Interval-Tree based Multi-Objective 3D Bin Packing using Evolutionary Extreme Point Heuristic

The three-dimensional bin packing problem represents a central challenge in logistics. It enables the efficient modeling of transportation and storage capacity usage, which optimization can lead to lower costs and enable more sustainable transportation practices. Alongside minimizing the storage capacity of loads, practical applications should also take into account factors such as stability. This work introduces a novel method that addresses the problem as a multi-objective optimization challenge, considering multiple factors for minimizing the required height and maximizing stability simultaneously. This approach combines evolutionary algorithms with the extreme point heuristic to achieve an adaptive solution. Additionally, a geometric model based on interval trees is presented, which serves as the foundation for solving the problem. The effectiveness and flexibility of the proposed method are evaluated through experiments on publicly available benchmarks. Experimental results show that the proposed method significantly enhances packing efficiency and stability, making it highly applicable to real-world logistics.