Multi-car paint shop optimization with quantum annealing

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...

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Bibliographic Details
Published in2021 IEEE International Conference on Quantum Computing and Engineering (QCE) pp. 35 - 41
Main Authors Yarkoni, Sheir, Alekseyenko, Alex, Streif, Michael, Von Dollen, David, Neukart, Florian, Back, Thomas
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2021
Subjects
Greedy algorithms
Image color analysis
NP-hard problem
optimization
Production facilities
Program processors
quantum annealing
Quantum computing
sequencing
Simulated annealing
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DOI10.1109/QCE52317.2021.00019

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Summary: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.
DOI:10.1109/QCE52317.2021.00019