A hybrid differential evolution algorithm for multiple container loading problem with heterogeneous containers

• We investigate the multiple container loading problem, commonly known as the three-dimensional bin packing problem.
• We model it as a mixed integer programming model.
• We develop a differential evolution (DE) algorithm hybridized with a novel packing heuristic strategy.
• The proposed algorithm outperform existing solutions.

We consider a multiple container loading problem, commonly known as the three-dimensional bin packing problem (3D-BPP), which deals with maximizing container space utilization while the containers available for packing are heterogeneous, i.e., varying in size. The problem has wide applications in cargo transportation, warehouse management, medical packaging, and so on. We develop a differential evolution (DE) algorithm hybridized with a novel packing heuristic strategy, best-match-first (BMF), which generates a compact packing solution based on a given box packing sequence and a container loading sequence. The effectiveness of the proposed algorithm is evaluated on a set of industrial instances and randomly generated instances. The results show that the proposed algorithm outperforms existing solution approaches in terms of solution quality.