COMPACT: Concurrent or Ordered Matrix-based Packing Arrangement Computation Technique

TL;DR

COMPACT is a novel raster-based packing algorithm that allows arbitrary object rotation, introduces a new performance metric for space efficiency, and simplifies optimization by removing overlap constraints.

Contribution

It presents a new raster-based packing method that supports arbitrary rotations, a novel efficiency metric, and unconstrained optimization, advancing packing solution techniques.

Findings

Effective packing arrangements for various objects

Supports arbitrary object rotations in raster methods

Improves efficiency with loop-free raster operations

Abstract

Packing optimization is a prevalent problem that necessitates robust and efficient algorithms that are also simple to implement. One group of approaches is the raster methods, which rely on approximating the objects with pixelated representations. Although they are versatile in treating irregular geometries, the raster methods received limited attention in solving problems involving rotatable objects, where available studies generally analyze only right-angled rotations. In addition, raster approximation allows the use of unique performance metrics and indirect consideration of constraints, which have not been exploited in the literature. This study presents the new Concurrent or Ordered Matrix-based Packing Arrangement Computation Technique (COMPACT). The method relies on raster representations of the objects that can be rotated by arbitrary angles, unlike the right-angled rotation restrictions imposed in many existing packing optimization studies based on raster methods. The raster approximations are obtained through loop-free operations that improve efficiency. Besides, a novel performance metric is introduced, which favors efficient filling of the available space by maximizing the internal contact between the objects as well as the contact between the objects and domain boundaries. Moreover, the objective functions are exploited to discard overlap and overflow constraints and enable the use of unconstrained optimization methods. Several test problems involving concurrent and ordered packing of multiple rectangular and circular objects into square bins are investigated. The results show that the proposed technique performs effectively in determining the packing arrangements.