Integration of production–maintenance planning and monitoring simple linear profiles via Hotelling's T2 control chart and particle swarm optimization

• Published in: Computers & industrial engineering 2024-02, Vol.188, p.109864, Article 109864
• Main Authors: Shojaee, Mohsen, Noori, Siamak, Jafarian-Namin, Samrad, Johannssen, Arne
• Format: Article
• Language: English
• Subjects: Economic production; Integrated model; Maintenance policy; Particle swarm optimization; Profile monitoring
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2023.109864

•Developing an integrated model considering quality control, production cost, and maintenance policies.•Joint consideration of production and maintenance costs in profile monitoring.•Implementing particle swarm optimization to determine decision variables.•Conducting sensitivity analysis on the impact of input parameters on the optimal solutions.•Evaluating the effectiveness of the proposed model by comprehensive comparative analysis. This paper introduces an integrated model that merges three key concepts: quality control through linear profile monitoring using Hotelling's T2 control chart, production cost evaluation, and maintenance policies. The objective function of the integrated model is to minimize total costs. This study is the first to jointly consider production and maintenance costs in the framework of profile monitoring using Hotelling’s T2 control chart for linear profiles without compromising on the versatility of simple linear profiles as a modeling tool, particularly in calibration applications. Appropriate assumptions are made for the proposed integrated model and the particle swarm optimization algorithm is implemented to determine decision variables. To evaluate the effectiveness of the integrated model, a numerical example and a comprehensive comparative analysis are conducted. Moreover, a sensitivity analysis to study the effects of various input parameters on the optimal solutions is performed. The findings suggest that adopting the integrated model with specific decision variables results in lower expected total costs when compared to the joint model and fixed parameter schemes.