A Multi-Heuristic Algorithm for Multi-Container 3-D Bin Packing Problem Optimization Using Real World Constraints

With the growing demand for sustainable and optimal packaging solutions, this study proposes a novel two-stage algorithm for the multi-container three-dimensional bin packing problem. The research addresses this problem within the context of a real-world industrial scenario and implements several practical constraints including: full shipment, customer positioning requirements, and product geometric interlocking, for increased stability and with the purpose of minimizing the use of plastic wrapping and/or additional supporting surfaces. The main optimization target is to minimize the total number of containers used in the palletization process of custom orders with varying degrees of complexity. The proposed algorithm includes two stages/phases of processing. In the first phase, the algorithm uses constructive heuristics to generate homogeneous product layers. The layers are then stacked to produce blocks, which are then placed on individual containers or pallets. The second phase packs the leftover items using a genetic algorithm. The performance of the proposed solution is benchmarked using real-world industrial data, as well as a more classic academic benchmark. It is demonstrated, across a very large set of orders, that the algorithm always achieves solutions for full palletization of the orders. The analysis shows that the approach is generic and the quality of the solutions generated is relatively even for both small and large, homogeneous and heterogeneous problem instances.