Packing problems have been studied for a long time and have great applications in real-world scenarios. In recent times, with problems in the industrial world increasing in size, exact algorithms are often not a viable option and faster approaches are needed. We study Monte Carlo tree search, a random sampling algorithm that has gained great importance in literature in the last few years. We propose three approaches based on MCTS and its integration with metaheuristic algorithms or deep learning models to obtain approximated solutions to packing problems that are also interpretable by means of MCTS exploration and from which knowledge can be extracted. We focus on two-dimensional rectangle packing problems in our experimentation and use several well known benchmarks from literature to compare our solutions with existing approaches and offer a view on the potential uses for knowledge extraction from our method. We manage to match the quality of state-of-the-art methods, with improvements in time with respect to some of them and greater interpretability.

包装问题已经被研究了很长时间，并且在现实场景中有很大的应用。近年来，随着工业界问题的规模不断扩大，精确的算法通常不是可行的选择，需要更快的方法。我们研究蒙特卡罗树搜索，这是一种随机采样算法，在过去几年中在文献中获得了非常重要的地位。我们提出了三种基于 MCTS 的方法及其与元启发式算法或深度学习模型的集成，以获得包装问题的近似解决方案，这些解决方案也可以通过 MCTS 探索来解释，并可以从中提取知识。我们在实验中重点关注二维矩形填充问题，并使用文献中的几个众所周知的基准将我们的解决方案与现有方法进行比较，并提供有关从我们的方法提取知识的潜在用途的观点。我们设法与最先进方法的质量相匹配，并对其中一些方法进行及时改进并提高可解释性。