Optimizing 3D Irregular Object Packing from 3D Scans Using Metaheuristics

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.