We present a generalization of the binary paint shop problem (BPSP) to tackle an automotive industry application, the multi-car paint shop (MCPS) problem. The objective of the optimization is to minimize the number of color switches between cars in a paint shop queue during manufacturing, a known NP-hard problem. We distinguish between different sub-classes of paint shop problems, and show how to formulate the basic MCPS problem as an Ising model. The problem instances used in this study are generated using real-world data from a factory in Wolfsburg, Germany. We compare the performance of the D-Wave 2000Q and Advantage quantum processors to other classical solvers and a hybrid quantum-classical algorithm offered by D-Wave Systems. We observe that the quantum processors are well-suited for smaller problems, and the hybrid algorithm for intermediate sizes. However, we find that the performance of these algorithms quickly approaches that of a simple greedy algorithm in the large size limit.
@article{arxiv.2109.07876,
title = {Multi-car paint shop optimization with quantum annealing},
author = {Sheir Yarkoni and Alex Alekseyenko and Michael Streif and David Von Dollen and Florian Neukart and Thomas Bäck},
journal= {arXiv preprint arXiv:2109.07876},
year = {2021}
}
Comments
Accepted to IEEE International Conference on Quantum Computing and Engineering (QCE21)