International Journal of Industrial Engineering & Production Research
Iran University of Science & Technology
Sun, Dec 22, 2024
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Volume 32, Issue 4 (IJIEPR 2021)
IJIEPR 2021, 32(4): 1-18 |
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Jafarian-Namin S, Fallahnezhad M S, Tavakkoli-Moghaddam R, Salmasnia A, Abooei M H. A Comparative Study on a Triple-Concept Model of Two Techniques for Monitoring the Mean of Stationary Processes. IJIEPR 2021; 32 (4) :1-18
URL: http://ijiepr.iust.ac.ir/article-1-1082-en.html
URL: http://ijiepr.iust.ac.ir/article-1-1082-en.html
Samrad Jafarian-Namin1 
, Mohammad saber Fallahnezhad 2, Reza Tavakkoli-Moghaddam3 , Ali Salmasnia4 , Mohammad Hossein Abooei1
, Mohammad saber Fallahnezhad 2, Reza Tavakkoli-Moghaddam3 , Ali Salmasnia4 , Mohammad Hossein Abooei1
1- Department of Industrial Engineering, Faculty of Engineering, Yazd University
2- Department of Industrial Engineering, Faculty of Engineering, Yazd University ,fallahnezhad@yazd.ac.ir
3- School of Industrial Engineering, College of Engineering, University of Tehran
4- Department of Industrial Engineering, Faculty of Technology and Engineering, University of Qom
2- Department of Industrial Engineering, Faculty of Engineering, Yazd University ,
3- School of Industrial Engineering, College of Engineering, University of Tehran
4- Department of Industrial Engineering, Faculty of Technology and Engineering, University of Qom
Abstract: (2713 Views)
In recent years, it has been proven that integrating statistical process control, maintenance policy, and production can bring more benefits for the entire production systems. In the literature of triple-concept integrated models, it has generally been assumed that the observations are independent. However, the existence of correlated structures in some practical applications put the traditional control charts in trouble. The mixed EWMA-CUSUM (MEC) control chart and the ARMA control chart are effective tools to monitor the mean of autocorrelated processes. This paper proposes an integrated model subject to some constraints for determining the decision variables of triple concepts in the presence of autocorrelated data. Three types of autocorrelated processes are investigated to study their effects on the results. Moreover, the results of the MEC and ARMA charts are compared. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to select optimal decision variables. An industrial example and extensive comparisons are provided
Keywords: Statistical process control, Production, Maintenance policy, Autocorrelated process, Meta-heuristic algorithm
Type of Study: Research |
Subject:
Statistical Process Control Statistical Process Control or Quality Control
Received: 2020/06/5 | Accepted: 2021/11/2 | Published: 2021/12/7
Received: 2020/06/5 | Accepted: 2021/11/2 | Published: 2021/12/7
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