Abstract
Factories in the automotive supply industry paint a large number of items requested by car manufacturing companies on a daily basis. As these factories face numerous constraints and optimization objectives, finding a good schedule becomes a challenging task in practice, and full-time employees are expected to manually create feasible production plans. In this study, we propose novel constraint programming models for a real-life paint shop scheduling problem. We evaluate and compare our models experimentally by performing a series of benchmark experiments using real-life instances in the industry. We also show that the decision variant of the paint shop scheduling problem is NP-complete.
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