Journal of Quality Engineering and Management

Abstract Today, the increasing progress in technology, the expansion and development of human needs for sustainable technologies have caused attention to electric energy to be in the center of attention more than in the past. Therefore, increasing the reliability of electrical systems in the power industry is very important. The purpose of this research is to provide a mathematical model to calculate and increase reliability in the power distribution network. This research is practical in terms of its purpose and results, and in terms of the method and nature of implementation, it is based on operational research that was conducted using mathematical modeling and using Python software based on data from 1398 to 1402. The findings show that parameters such as generators, high pressure and low pressure busbars, 20 kV to 400 V power transformers, communication cables, capacitors, generators and UPS are more important in calculating the reliability of this network. Therefore, according to the purpose and the corresponding limitations, a suitable mathematical model has been presented for each parameter. The results show that after 50 repetitions and simulations, the ultra-emergency line has a higher importance and rank in reliability among the four output feeders. Also, based on the presented model, it has been observed that the entire 20 kV line under investigation has 0.67 degrees of reliability. The results of this research can be considered as a suitable basis for the implementation of research and operational projects in wide radial networks in the electricity industry.

Abstract One of the most practical methods of monitoring and controlling project performance is earned value management. The widespread use of this method in many projects leads to the production of multiple indicators with mutual influence, This important feature makes their individual analysis with errors. In this method, not only the mutual influence of the indicators is not paid attention to, but also the lack of attention to the monitoring of the variability of the indicators has caused this method to monitor the project based on the criterion of indexability. In this research, using multivariable control charts, we have simultaneously checked the performance indicators of a gas supply project based on real data. After analyzing the obtained results, we have compared the performance of Hotelling and (MEWMA) charts with each other. After comparison, it was found that MEWMA chart has more capability and sensitivity than Hotelling chart in identifying changes in multivariate processes.

Abstract In this article, a queuing system with finite capacity, referred to as M/M/m/K, is analyzed for m ≥ 2, where K represents the system's capacity and m indicates the number of servers. Initially, a function known as the system cost function is introduced. This function is based on the number of customers present in the queue and the number of servers available. The main objective is to identify the optimal number of servers, termed mOpt, that minimizes the system cost function. This optimal configuration, denoted as M/M/mOpt/K, is termed the optimal system. To illustrate the concept, a numerical example is provided, showcasing various values of K to determine the optimal systems. The analysis covers key performance metrics such as the average number of customers in the queue and the entire system, the average waiting time of the customers both in the queue and the system, and a metric referred to as the average degree of customer satisfaction within these queuing systems. Through this comprehensive approach, the study aims to provide valuable insights into optimizing queuing systems for better efficiency and customer satisfaction.

Abstract : Mineral industries are one of the important sectors of industry in Iran, therefore, it is necessary to improve the quality of mineral products. One of these products is foundry silica sand. The aim of this study was to create a complete model using this type of silica sand. A comprehensive analysis was done on ten mines and seven mines were selected to perform the quality improvement stage. A total of 1400 tests were conducted to achieve the main goal of the research, which was to increase the quality of silica sand parameters. It was also found that the seven basic characteristics of silica sand have a significant effect on the quality of the final products. The quality of silica sands is influenced by elements such as calcium, sodium, potassium and magnesium, which are alkaline elements of the soil. A higher percentage of silica in a mineral is usually associated with increased quality, as it ensures the achievement of ideal properties and performance in silica sands. Factors affecting the quality of silica sand were prioritized by experts using the fuzzy Delphi technique and hierarchical analysis. These factors have an effect on the chemical composition, purity, reactivity and performance of silica sands. Also, a data mining model was designed to predict the quality of these sands. The findings of this study show that the presence of calcium, sodium, potassium, magnesium, silica content, ADV (sand alkalinity or acidity) and pH affect the quality of silica sands. It is concluded that this model provides an efficient attitude and prediction to increase product quality.

Abstract The aims of this research is to prioritize airline insurance risks, In order to identify and categorize the risks, the initial risks were determined from the literature review and then the risk tree was drown using the experts' opinion. Fuzzy paired comparison questionnaire has been used to prioritize risks. After collecting the experts' opinions, if the answers are inconsistent, the inconsistent opinions have been corrected with the generalized error square method, and finally, the collective opinions of the experts have been used as the criteria for risk ranking. The findings of this research indicate the identification of 4 types of main risks and their indicators in the aviation insurance industry, which are: a) Internal financial risks including capital adequacy risk, capital quality risk, operating ratio risk, The risk of non-realization of income and profit and liquidity risk; b) internal non-financial risks including credit risk, human resources risk and the risk of the company's internal processes and systems; c) external risks including sanctions risk, the risk of wars and military conflicts, the risk of natural disasters and market risk, and d) customer risks, including the risk of customer churn and the risk of bankruptcy and credit. Among these 4 types of defined risks, internal financial risk is the most important risk and customer risk is the least important risk according to experts' opinions. The most important risk index is the risk of sanctions and the least important risk index is the risk index of the company's internal processes and systems.

Abstract Modelling and estimating the intensity function of a point pattern is one of the preliminary and fundamental issues in inference of point processes, and it considered as a prerequisite for many other problems. It has been addressed from different perspectives. With the rapid development of data-collection technologies, a wide range of data has been produced, and considering covariates has been a big step forward in the theory of point processes, which has mainly been addressed from a parametric perspective.

In this paper, we introduce a novel approach for nonparametrically estimating the intensity of an inhomogeneous Poisson point process, which is an unknown function of several independent spatial covariates. In the proposed method, using the approximation technique of radial basis function for unknown multivariate functions, the nonparametric model of the intensity function is transformed into a log-linear model. Since the accuracy of the multivariate function approximation directly affects the accuracy of the intensity function estimate, we enhance the nonparametric estimation quality of the intensity function in spatial Poisson point processes by optimizing the shape parameter of the radial basis function through minimizing the Bayesian information criterion.