Journal of Quality Engineering and Management

Abstract Reliability is an essential characteristic in any application area and is defined as the probability of performing a specified task under given conditions within a predetermined period of time. In this study, the strength (capacity) of a product and the stress or load applied to it are used to model and calculate reliability.
In most cases, the imposed load cannot be controlled; however, various approaches can be employed to improve the capacity of the manufactured product. This paper investigates the effect of reducing variability in each quality characteristic of a product under specific distributions.
Analytical and numerical results indicate that decreasing the variability of the product’s quality characteristics increases its capacity and consequently enhances its reliability.

Abstract Decision variables aimed at improving system performance are considered one of the key issues in most industries. To this end, numerous multi-objective optimization methods have been developed in recent years to address such problems. However, most of these approaches overlook the potential correlations among objectives. In this study, an optimization approach based on a utility function framework is proposed to solve the perindopril separation problem (perindopril is a widely used and effective pharmaceutical agent for treating cardiovascular diseases and hypertension). The proposed approach not only places all objectives at a minimally acceptable level of utility from the decision maker’s (DM) perspective but also accounts for the potential correlations among the objectives and their relative importance. A comparison of the results obtained from a numerical example using the proposed method with those of existing approaches in the literature demonstrates its favorable performance.

Abstract Control charts are powerful tools used for monitoring process variations. In statistical process control, there are many situations in which qualitative (attribute) characteristics of a product or process are monitored simultaneously. Although an out-of-control signal in control charts indicates the presence of a process change, the exact time at which the change occurs is often unknown. Identifying the precise change-point helps process engineers determine the causes of variation and improve the process.
In this study, statistical process control techniques are examined for a process in which qualitative attribute characteristics of the conforming/nonconforming type are measured in a bivariate form. Using the maximum likelihood method, we propose an estimator for determining the change-point in such processes. It is assumed that the two qualitative attribute characteristics in a product are correlated. Simulation results show that the proposed method performs well in detecting the change-point in the process mean vector.





 




 

Abstract Profile monitoring is one of the emerging research areas in the field of statistical process control. A profile describes the relationship between a response variable and one or more independent variables. This relationship, which is typically modeled using a regression equation, may be simple linear, multiple linear, polynomial, or in some cases nonlinear. In order to monitor the quality of a process or product whose quality characteristic is expressed as a profile, multivariate control charts must be used due to the correlation among the regression model parameters. Various types of multivariate control charts can be applied to detect small and large shifts in the process.
A major limitation of multivariate control charts is their inability to identify the specific parameter that goes out of control once a change is detected. In this study, after a multivariate MCUSUM control chart in the MCUSUM–Chi-square method signals a shift in a polynomial profile, a multilayer perceptron neural network is employed to identify the out-of-control parameter. The designed network is trained and then tested, and the results demonstrate its strong performance in identifying the parameter that has shifted out of control.

Abstract Traffic smoothing from an economic perspective, traffic management considerations, and the reduction of environmental pollutants are critical issues in large metropolitan areas. This paper analyzes the traffic problem of a congested freeway network by integrating microscopic traffic simulation with Design of Experiments (DoE) to investigate the effects of highway traffic control strategies.
To manage network traffic, two strategies—ramp metering and lane blocking—are employed. These strategies have recently been implemented on Tehran’s freeways using police vehicles to reduce the volume of incoming traffic to the main freeway.
To evaluate traffic-smoothing strategies, the effects of six decision variables, three variables representing the occurrence of lane blocking near three entrance ramps, and three variables representing ramp metering activation at these ramps, on two performance measures (average speed and total network throughput) are examined using a two-level factorial experimental design.
A total of 64 experimental scenarios were executed in the Aimsun microscopic simulation software, and the outputs were analyzed in Minitab to assess the significance of main and interaction effects of the decision variables. Finally, a regression model was fitted for each performance measure.
The application of Design of Experiments provides enhanced insights into the impacts of traffic-smoothing strategies and opens new avenues for the combined use of these strategies to improve traffic flow.

Abstract The importance of software is increasing day by day, and with this growing importance, continuous efforts are being made to develop technologies that lead to the creation of high-quality software. Software metrics are essential tools for project and quality management. In addition to selecting appropriate metrics for monitoring, detecting meaningful behaviors and changes or deviations in the process, analyzing them, and determining whether process shifts are statistically significant are also crucial.
Since statistical quality control is one of the well-established approaches for addressing these issues, this paper aims, for the first time, to identify and categorize all techniques used in the existing literature related to Statistical Process Control (SPC) in software processes. This classification helps researchers recognize practical topics and research directions and supports them in conducting useful and statistically sound studies based on the compiled material.

Abstract Nowadays, most manufactured products are produced through multiple interdependent process stages. Due to the cascading nature of many such processes, monitoring them using conventional control charts often leads to unavoidable errors and incorrect decisions. One of the useful charts for monitoring multistage processes is the deviation-based control chart. These types of charts have mostly been applied to normally distributed quality characteristics.
In this paper, a two-stage process is examined in which the quality characteristic in the second stage follows a gamma distribution, and the deviation-based control chart is employed for monitoring this process. The test statistic of the deviation-based control chart is derived from combining the inverse NORTA method with a generalized linear model.
To evaluate the performance of the proposed method, the Average Run Length (ARL) index is used, and the results are compared with the best existing method in the literature. The findings indicate that the proposed approach performs better in detecting both upward and downward shifts.

Abstract As is well known, data obtained from real-world processes are typically autocorrelated, and monitoring such processes requires accounting for this autocorrelation. The control charts developed so far for monitoring the defective proportion (p) are generally based on the assumption that binary observations are independent, which ignores the inherent correlation in the data.
In this paper, a cumulative sum (CUSUM) control chart is proposed that incorporates the autocorrelation between binary observations using a first-order two-state Markov chain model. Furthermore, using the Average Number of Observations to Signal (ANOS) index, it is shown that under 100% inspection conditions, the proposed chart performs better than the Bernoulli CUSUM chart—which assumes independent observations—and, in other words, detects increases in p more quickly.