Comparing Heuristics, Constraint Optimization, and Reinforcement Learning for an Industrial 2D Packing Problem

TL;DR

This paper compares heuristics, constraint optimization, and deep reinforcement learning for a 2D furniture packing problem, finding heuristics most efficient and effective, while RL requires further training and is less suitable.

Contribution

It provides an experimental comparison of different methodologies for a specific industrial packing problem, highlighting the strengths and limitations of each approach.

Findings

Greedy heuristic yields optimal solutions and fastest runtime.

Constraint optimization also finds optimal solutions but is slower.

Deep reinforcement learning underperforms without extensive training.

Abstract

Cutting and Packing problems are occurring in different industries with a direct impact on the revenue of businesses. Generally, the goal in Cutting and Packing is to assign a set of smaller objects to a set of larger objects. To solve Cutting and Packing problems, practitioners can resort to heuristic and exact methodologies. Lately, machine learning is increasingly used for solving such problems. This paper considers a 2D packing problem from the furniture industry, where a set of wooden workpieces must be assigned to different modules of a trolley in the most space-saving way. We present an experimental setup to compare heuristics, constraint optimization, and deep reinforcement learning for the given problem. The used methodologies and their results get collated in terms of their solution quality and runtime. In the given use case a greedy heuristic produces optimal results and outperforms the other approaches in terms of runtime. Constraint optimization also produces optimal results but requires more time to perform. The deep reinforcement learning approach did not always produce optimal or even feasible solutions. While we assume this could be remedied with more training, considering the good results with the heuristic, deep reinforcement learning seems to be a bad fit for the given use case.