We study the two-dimensional hierarchical rectangle packing problem, motivated by applications in analog integrated circuit layout, facility layout, and logistics. Unlike classical strip or bin packing, the dimensions of the container are not fixed, and the packing is inherently hierarchical: each item is either a rectangle or a block occurrence, whose dimensions are a solution of another packing problem. This recursive structure reflects real-world scenarios in which components, boxes, or modules must be packed within higher-level containers. We formally define the problem and propose exact formulations in Mixed-Integer Linear Programming and Constraint Programming. Given the computational difficulty of solving complex packing instances directly, we propose decomposition heuristics. First, we implement an existing Bottom-Up baseline method that solves subblocks before combining them at higher levels. Building upon this, we introduce a novel multilevel Logic-based Benders Decomposition method. This heuristic method dynamically refines block dimension constraints, eliminating the need for manual selection of candidate widths or aspect ratios. Experiments on synthetic instances with up to seven hierarchy levels, 80 items per block, and limited computation time show that the proposed decomposition significantly outperforms both monolithic formulations and the Bottom-Up method in terms of solution quality and scalability.
翻译:本研究针对二维层次矩形装箱问题展开研究,该问题在模拟集成电路布局、设施规划和物流等领域具有重要应用价值。与经典的条带装箱或箱式装箱问题不同,该问题的容器尺寸并非固定,且装箱结构具有内在层次性:每个待排布对象可以是矩形,也可以是区块实例,其尺寸本身又是另一个装箱问题的解。这种递归结构反映了现实场景中组件、箱体或模块必须被装入更高层级容器的实际情况。我们首先对该问题进行形式化定义,并提出了混合整数线性规划和约束规划两种精确建模方法。鉴于直接求解复杂装箱实例的计算困难性,我们提出了分解启发式算法。首先,我们实现了现有的自底向上基线方法,该方法先在底层求解子区块,再向高层逐级组合。在此基础上,我们提出了一种新颖的多级逻辑Benders分解方法。这种启发式方法能动态优化区块尺寸约束,无需人工预设候选宽度或宽高比。在包含多达七个层次级别、每区块80个物品的合成算例上进行的实验表明,在有限计算时间内,所提出的分解方法在求解质量和可扩展性方面均显著优于整体求解方法和自底向上方法。