A large neighborhood search approach for the paint shop scheduling problem

Minimizing the setup costs caused by color changes is one of the main concerns for paint shop scheduling in the automotive industry. Yet, finding an optimized color sequence is a very challenging task, as a large number of exterior systems for car manufacturing need to be painted in a variety of different colors. Therefore, there is a strong need for efficient automated scheduling solutions in this area. Previously, exact and metaheuristic approaches for creating efficient paint shop schedules in the automotive supply industry have been proposed and evaluated on a publicly available set of real-life benchmark instances. However, optimal solutions are still unknown for many of the benchmark instances, and there is still a potential of reducing color change costs for large instances. In this paper, we propose a novel large neighborhood search approach for the paint shop scheduling problem. We introduce innovative exact and heuristic solution methods that are utilized within the large neighborhood search and show that our approach leads to improved results for large real-life problem instances compared to existing techniques. Furthermore, we provide previously unknown upper bounds for 14 benchmark instances using the proposed method.

Online Simulation Optimization Using Neutrosophic Cross-Efficiency DEA and Box–Behnken Experimental Design (A Case Study on the Automotive Paint Shop)

Paint shops are considered as bottlenecks in many automobile companies. As all processes in the paint shop are involved with chemical materials, time is really crucial in the production process, so offering instant remedial actions is crucial. This paper optimizes an online simulation (OS) model, using Discrete-Event Simulation (DES), applied to a paint shop in the automotive industry. To this aim, an integrated Box–Behnken design (BBD) and cross-efficiency data envelopment analysis (DEA) under a neutrosophic environment have been implemented. The former has generated cost-effective scenarios with the minimum number of experimental design, and the latter has provided the efficiency of each scenario enabling to obtain a unique weight for each decision-making unit (DMU) using aggressive and benevolent models as well as a general representation of the human perception toward risks arising from uncertain information, leading to determine the optimal scenario. The proposed approach has been implemented in an automotive industrial plant in Iran, and the results have shown that this approach, compared with previous studies, is a practical way for online monitoring and optimizing the paint shop.

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

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.