Constraint-based Scheduling for Paint Shops in the Automotive Supply Industry

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.

Solving the paintshop scheduling problem with memetic algorithms

“…It is a challenging problem -in fact, Winter and Musliu [2019a] have proven that the problem's decision variant is NP-complete. A detailed textual description and a formal problem description can be found in the work of Winter et al [2019].…”

“…Publicly available CPU benchmarks 2 show that our CPU's single-thread performance is about 1.7 times faster. Results from the constraint programming approach from Winter and Musliu [2019a] are shown in column CP. With constraint programming, only instances from the small instance set could be solved.…”

“…In literature, this scheduling problem has recently been introduced by Winter et al [2019], and is called the Paint Shop Scheduling Problem, abbreviated as PSSP. It is a complex combinatorial problem, and the decision variant of the problem was shown to be NP-complete by Winter and Musliu [2019a].…”

Optimizing Painting Sequence Scheduling Based on Adaptive Partheno-Genetic Algorithm

“…Each case of research has its unique characteristics that must be considered for developing appropriate models and solving approaches. In the automotive supply industry, the general paint scheduling problem has been proved to be a non-deterministic polynomial (NP) complete problem [3,4]. To reduce the difficulty of scheduling and obtain feasible solutions, a single objective, such as the number of color changes, is often considered.…”

“…However, these solution methods are not suitable for scheduling problems that consider due dates, color changes, and carrier changes. In references [4,10], the authors proposed a novel paint shop scheduling problem that aims to achieve the optimal schedule by minimizing color changes, minimizing carrier changes, carrier constraints, and due date constraints. They provided a novel metaheuristic solution approach using simulated annealing to solve this problem.…”