نشریه مهندسی و مدیریت کیفیت
سال دوازدهم شماره 3 (پاییز 1401)

Considering the high flexibility of type-2 fuzzy sets to represent uncertainty, their applications in different scopes included control charts are extended. In this paper, to control the centrality of non-normal and ambiguous processes, two non parametric control charts included sign and signed-rank charts have been developed using interval type-2 fuzzy sets. In the type-2 fuzzy sign chart and type-2 fuzzy signed-rank chart, the observations of each sample are compared with the centrality of the process in the control state, which for this purpose it used two different methods. To describe the applicability of the proposed charts, they are used in an example with real data and it is showed their correctness performance. Also, to evaluate the performance of type-2 fuzzy sign chart and type-2 fuzzy signed rank chart, simulation programs have been used in which type-2 fuzzy random variables with three different density functions are generated and in each distribution and both methods, the average run length (ARL) of the charts are calculated. The numerical results show the appropriate performance and applicability of the type-2 fuzzy sign chart and type-2 fuzzy signed-rank chart to control the centrality of non-normal fuzzy random variables.

In this article, we introduce three new distribution-free Shewhart-type control charts that exploit run and Wilcoxon-type rank-sum statistics to detect possible shifts of a monitored process is introduced. Exact formulae for the alarm rate, the run length distribution, and the average run length (ARL) are all derived. A key advantage of these charts is that, due to their nonparametric nature, the false alarm rate (FAR) and in-control run length distribution is the same for all continuous process distributions. Tables are provided for the implementation of the charts for some typical FAR values. Furthermore, a numerical study carrited out reveals that the new charts are quite flexible and efficient in detecting shifts to Lehmann-type out-of-control situations.Statistical quality control charts were introduced in the early work of Shewhart (1926) and since then several variations of them have been proposed for monitoring continuous characteristics. Most of the control charts are distribution-based procedures in the sense that the process output is assumed to follow a specified probability distribution (usually normal); see, for example, Albers et al. (2004).

Quadcopters are a special type of unmanned drones that have many applications in today's world. Due to limited resources, the design of a system must be done in such a way as to achieve the highest possible amount of reliability based on our limited resources.For this purpose, first the reliability of each subsystem was calculated. Then the reliability was optimized using computer algorithms. One of the conventional methods of increasing the reliability of systems is to use redundancies, but due to its limitations Finance and mass for quadcopters, we cannot use any number of extras to increase reliability. Therefore, optimization should be used. The most famous meta-heuristic algorithms can be mentioned as Genetic Algorithm, Coco, Ant Colony, Gray Wolf, etc.With the help of the firefly algorithm and reliability model, the quadcopter was checked in the presence of redundancy in terms of cost and mass minimization and having the most optimal reliability, and the resulting results were validated by genetic algorithm.

The purpose of this research is to investigate the role of Industry 4.0 by identifying and improving the achievements of Industry 4.0 in the quality of products and services provided in the country's home appliance industry. In order to conduct the present research, nine achievements of Industry 4.0 in the quality of products and services provided were identified using research literature. The statistical population of this research is managers, employees, and vice presidents of companies active in the field of household appliances all over the country, 72 of whom were selected by purposive sampling. Using the fuzzy cognitive map, the current status of each of the identified achievements was investigated. This research, by designing a backward scenario, has shown what achievements should be improved in order to reduce waste and production costs. Also, , by compiling and designing a forward-looking scenario, seeks to find out what achievements will be made in order to improve the quality of products and services in the country's home appliance industry, if the costs of waste and production are reduced.

Iran is a windy country due to its proximity to the sea and having various plateaus. The global statistics of the last 30 years indicate an increase in the world's energy needs. Therefore, the optimal use of renewable energy sources, including wind energy for electricity generation, especially based on environmental attitudes, is increasing in many countries of the world. But due to the instability of wind energy, its use faces a challenge, which can be effectively reduced by modeling wind speed volatilities. In this article, the weekly mean recorded data of wind speed in Ardabil meteorological station during 1380-1395 are modeled using time series GARCH models (including GARCH model and asymmetric GARCH models). Based on the Bayesian information criterion, the best model for wind speed volatilities in Ardabil meteorological station is the GARCH model. In this article, Box-Jenkins modeling method with Eviews and R software is used for data analysis.

In some practical applications, the quality of a product or process is defined by a profile, which is a relationship between a response variable and one or more explanatory variables. Simple linear profiles (SLPs) are one of the various types of profiles in which the product or process quality is related to a simple linear function between a response and an explanatory variable. In this article, a functional capability index for a simple linear profile with asymmetric tolerance is introduced. The performance of the proposed index and existing ones (  and ) are studied using numerical examples and simulation studies in terms of mean absolute error (MAE), mean square error (MSE) and absolute percentage error (APE) metrics. The results show that the new index performs better than the two existing indices. Furthermore, confidence intervals for the proposed index are constructed using three bootstrap methods, and their performance is evaluated using simulation studies. A real-world case study is presented to demonstrate the application of the proposed index.