For efficient construction-assemblies transportation, volume constrained 3D printing, dry stacking, and facility waste management, a common problem must be solved. It is the practical problem of packing irregular 3D rigid objects into a container with fixed dimensions so that the volume of the final packed objects is minimized. To solve this problem, a methodology is presented that begins with capturing the initial as-is 3D shape data for each object, followed by a metaheuristic-based packing optimization algorithm. This methodology is demonstrated to be effective in two situations with known optimum solutions and in a third situation involving packing of real-life as-is objects. A high-level selection algorithm that is designed to guide the search of possible object subsets, when not all objects can fit into a single predefined container, is discussed as well. Performance is examined for variations, and a preliminary sensitivity analysis is performed. The methodology and its key algorithms are demonstrated to produce effective packing solutions in a mostly automatic manner. Object packing for this class of applications in civil engineering can thus be potentially improved in terms of outcome efficiency and level of planning effort required.

为了有效地进行建筑组件运输，体积受限的3D打印，干式堆垛和设施废物管理，必须解决一个常见问题。将不规则的3D刚性对象包装到具有固定尺寸的容器中以使最终包装的对象的体积最小化是一个实际问题。为了解决这个问题，提出了一种方法，该方法从捕获每个对象的初始3D形状数据开始，然后是基于元启发式的打包优化算法。事实证明，该方法在两种情况下均具有已知的最佳解决方案是有效的，而在第三种情况下则涉及包装真实的实际物体。一种高级选择算法，旨在在并非所有对象都可以放入单个预定义容器的情况下，指导可能对象子集的搜索，也进行了讨论。检查性能是否有变化，并进行初步的灵敏度分析。演示了该方法及其关键算法，可以以最自动化的方式产生有效的包装解决方案。因此，可以在结果效率和所需计划工作水平方面潜在地改善土木工程中此类应用的对象打包。