Paint shop vehicle sequencing based on quantum computing considering color changeover and painting quality
Jing Huang, Hua-Tzu Fan, Guoxian Xiao, Qing Chang

TL;DR
This paper explores using quantum computing, specifically QAOA, to optimize vehicle sequencing in paint shops, considering both color changeover and painting quality, which is challenging for classical methods.
Contribution
It introduces a quantum computing approach to vehicle sequencing that accounts for painting quality, extending beyond traditional cost-focused methods.
Findings
Quantum algorithms can model complex sequencing problems.
Preliminary results show potential for quantum solutions in manufacturing.
The approach considers both changeover and quality costs.
Abstract
As customer demands become increasingly diverse, the colors and styles of vehicles offered by automotive companies have also grown substantially. It poses great challenges to design and management of automotive manufacturing system, among which is the proper sequencing of vehicles in everyday operation of the paint shop. With typically hundreds of vehicles in one shift, the paint shop sequencing problem is intractable in classical computing. In this paper, we propose to solve a general paint shop sequencing problem using state-of-the-art quantum computing algorithms. Most existing works are solely focused on reducing color changeover costs, i.e., costs incurred by different colors between consecutive vehicles. This work reveals that different sequencing of vehicles also significantly affects the quality performance of the painting process. We use a machine learning model pretrained on…
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Taxonomy
TopicsTannin, Tannase and Anticancer Activities · Industrial Vision Systems and Defect Detection
