Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 14 2 2024 08 22 Presenting a proposed model to identify and reduce the dimensions of variables affecting the quality of slabs with a multi-variable-multi-stage approach (Case study: Isfahan Mobarakeh steel company) Presenting a proposed model to identify and reduce the dimensions of variables affecting the quality of slabs with a multi-variable-multi-stage approach (Case study: Isfahan Mobarakeh steel company) 91 104 215016 10.48313/jqem.2024.215016 FA Mehdi Karbasian Department of Industrial Engineering, Faculty of Industry, Malek Ashtar University of Technology, Isfahan, Iran. 0000-0001-9132-5078 Mahsa Jafari Department of Industrial Engineering, Faculty of Industry, Malek Ashtar University of Technology, Isfahan, Iran. 0009-0008-3556-8006 Sadegh Shahbazi Department of Industrial Engineering, Faculty of Industry, Malek Ashtar University of Technology, Isfahan, Iran. 0000-0001-6790-0917 Journal Article 2024 01 07 Purpose: Multivariate and multi-state processes refer to types of processes that involve a large number of variables at each production stage, which may be interrelated. The objective of this study is to propose a novel approach for selecting, reducing, and defining new control variables in complex manufacturing processes, enabling more effective and efficient quality control. Methodology: This study employs an applied, descriptive research methodology. Machine learning techniques and dimensionality reduction methods, such as Principal Component Analysis (PCA), are utilized, along with regression and correlation analysis. To evaluate the proposed method, a case study was conducted using real production data from the slab manufacturing process at Mobarakeh Steel Company in Isfahan. Findings: The slab production process consisted of three main stages: furnace, secondary metallurgy, and casting. In each stage, the proposed method was applied to reduce the number of control variables. For instance, in the furnace unit, nine initial variables were grouped into three clusters, and correlation and PCA were applied within each group. Key variables were extracted, and experts validated the results. The findings indicated that this approach effectively reduces the number of quality-related variables. Originality/Value: The novelty of this research lies in integrating machine learning and dimensionality reduction techniques to optimize quality control in multistage, multivariate processes. This method provides an effective tool for quality engineers and process analysts, particularly when traditional methods prove ineffective. Purpose: Multivariate and multi-state processes refer to types of processes that involve a large number of variables at each production stage, which may be interrelated. The objective of this study is to propose a novel approach for selecting, reducing, and defining new control variables in complex manufacturing processes, enabling more effective and efficient quality control. Methodology: This study employs an applied, descriptive research methodology. Machine learning techniques and dimensionality reduction methods, such as Principal Component Analysis (PCA), are utilized, along with regression and correlation analysis. To evaluate the proposed method, a case study was conducted using real production data from the slab manufacturing process at Mobarakeh Steel Company in Isfahan. Findings: The slab production process consisted of three main stages: furnace, secondary metallurgy, and casting. In each stage, the proposed method was applied to reduce the number of control variables. For instance, in the furnace unit, nine initial variables were grouped into three clusters, and correlation and PCA were applied within each group. Key variables were extracted, and experts validated the results. The findings indicated that this approach effectively reduces the number of quality-related variables. Originality/Value: The novelty of this research lies in integrating machine learning and dimensionality reduction techniques to optimize quality control in multistage, multivariate processes. This method provides an effective tool for quality engineers and process analysts, particularly when traditional methods prove ineffective.

https://www.pqprc.ir/article_215016_e434ddd048d0c963bfdb3db316f7dab4.pdf

Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 14 2 2024 08 22 Presenting a fuzzy mathematical programming model for allocating and scheduling parts in a flexible manufacturing system (FMS) and the impact of repairs and maintenance on product quality Presenting a fuzzy mathematical programming model for allocating and scheduling parts in a flexible manufacturing system (FMS) and the impact of repairs and maintenance on product quality 105 126 215017 10.48313/jqem.2024.215017 FA Jafar Hassan Beigi Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran. Meghdad Jahromi Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran. Mohammad Taghipour Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran. 0000-0003-3720-3795 Journal Article 2023 12 20 Purpose: This study aims to develop a mathematical model for flexible job shop scheduling. The primary focus is on optimizing three objectives: the makespan, the maximum machine workload, and the total workload. The ultimate goal is to enhance productivity and flexibility in manufacturing systems. Methodology: Two metaheuristic algorithms, NSGA-II and MOGWO, were used to solve the model. The model was first validated on a small scale, and then a sensitivity analysis was conducted on larger instances. The performance of the algorithms was compared based on accuracy and solution quality metrics. Findings: The results indicate that MOGWO performs better on medium-sized problems, whereas in large-scale cases, the difference between the two algorithms is not significant. The highest sensitivity was observed among the objectives regarding production and maintenance costs. Additionally, a resource-allocation pattern and an optimal sequence of operations were derived. Originality/Value: The originality of this research lies in developing and applying a multi-objective mathematical model for flexible job-shop scheduling that considers real-world constraints, including costs and resource limitations. The simultaneous use and detailed comparison of NSGA-II and MOGWO across different problem sizes is another contribution. Furthermore, the proposed operational pattern improves the applicability of the results in industrial environments. Purpose: This study aims to develop a mathematical model for flexible job shop scheduling. The primary focus is on optimizing three objectives: the makespan, the maximum machine workload, and the total workload. The ultimate goal is to enhance productivity and flexibility in manufacturing systems. Methodology: Two metaheuristic algorithms, NSGA-II and MOGWO, were used to solve the model. The model was first validated on a small scale, and then a sensitivity analysis was conducted on larger instances. The performance of the algorithms was compared based on accuracy and solution quality metrics. Findings: The results indicate that MOGWO performs better on medium-sized problems, whereas in large-scale cases, the difference between the two algorithms is not significant. The highest sensitivity was observed among the objectives regarding production and maintenance costs. Additionally, a resource-allocation pattern and an optimal sequence of operations were derived. Originality/Value: The originality of this research lies in developing and applying a multi-objective mathematical model for flexible job-shop scheduling that considers real-world constraints, including costs and resource limitations. The simultaneous use and detailed comparison of NSGA-II and MOGWO across different problem sizes is another contribution. Furthermore, the proposed operational pattern improves the applicability of the results in industrial environments.

https://www.pqprc.ir/article_215017_b5030d8ce7485f84e7c659e85f1f68d3.pdf

Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 14 2 2024 08 22 Realistic economic-statistical design of control chart based on the Lorenzen and Vance model in the presence of independent multiple assignable causes under the burr-XII shock model Realistic economic-statistical design of control chart based on the Lorenzen and Vance model in the presence of independent multiple assignable causes under the burr-XII shock model 127 144 214758 10.48313/jqem.2024.214758 FA Farnoosh Shiravani Department of Statistics, Faculty of Statistics, Mathematics and Computer Science, Allameh Tabatabaei University, Tehran, Iran. Mohammad Bamanimoghadam Department of Statistics, Faculty of Statistics, Mathematics and Computer Science, Allameh Tabatabaei University, Tehran, Iran. 0000-0003-1324-8024 Reza Pourtaheri Department of Statistics, Faculty of Statistics, Mathematics and Computer Science, Allameh Tabatabaei University, Tehran, Iran. Journal Article 2023 07 15 Purpose: The main goal of this study is to propose a realistic and practical model for the economic-statistical design of control charts in the presence of multiple independent assignable causes under the Burr Type XII shock model. The model aims to minimize the underestimation of the actual cost per unit time of the quality cycle. Methodology: This research utilizes the Burr Type XII distribution as a shock model to develop the RED model for optimal design of control charts. The Lorenz and Van cost function is also extended to account for multiple assignable causes, and a numerical example is provided to demonstrate the solution approach. Findings: Numerical results reveal that the proposed model outperforms existing models in accurately estimating the real cost per unit time of the quality cycle. Furthermore, an increase in the shock probability leads to a non-decreasing trend in the average cost, underscoring the importance of accounting for this probability in E(A) calculations. Originality/Value: This is the first study to employ the Burr Type XII distribution as a shock model in the economic-statistical design of control charts. By extending existing cost models, the paper introduces a novel and realistic approach to designing control charts in the presence of multiple independent shocks. Purpose: The main goal of this study is to propose a realistic and practical model for the economic-statistical design of control charts in the presence of multiple independent assignable causes under the Burr Type XII shock model. The model aims to minimize the underestimation of the actual cost per unit time of the quality cycle. Methodology: This research utilizes the Burr Type XII distribution as a shock model to develop the RED model for optimal design of control charts. The Lorenz and Van cost function is also extended to account for multiple assignable causes, and a numerical example is provided to demonstrate the solution approach. Findings: Numerical results reveal that the proposed model outperforms existing models in accurately estimating the real cost per unit time of the quality cycle. Furthermore, an increase in the shock probability leads to a non-decreasing trend in the average cost, underscoring the importance of accounting for this probability in E(A) calculations. Originality/Value: This is the first study to employ the Burr Type XII distribution as a shock model in the economic-statistical design of control charts. By extending existing cost models, the paper introduces a novel and realistic approach to designing control charts in the presence of multiple independent shocks.

https://www.pqprc.ir/article_214758_cf480b92152b599bc2bb4e9be74e2764.pdf

Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 14 2 2024 08 22 Analysis and calculation of Iran's composite quality index using the Shannon entropy approach Analysis and calculation of Iran's composite quality index using the Shannon entropy approach 145 161 218908 10.48313/jqem.2024.218908 FA Zahra Pourkhaghan Shahrezaii Economic expert and project feasibility studies, Iran Quality and Standard Inspection Company, Tehran, Iran. 0009-0005-4041-7067 Journal Article 2024 01 27 Purpose: This study aims to provide a comprehensive and accurate assessment of Iran's overall quality status in 2023 across six key dimensions: economy, technology, infrastructure, education, healthcare, and environment. The research also seeks to determine Iran's global ranking and offer insights for future improvement. Methodology: Key indicators within each dimension were analyzed and weighted using Shannon entropy. The six individual quality indices were then aggregated to construct a single, unified national quality index. Findings: Iran's overall quality index was estimated at 0.449, below the global average of 0.53, ranking 72nd out of 130 countries. While Iran performs close to the worldwide average in most dimensions, its infrastructure index falls significantly short. Originality/Value: This study is among the first to apply a multidimensional index of national quality constructed using Shannon entropy. It provides a quantitative and practical framework that highlights Iran's strengths and weaknesses, supporting evidence-based policy planning. Purpose: This study aims to provide a comprehensive and accurate assessment of Iran's overall quality status in 2023 across six key dimensions: economy, technology, infrastructure, education, healthcare, and environment. The research also seeks to determine Iran's global ranking and offer insights for future improvement. Methodology: Key indicators within each dimension were analyzed and weighted using Shannon entropy. The six individual quality indices were then aggregated to construct a single, unified national quality index. Findings: Iran's overall quality index was estimated at 0.449, below the global average of 0.53, ranking 72nd out of 130 countries. While Iran performs close to the worldwide average in most dimensions, its infrastructure index falls significantly short. Originality/Value: This study is among the first to apply a multidimensional index of national quality constructed using Shannon entropy. It provides a quantitative and practical framework that highlights Iran's strengths and weaknesses, supporting evidence-based policy planning.

https://www.pqprc.ir/article_218908_05a8f43f00efe3b376083d3b2805c60b.pdf

Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 14 2 2024 08 22 Statistical-economic design of control charts RNLMVSIT2 Statistical-economic design of control charts RNLMVSIT2 162 176 214759 10.48313/jqem.2024.214759 FA Asghar Seif Department of Statistics, Faculty of Basic Sciences, Bu-Ali Sina University, Hamedan, Iran. 0000-0002-7044-3240 Mitra Abdolmohammadi Department of Statistics, Science and Research Branch, Islamic Azad University, Tehran, Iran. Journal Article 2023 10 10 Purpose: In many industrial processes, there are situations where simultaneous monitoring and control of two or more dependent variables are necessary. In such cases, univariate control of quality characteristics can be misleading when considered independently. In the classical approach, when a quality characteristic falls outside the specified technical limits, the quality loss is regarded as a cost. All products within the technical limits of the quality characteristic are assumed to have similar quality, regardless of the deviation of the quality characteristic from its target value. However, it is essential to distinguish between products that fall within the technical limits of the quality characteristic, as any deviation from the target value incurs a proportional loss. Methodology: This paper introduces, for the first time in the literature, a reflected normal loss function to determine the average cost of producing non-conforming products when two quality characteristics are evaluated. In summary, this study focuses on the statistical-economic design of a multivariate T²-Hotelling control chart with multivariate variable sampling intervals in the presence of a reflected normal multivariate loss function (RNLMVSIT²). Additionally, a sensitivity analysis is conducted to examine the effects of time and cost parameters on the design parameters and the average cost. Findings: The results demonstrate the satisfactory performance of the proposed models. Originality/Value: This paper introduces, for the first time in the literature, a reflected normal loss function to determine the average cost of producing non-conforming products when two quality characteristics are evaluated. Purpose: In many industrial processes, there are situations where simultaneous monitoring and control of two or more dependent variables are necessary. In such cases, univariate control of quality characteristics can be misleading when considered independently. In the classical approach, when a quality characteristic falls outside the specified technical limits, the quality loss is regarded as a cost. All products within the technical limits of the quality characteristic are assumed to have similar quality, regardless of the deviation of the quality characteristic from its target value. However, it is essential to distinguish between products that fall within the technical limits of the quality characteristic, as any deviation from the target value incurs a proportional loss. Methodology: This paper introduces, for the first time in the literature, a reflected normal loss function to determine the average cost of producing non-conforming products when two quality characteristics are evaluated. In summary, this study focuses on the statistical-economic design of a multivariate T²-Hotelling control chart with multivariate variable sampling intervals in the presence of a reflected normal multivariate loss function (RNLMVSIT²). Additionally, a sensitivity analysis is conducted to examine the effects of time and cost parameters on the design parameters and the average cost. Findings: The results demonstrate the satisfactory performance of the proposed models. Originality/Value: This paper introduces, for the first time in the literature, a reflected normal loss function to determine the average cost of producing non-conforming products when two quality characteristics are evaluated.

https://www.pqprc.ir/article_214759_512e7ff1578297f2052f8062bf7b9bc4.pdf

Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 14 2 2024 08 22 Bayesian inference of the parameters under the generalized power Lindley distribution based on the hybrid type-II censoring scheme: a simulation study and application Bayesian inference of the parameters under the generalized power Lindley distribution based on the hybrid type-II censoring scheme: a simulation study and application 177 198 218909 10.48313/jqem.2024.218909 FA Nassrin Baloch Roodbary Department of Statistics, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran. Iman Makhdoom Department of Statistics, Payam Noor University, Tehran, Iran. 0000-0002-2768-1024 Journal Article 2023 11 13 Purpose: In this paper, we examine the Bayesian inference of the parameters of the generalized power Lindley distribution in the presence of type two hybrid censored data. Methodology: To estimate the maximum likelihood of the parameters, given that the estimates cannot be obtained implicitly and do not have a closed-form solution, we employ the EM algorithm and use the Fisher information matrix to construct asymptotic confidence intervals. Additionally, when estimating the parameters of the generalized power Lindley distribution, which we denote EPL throughout the article, we employ two Lindley approximation methods and Markov chain Monte Carlo under the squared-error loss function. We obtain HPD confidence intervals according to Bayesian estimates. Then we compare two Bayesian methods using simulation studies. Findings: The Monte Carlo method for the three-parameter distribution shows less bias and greater consistency than the Bayesian parameter estimates derived from the Lindley approximation. In high-dimensional distributions, the MCMC method yields more accurate forecasts than the Lindley approximation, and convergence occurs more rapidly. The MSE estimates from the Lindley approximation, as shown in Table 2, are significantly larger than the data dispersion for a similar sample size from the MCMC method, as presented in Table 3. We also provide an example of real data. Originality/Value: Given that no study has been conducted so far on the generalized Lindley power distribution in the presence of censored hybrid type II, the findings of this study can be used for future studies. Purpose: In this paper, we examine the Bayesian inference of the parameters of the generalized power Lindley distribution in the presence of type two hybrid censored data. Methodology: To estimate the maximum likelihood of the parameters, given that the estimates cannot be obtained implicitly and do not have a closed-form solution, we employ the EM algorithm and use the Fisher information matrix to construct asymptotic confidence intervals. Additionally, when estimating the parameters of the generalized power Lindley distribution, which we denote EPL throughout the article, we employ two Lindley approximation methods and Markov chain Monte Carlo under the squared-error loss function. We obtain HPD confidence intervals according to Bayesian estimates. Then we compare two Bayesian methods using simulation studies. Findings: The Monte Carlo method for the three-parameter distribution shows less bias and greater consistency than the Bayesian parameter estimates derived from the Lindley approximation. In high-dimensional distributions, the MCMC method yields more accurate forecasts than the Lindley approximation, and convergence occurs more rapidly. The MSE estimates from the Lindley approximation, as shown in Table 2, are significantly larger than the data dispersion for a similar sample size from the MCMC method, as presented in Table 3. We also provide an example of real data. Originality/Value: Given that no study has been conducted so far on the generalized Lindley power distribution in the presence of censored hybrid type II, the findings of this study can be used for future studies.

https://www.pqprc.ir/article_218909_959fc743169297a656ad68999750660c.pdf