Online Packing

Online packing, a computationally challenging problem, focuses on efficiently arranging items into containers, often with real-world constraints like irregular shapes, weight limits, and stability requirements. Current research emphasizes learning-based approaches, employing techniques like gradient field learning, reinforcement learning, and hybrid quantum-classical algorithms, to improve packing density and speed, particularly for complex scenarios involving 3D objects and varying item types. These advancements have significant implications for logistics, manufacturing, and other industries by optimizing resource utilization and reducing costs, while also contributing to the broader field of combinatorial optimization through the development of novel algorithms and model architectures.