Journal of Quality Engineering and Management

Abstract Control chart is one of the most widely used statistical control tools of processes that plays an important role in improving their quality. One of the weaknesses of its low speed control diagrams is the detection of changes in the process parameter. To this end, adaptive control charts have been developed to improve the performance of control charts to detect small changes. In this paper, the adaptive method of variable sampling distance is used to improve the performance of the control chart of the maximum exponentially balanced moving average squared-exponential moving average squared deviation to detect changes in mean and variance and simultaneous changes in mean and variance. The performance of the proposed method is compared with the sampling control diagram at fixed intervals using simulations and the criteria of mean time to alert and moderate time to alarm occurrence. The results show that the proposed method performs better than the control chart results in the research literature for large changes.

Abstract Control chart is one of the basic tools of statistical quality control and monitoring during production of production and service processes. The cost of quality in the classical approach to control chart design depends on whether the quality characteristic is inside or outside the control range. Since the integration of the loss function approach into in-production monitoring activities such as control charts, which is derived from Taguchi's concept of social quality loss, in which the cost of quality depends on the amount of deviation of the quality characteristic from the target value, is a more comprehensive evaluation process. It is better guided in the management strategy. This paper combines the Taguchi loss function and the economic design of the X نمودار control chart. In addition, since the output data of the process may not follow the normal distribution or the assumptions of the central limit theorem may not be true of it, it is necessary to study the integrated model in these situations, in addition to the normal distribution mode. In this regard, the economic design parameters of the integrated model will be compared in the presence of normal and abnormal data, in which, due to the extent of incremental failure rate in production systems, from non-uniform sampling design for sample inspection and Weibull shock model for failure mechanism. The process is used. 

Abstract Widespread environmental degradation and increasing emissions of greenhouse gases have raised environmental concerns among communities and governments. One of the ways to reduce environmental pollution is to implement a green supply chain. In this study, the selection of green suppliers of biomass power plant equipment and how to allocate demand to them as one of the key strategic decisions of the supply chain has been investigated. Due to the multiplicity of criteria in this issue, using pairwise comparison methods for weighting will not be effective. In the proposed integrated model, the principal component analysis method is used to meet this challenge. Finally, using a multi-objective mathematical planning model, the amount of demand allocation to each supplier is determined. The proposed method was used to select green suppliers of Shiraz biomass power plant equipment. The results show the efficiency of the proposed model for selecting green suppliers of biomass power plant equipment.

Abstract In some Problems, quality of a process or a product is described by a relationship between one or more response variables and one or more explanatory variables. In the case that the response variables are correlated, quality of a process or a product can be characterized by a multivariate Profile. In some situations, the values of response variables in a Profile are auto-correlated due to the time collapse between two successive samples. The autocorrelation affects the regression parameters estimates and as a result, the performance of control charts in detecting shifts deteriorates. In this paper, an auto-correlated multivariate simple linear profile is considered and assumed that the autocorrelation can be described by AR (1) and MA(1) models. Then, two control charts for monitoring the multivariate simple linear profiles are suggested. The Performance of the proposed control charts are compared in Phase I by using simulation in terms of Power criterion.

Abstract Purpose: This study aims to improve diesel engine reliability estimation by using a risk-based model that incorporates key environmental factors, especially wear particles in engine oil, for more accurate analysis than traditional time-based methods.
Methodology: The Proportional Hazards Model (PHM) was used to assess engine reliability based on wear particles in oil. The Harrell and Lee test checked model assumptions, and the Wald test validated coefficients. Reliability was then compared across two engine groups under different conditions.
Findings: The study's results showed that incorporating risk factors, such as the level of wear particles in engine oil, increases the accuracy of reliability estimation for diesel engines. Specifically, it was found that engine age, maintenance status, and operational conditions significantly impact reliability, such that worn-out engines reach lower levels of reliability more quickly. The proposed model, by providing a more precise analysis, can serve as an effective tool for optimizing maintenance scheduling and preventing unexpected failures in industrial systems.
Originality/Value: This research's primary distinction lies in the integration of qualitative data related to the internal condition of the engine (wear particles in oil) with advanced statistical models of PHM, which has been less addressed in previous studies. This approach, by creating a link between condition-based data analysis and reliability analysis, opens new horizons for condition-based maintenance planning.

Abstract In helicopter design, determining the geometric shape of the fuselage is one of the main and primary issues that affect the performance characteristics of the helicopter. The aerodynamic coefficients of the helicopter fuselage are the main criteria for determining the quality and appropriateness of the geometric shape of the helicopter fuselage. Optimal design of helicopter body geometry is a complex activity and it is necessary to determine the effects of different parameters of helicopter geometry on aerodynamic coefficients. In this paper, design of computer experiments based on simulation of computational fluid dynamics to study the effects of the main parameters of the helicopter body geometry, such as ratio of largest helicopter body width to helicopter length, ratio of largest helicopter body height to helicopter length and nose to radius of curvature radius ratio The fuselage is based on the aerodynamic coefficients of drag, lift and torsional torque. The experiments are based on Taguchi's orthogonal array L25 (53). To determine the relationship between aerodynamic coefficients and parameters of the geometric shape of the helicopter fuselage and the importance of each parameter in aerodynamic coefficients, three-dimensional procedure diagrams, signal to noise ratios, mean of main effects, response procedure methodology and analysis of variance were used. Also, mathematical models were developed to estimate the aerodynamic coefficients of drag, lift and torsional torque through the response procedure methodology. The results at 95% confidence level show that the most effective parameter in the value of the helicopter body drag coefficient is the ratio of the largest height of the helicopter body to the length of the helicopter and in the lift and torsional torque parameters is the ratio of the largest width of the helicopter to the length of the helicopter.

Abstract The reliability assessment of multi-state systems is often investigated by assuming the independence of the components. It is very useful to consider the interactions between components to evaluate the reliability of such systems. The traditional block diagrams do not evaluate the reliability of a repairable multi-state system. The use of simple stochastic process methods is very difficult due to the high dimension of the problem (the very large number of system states) for engineering applications. To the best of our knowledge, the universal generating function has not been used for systems with dependent components. In this paper, using stochastic processes and a universal generating function method, multi-system systems are analyzed in a situation in which the performance distributions of some components depend on the state of others.

Abstract 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.

Abstract Today, the defense of sensitive areas and resources is a fundamental issue that encompasses all key infrastructures, and to achieve the goal of reducing the damage and injuries caused by the attacker, the use of informed and useful strategies is necessary. In this research, modeling is considered to optimize the protection of investment systems that have a series-parallel reliability structure and have a functional correlation with each other. In general, in this study, first considering the functional independence of subsystems, the probabilities of a successful attack, the system reliability structure and the game theory approach to finding the equilibrium point, a nonlinear programming model is proposed to determine the investment defense of systems. Then, by determining the reliability relationships despite the functional correlation between the subsystems, a linear programming model is introduced to determine the correlation coefficient of the subsystems and to redistribute investment to defend the systems. Finally, the proposed research model is used for a numerical example and the results are analyzed.

Abstract The available statistics and information about the free and special economic zones of the country show the fact that these zones, in comparison with the export processing zones of the world, have not been very successful in achieving the goals set for them. The statistical population of this research is 80 experts and experts on free and special economic zones, which are available from Aras and Mako free trade-industrial zones and Salmas special economic zone. In this study, the tool used to measure and measure the desired variables are two types of researcher-made questionnaires, one of which is used to use the Likert scale to assess the effect of each factor and the other questionnaire to compare Pairs have been used to prioritize factors. Using the theoretical foundations of six factors and quantitative and qualitative variables predicting the success or failure of investment projects to manage quality in free and special economic zones of the country to model with a multilayer neural network of perceptron, identify and after describing the variables and test Normality, using PLS software, confirmatory factor analysis of variables was performed, all of which had a good confirmatory factor analysis. Then, using linear regression and analysis of variance (ANOVA) test, the effect of each factor on the success or failure of investment projects was investigated. The results of this test showed the confirmation of the effect of each factor and finally the results of hierarchical analysis That is, product and service specifications came in first, and the rest of the factors came in second.

Abstract Abstract: In today's competitive world, considering customers' needs and having a proper understanding of them, as the first phase of the conceptual design and development in the life cycle of a system, ensures the success of economic institutions and determines the purpose of the system. In this context, all needs must be considered and correctly interpreted in a continuous interaction with the customer. The correct interpretation of these requirements will result in the creation of two categories of requirements, designated as functional and non-functional requirements. Addressing both of these requirements and their values as well as an eventual assessment of the designs under construction in agreement with these values in the early stages of the life cycle of a system will bring about a reduction in costs and create a customer- oriented system. The purpose of the present study that conducted on a submarine of class H, is to choice the effective conceptual design for design and construction the product using the calculation of the overall measure of effectiveness index and based on the requirements specified in the conceptual design stage. During this research, first, all needs were identified; being interpreted, they were classified into two categories of functional and non-functional requirements. Then, the quantitative values of functional requirements were calculated using experts’ judgments and the quantitative values of nonfunctional requirements were worked out using various methods on the basis of the processes, so, the paired comparison matrix was used to determine the weights of each of the requirements and the effectiveness measure of the proposed designs was determined on the basis of weight and quantitative values of each category of requirements for each the conceptual design. Finally, from the two conceptual designs considered, an effective conceptual design is introduced and introduced to the industry. 

Abstract Today, supply chain networks in a competitive business environment are faced with the occurrence of potential disruptions, uncertain nature of business parameters and constant changes in market demand that affect the efficiency and performance of the network. This research has developed a new stable-feasibility possibility combination for designing a multi-product closed-loop supply chain network under uncertainty conditions to develop a new approach to planning. Mathematics of credit limitation has been used. The above network has been designed with the objectives of maximizing accountability, reliability and cost minimization. . Reliable models based on credit constraint planning and new robust-credit constraint combination were presented and evaluated using real data from a national industrial project. The results show the proposed robust new combination with average cost-effectiveness and minimum standard deviation , Has improved the stability of the model and its effectiveness.

Abstract Abstract: In design of the supply chain, the use of returned products and their re-cycles in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network, determining the amount and location of facilities and planning of transportation in conditions of uncertainty of demand. So that: Maximize total profit of operation, Minimize Adverse environmental effects, Maximize customer & supplier service level. In order to deal with the uncertainty of the model, a scenariobased robust planning is used and to solve the model with the actual data of the case study in the steel industry, a meta-heuristic algorithm (NSGA-II) is utilized. The results of the model obtained from the actual data set and data validation indicate that the model can be integrated in optimizing the objectives and determining the amount and location of the necessary facilities in the steel industry. 

Abstract The reliability of manufactured products can vary according to changes in production quality. Field failure data provide useful information for assessing whether changes in reliability are significant or identifying the cause of changes. In order to identify these errors, we need to model the effect of these errors on product reliability. In this research, we intend to predict product reliability behavior based on the percentage of different quality errors with which products may be produced. In this regard, two types of quality errors, namely non-compliant items and assembly error are examined separately. In order to model, it is assumed that the percentage of qualitative errors follow the beta distribution and the failure times follow the Weibull distribution. Reliability, risk rate and probability chart of products are studied under these two types of qualitative errors. Based on the results of this research, it is possible to guess the type and percentage of quality errors with which products are produced.

Abstract Purpose: This study was conducted to investigate the relationships between Quality Management Practice (QMP), Industry 4.0 technologies, and organizational performance, and among them, the mediating role of customer satisfaction and technology was considered. The main objective of the study was to explain how the combination of quality and technology affects organizational performance improvement in Iranian manufacturing companies. Methodology: The study used a mixed approach, and the data were analyzed using structural equation modeling (PLS-SEM) and Artificial Neural Network (ANN). The statistical population comprised employees of Iranian manufacturing companies, and the data were collected via a valid questionnaire administered to 205 respondents. The research tool had five main variables, fourteen sub-components, and forty-five indicators. Findings: The results showed that QMP has a direct and significant effect on customer satisfaction and organizational performance. Also, Industry 4.0 technology and customer satisfaction played an effective mediating role in these relationships. Neural network analysis also indicates that customer satisfaction, process management, and data-centricity are most important for predicting organizational performance. The findings have collectively confirmed that combining QMP with new technologies can be an efficient strategy for improving organizational performance. Originality/Value: By combining the two methods, PLS and ANN, this research has presented an innovative approach for simultaneous analysis of causal relationships and nonlinear prediction. Also, by simultaneously examining the two mediating variables of customer satisfaction and technology and conducting the research in the local context of Iranian companies, it has covered the existing research gap and contributed to the development of the literature on quality management and digital transformation.

Abstract Control charts are widely used to identify changes in the production process, and when it comes to identifying small changes in the process, non-Shewhart control charts such as exponential weighted moving average (EWMA) and generalized weighted moving average (GWMA) are better alternatives to control charts than Shewhart ¯X control chart. In this context, nonparametric control charts are used when the distribution of quality characteristic of the process is unknown, in which the Sign control chart is one of the most popular nonparametric control charts. In this paper, for the first time, a generalized weighted moving average sign control chart using a ranked set sampling (RSS) design is introduced. The performance of the proposed control chart is evaluated using simulated data according to the average run length evaluation criterion, and simulation studies showed that the GWMA sign control chart under the RSS design is better at detecting small process changes than the EWMA sign control chart under the RSS design.

Abstract Abstract: Nowadays, extensive use of computers, networks and the internet in information systems has increased the diversity of information security risks so the management of these risks has been increasingly considered. According to the importance of information security in online research information systems as the main source of future researches, this study applies a hybrid of fuzzy Failure Mode and Effects Analysis (FMEA), analytic hierarchy process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), attempts to optimizely identify, assess and prioritize organization's information security risks. By using fuzzy logic ratings will be more accurate and more transparent, and with the combination of AHP and TOPSIS can measure the weight of the criteria of FMEA and with the calculation closeness coefficient of any risk prioritized each of them. The result to study of application this model in identifying and assessing potential risks of system studied in three main areas: confidentiality, availability and integrity of information, shows risks related to unauthorized access to information and incorrect and lack of integrated information are in the highest priority of the organization's experts.

Abstract In this paper, control diagrams are proposed to monitor the scale parameter of reliability data with Weibull distribution in the presence of type 2 censorship in cascading processes. A cumulative sum control diagram and a probability range control diagram are intended to detect decreasing shifts in the mean of the qualitative characteristic with the reliability fluid. The proposed control approaches are based on the distribution of the smallest limit value converted from the Weibull distribution to take into account the cascading property that is the main feature of multistage processes. Then, to evaluate the proposed control charts, a simulation is performed in which the comparison index of control charts is the average length of the sequence. An additional quadratic loss index has also been used to compare the ability to detect proposed control charts. In addition, sensitivity analysis has been studied to investigate the effect of the number of failures on the performance of the proposed control diagrams and the robustness of the monitoring approaches versus shifts in the previous stage of the process. Finally, to illustrate the performance of control charts, a case study from a glass bottle factory is reviewed. The results show the superiority of the cumulative control chart over the control chart with probability limits.

Abstract Multi-response optimization performed by the response procedure method is very common. Before optimization, we need to select and fit the appropriate model for each response. A major problem that may occur due to incorrect fitting of models and failure to reach optimal solutions is model misidentification. The solid regression model method, which is a semi-parametric method for estimating d, can have a better performance than both parametric and non-parametric estimation methods against model misclassification. In this research, the use of a robust regression model method is proposed to improve the model estimation and the appropriate fit of each of the answers will be investigated by one of the multivariate optimization methods, namely the utility function. In the following, an applied study is presented to compare parametric, nonparametric and semi-parametric methods. The results of this study show that the performance of the stable regression model is more appropriate in many situations as well as in the modeling stage than the other two methods. Therefore, the optimization results with a stable regression model are much more reliable.

Abstract Purpose: In today's world, the role of the economy in shaping business models and the power of nations is highly significant. Exports play a vital role in enhancing productivity and economic development, particularly in developing countries. The automotive industry, as a key sector, contributes considerably to this process. This study aims to identify the key influencing variables on non-oil exports and explore how they affect productivity growth and export improvement.
Methodology: This research employs a system dynamics approach to model the interactions among key economic variables. The study utilizes VENSIM software to simulate the system dynamics model of the automotive industry and non-oil exports. Causal loop diagrams and stock and flow diagrams were developed to analyze the relationships.
Findings: The main variables analyzed in this study include exchange rate, inflation, productivity, and competitiveness. The developed model was validated and tested under various scenarios. Results indicate how changes in these variables impact productivity and the performance of non-oil exports in the automotive industry.
Originality/Value: This study offers a dynamic model tailored to the automotive sector in developing economies like Iran, where over-reliance on oil has led to inefficiencies in other export sectors. The model helps policymakers and industry stakeholders understand complex interactions and make informed decisions to boost non-oil exports and overall productivity.

Abstract Abstract Control charts are used in process monitoring to identify any changes that may affect process quality. In many cases, it is assumed that the process data has a normal distribution, which may not be the case in practice. In this paper, we examine the economic statistical design of the   X ̅ control diagram when the qualitative characteristic distribution is not normal with the Markov chain approach. In this regard, we use the distribution as a model for the variable distribution of process quality. Due to the flexibility of its components, this distribution can model many distributions, including the normal distribution. We also show the design performance by analyzing the sensitivity of process parameters and based on the values ​​of skewness and elongation of the community, using genetic algorithm, for industrial application. 

Abstract Guarantee costs in production companies are very important and can have a great impact on the profit of the company. Putting effort into reducing these costs may result in profit increase. In this regard, detecting reliability related defects before their occurrence can be useful in reducing guarantee costs and also customer dissatisfaction. Reliability problems may be due to manufacturing defects. Removing defective items during production process can prevent high guarantee and customer dissatisfaction costs. In this paper a model is developed for early detection of reliability defects by means of guarantee data and qualitative parameters of the production line. A case study on TU5 engines manufactured by Irankhodro Company is also included.

Abstract 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.

Abstract Applying preventive maintenance strategies in control chart design, which is one of the main tools in statistical process quality control, not only increases the reliability of the system and reduces its wear and tear, but also reduces the cost of chart design. However, the cost of quality in the classical approach to control chart design, derived from the Crossby football gate philosophy, depends only on whether the quality characteristic is within or outside the control limits. In contrast, using the loss function approach in in-production monitoring activities such as control charts, where quality cost based on Taguchi's concept of social loss quality depends on the amount of quality characteristic deviation from the target value, is a more comprehensive assessment of the process and therefore decisions. It is better guided in planning and management. This paper presents an integrated model of Taguchi loss function, preventive maintenance strategies including incomplete maintenance and early replacement, and economic control diagram design in which the shock model or process failure mechanism has an incremental failure rate. In addition, due to the fact that the data obtained from the measurements related to the output of the production process may not follow the normal distribution or the assumptions of the central limit theorem may not be true for them, it is necessary to study the integrated model in these situations. Normal distribution is necessary. To illustrate the issue, numerical examples based on non-uniform sampling design and Weibull shock model are provided. The adjustment parameters obtained from the integrated model (sample size, sampling intervals, coefficient width of control limits, early replacement time, and cost level and incomplete maintenance time) in addition to significant differences in normal and abnormal conditions, show that with increasing preventive maintenance level, The average design cost will be reduced in both normal and abnormal modes.

Abstract This paper examines the issue of reliability and multi-objective redundancy allocation for series-parallel multi-state systems. Therefore, a suitable mathematical model is proposed in order to maximize the accessibility of the system and minimize the relevant design costs by considering the budget constraints and the physical weight of the system. In order to estimate the accessibility of a multi-state system, the general generator function method has been used, which is an efficient method for calculating the reliability and accessibility of multi-state systems. After solving the mathematical model by the Epsilon constraint method, in order to simultaneously optimize the two objective functions and generate partial solutions of the mathematical model of the problem on a larger scale, the second version of the genetic metaheuristic algorithm with unfavorable sorting has been developed. Finally, to evaluate the performance of the proposed solution algorithm, a number of sample problems in different dimensions have been generated and solved. The results of the meta-heuristic algorithm are compared with the results obtained from solving the mathematical model by the Epsilon constraint method by t-test, which indicates the efficiency of the proposed solution algorithm.