Morvarid Derakhshan AndishehJournal of Quality Engineering and Management3134-08496120160420Combining Taguchi loss function and economic design of X ̅ control diagrams in the presence of normal and abnormal dataCombining Taguchi loss function and economic design of X ̅ control diagrams in the presence of normal and abnormal data1855874FAMohammadBamenimoghadamAllameh Tabatabaei University0000-0003-1324-8024MojtabaAghajanpour PashaStatistics, Mathematics and Statistics, Allameh Tabatabai University, Tehran, IranShabnamFaniStatistics, Mathematics and Statistics, Allameh Tabatabai University, Tehran, IranJournal Article20180120Control 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. <br /><br />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. <br /><br />https://www.pqprc.ir/article_55874_1dcee2628d726541e45f1ba8c31afe98.pdfMorvarid Derakhshan AndishehJournal of Quality Engineering and Management3134-08496120160420Presenting new control approaches for monitoring quality characteristics with Weibull distribution under type 2 censorship in a two-step processPresenting new control approaches for monitoring quality characteristics with Weibull distribution under type 2 censorship in a two-step process92055875FAShervinAsadzadehFaculty member of North Azad UniversityFatemehKiadaliryIndustrial Engineering, Technical and Engineering, Islamic Azad University, North Tehran Branch, Tehran, IranJournal Article20180120In 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.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.https://www.pqprc.ir/article_55875_afb388d6405dcf9258c5d53ccae88610.pdfMorvarid Derakhshan AndishehJournal of Quality Engineering and Management3134-08496120160420Estimation of the point of change in the multivariate normal process covariance matrix using neural networksEstimation of the point of change in the multivariate normal process covariance matrix using neural networks213455876FAAmirhossainAmiriIndustrial Engineering, Technical and Engineering, Shahed University, Tehran, Iran0000-0002-2385-8910MohammadrezaMalekiIndustrial Engineering, Technical and Engineering, Shahed University, Tehran, IranMohammadhossainKalaniIndustrial Engineering, Technical and Engineering, Shahed University, Tehran, IranJournal Article20180120In most cases, the alert received from a control chart does not indicate the actual time of the process change due to the delay between the actual change time and the time of receiving the alert from the control chart. As a result, it is necessary to examine the real time of change, which is referred to as the "point of change". By reviewing the literature on identifying real-time process changes, it can be concluded that most research in this field focuses on univariate processes and little research is devoted to multivariate processes. In addition, most research in the field of estimating change time in multivariate processes has focused on changes in the mean process vector, and only one research has been done on the covariance matrix. In this paper, a model based on artificial neural network is proposed to estimate the point of change in the covariance matrix of multivariate normal processes. The method presented in phase 2 is control diagrams and the type of change that occurred in the variance of qualitative characteristics is assumed to be the type of step changes. The performance of the proposed method in estimating the change point is evaluated based on two criteria of experimental distribution of estimates as well as the mean and standard deviation of the change point estimator for different step shifts in the variance of process variables in a simulation study. Finally, in order to further explain the proposed method, a numerical example is provided. The results show the proper performance of the proposed method in estimating the change point in the covariance matrix of multivariate normal processes.In most cases, the alert received from a control chart does not indicate the actual time of the process change due to the delay between the actual change time and the time of receiving the alert from the control chart. As a result, it is necessary to examine the real time of change, which is referred to as the "point of change". By reviewing the literature on identifying real-time process changes, it can be concluded that most research in this field focuses on univariate processes and little research is devoted to multivariate processes. In addition, most research in the field of estimating change time in multivariate processes has focused on changes in the mean process vector, and only one research has been done on the covariance matrix. In this paper, a model based on artificial neural network is proposed to estimate the point of change in the covariance matrix of multivariate normal processes. The method presented in phase 2 is control diagrams and the type of change that occurred in the variance of qualitative characteristics is assumed to be the type of step changes. The performance of the proposed method in estimating the change point is evaluated based on two criteria of experimental distribution of estimates as well as the mean and standard deviation of the change point estimator for different step shifts in the variance of process variables in a simulation study. Finally, in order to further explain the proposed method, a numerical example is provided. The results show the proper performance of the proposed method in estimating the change point in the covariance matrix of multivariate normal processes.https://www.pqprc.ir/article_55876_3e6c1d5ebd08633cea4ba168e51bcfe2.pdfMorvarid Derakhshan AndishehJournal of Quality Engineering and Management3134-08496120160420Replacing the sequence probability ratio test with the V mask in the cumulative summative control chartReplacing the sequence probability ratio test with the V mask in the cumulative summative control chart354455877FAAbbasParchamiBahonar University of KermanBahramSadrghpurgildehAssociate Professor, Ferdowsi University of Mashhad, Faculty of Mathematical Sciences, Department of StatisticsJournal Article20180120Cumulative cumulative control charts are presented in most quality control books regardless of the statistical formulas behind the V-mask. In this paper, after introducing and reviewing the cumulative sum control chart, the decision rule is examined and its relationship with sequence probability ratio tests. Many references and quality control books consider the V mask method to be equivalent to a cumulative test with inverted data. Although the two methods have many similarities, their decision-making rules are not the same. In order to further clarify, in this article, the similarities and differences between the two approaches are examined and compared.Cumulative cumulative control charts are presented in most quality control books regardless of the statistical formulas behind the V-mask. In this paper, after introducing and reviewing the cumulative sum control chart, the decision rule is examined and its relationship with sequence probability ratio tests. Many references and quality control books consider the V mask method to be equivalent to a cumulative test with inverted data. Although the two methods have many similarities, their decision-making rules are not the same. In order to further clarify, in this article, the similarities and differences between the two approaches are examined and compared.https://www.pqprc.ir/article_55877_6e650442ac3985085563c7d6e7eae501.pdfMorvarid Derakhshan AndishehJournal of Quality Engineering and Management3134-08496120160420Investigation of the effect of inertia on the performance of nonparametric signals of cumulative sum and rhythmic moving averageInvestigation of the effect of inertia on the performance of nonparametric signals of cumulative sum and rhythmic moving average455655878FAMaysamMadadiIndustrial Engineering, Technical and Engineering, Islamic Azad University, South Tehran Branch, Tehran, IranMajidNojavanFaculty of Industrial Engineering, South Tehran Branch, Islamic Azad University0000-0001-7163-5271Journal Article20180120In CUSUM and EWMA, a change in process may cause the statistic to move away from the center. In this case, if another change occurs in the opposite direction, the chart needs more time to be warned. This is called the inertia effect. In this paper, the effect of inertia on the performance of non-parametric CUSUM and EWMA sign diagrams is investigated and the two diagrams are compared. To do this, a simulation program is developed that calculates the average length of the sequence in the controlled state (ARL0) and out of control (ARL1) in these diagrams for different parameter values using three different distributions. The results of the simulation show that the values of ARL0 in the CUSUM symbol are not affected by inertia, but the values of ARL1 in the graph increase in large changes and the power of the graph in detecting these changes decreases. Also in the EWMA symbol chart, the ARL0 values decrease and the ARL1 values increase for medium and large changes. In other words, due to inertia in this chart, the number of false warnings increases and the power to detect changes decreases. Due to the less negative effect of inertia on the performance of the CUSUM signal diagram than EWMA, the use of this diagram in the control of abnormal processes is recommended.In CUSUM and EWMA, a change in process may cause the statistic to move away from the center. In this case, if another change occurs in the opposite direction, the chart needs more time to be warned. This is called the inertia effect. In this paper, the effect of inertia on the performance of non-parametric CUSUM and EWMA sign diagrams is investigated and the two diagrams are compared. To do this, a simulation program is developed that calculates the average length of the sequence in the controlled state (ARL0) and out of control (ARL1) in these diagrams for different parameter values using three different distributions. The results of the simulation show that the values of ARL0 in the CUSUM symbol are not affected by inertia, but the values of ARL1 in the graph increase in large changes and the power of the graph in detecting these changes decreases. Also in the EWMA symbol chart, the ARL0 values decrease and the ARL1 values increase for medium and large changes. In other words, due to inertia in this chart, the number of false warnings increases and the power to detect changes decreases. Due to the less negative effect of inertia on the performance of the CUSUM signal diagram than EWMA, the use of this diagram in the control of abnormal processes is recommended.https://www.pqprc.ir/article_55878_ee5bc46a2cac52138903481e84b14dda.pdfMorvarid Derakhshan AndishehJournal of Quality Engineering and Management3134-08496120160420Simulation of transportation costs Supply chain network design taking into account price and quality dependent demandSimulation of transportation costs Supply chain network design taking into account price and quality dependent demand576555879FASayed Mohammad MahdiKazemiIndustrial Engineering, Technical and Engineering, Islamic Azad University, Damavand Branch, Tehran, IranPaymanTakiIndustrial Engineering, Technical and Engineering, Islamic Azad University, Damavand Branch, Tehran, IranJournal Article20180120Proper network design has many effects on the performance, efficiency and effectiveness of supply chains in achieving the expected goals and meeting the needs of customers. In this research, a multi-level multi-objective model for supply chain network design considering pricing, product quality level and disruption is presented. The cost of transporting each vehicle is assumed to be a dynamic random function rather than a parameter. Therefore, discrete-event simulation has been used to estimate transportation costs. Due to the important role of risk and disruption concepts in supply chain network design, risk minimization along with profit maximization according to pricing and quality concepts have been defined as objective functions. Supply chain demand is considered a linear function of price and quality level of products. Finally, the supply chain network design problem is solved by simulation, risk and price-dependent demand with NSGA-II algorithm and the results are validated by MOSA algorithm.Proper network design has many effects on the performance, efficiency and effectiveness of supply chains in achieving the expected goals and meeting the needs of customers. In this research, a multi-level multi-objective model for supply chain network design considering pricing, product quality level and disruption is presented. The cost of transporting each vehicle is assumed to be a dynamic random function rather than a parameter. Therefore, discrete-event simulation has been used to estimate transportation costs. Due to the important role of risk and disruption concepts in supply chain network design, risk minimization along with profit maximization according to pricing and quality concepts have been defined as objective functions. Supply chain demand is considered a linear function of price and quality level of products. Finally, the supply chain network design problem is solved by simulation, risk and price-dependent demand with NSGA-II algorithm and the results are validated by MOSA algorithm.https://www.pqprc.ir/article_55879_a98940cb04d0359f9e42426587225057.pdf