimension bin-packing problem. The empirical study aims to highlight the improvements made with GenSA-3DBPP on three levels. To solve this issue, we proposed The threeDorientationsII heuristic, which places the item in the orientation that maximizes load while respecting weight constraints. The Genetic Algorithm level explores the search space to tackle the main purpose of the problem, which is the minimization of both number of bins and wasted space. The Simulated Annealing level utilizes the Genetic Algorithm’s best solution as input and generates a best solution.