Journal of Quality Engineering and Management

Abstract Micro-coatings have wide applications in modern industrial production. The splashing of micro droplets during impact on the surface, reduce the quality of the surface coating. Spray pressure is one of the most important factors in micro-droplet splashing. In this research, we study the effect of pressure on the diameter of the micro-droplet splashing using the reliability model based on the censored data. The inverted exponentiated Rayleigh as an adequate distribution is used to calculate the reliability model and the maximum likelihood estimator of the parameters of model are obtained. Also based on the Metropolis-Hastings algorithm, the unknown parameters are estimated. The results indicate that the proposed reliability model performs well in estimating the probability of micro-droplet splashing at different spraying pressures. Based on the proposed model, as the nozzle pressure increases, the micro-droplet splashing diameter decreases.   
  

Abstract  In this research, in order to allocate reliability to the subsystems of a cube satellite in the conceptual design phase-with the objective of achieving a 73% reliability-we have developed. The target feasibility method as our research fundamental procedure. In no research study in the area of reliability allocation conducted so far have we found a method which has developed the technology factor or considered the relationships between suppliers and the issue of reliability allocation.  Therefore, the current research study focuses on the exact calculations of the technology factor. In order to estimate the letter factor, a concept designated as technology preparation assessment and another one as level of suppliers technological capacities are being discussed. In this regard, a model has been presented which is a synthesis of the letter two concepts.  The obtained results indicate that the reliability allocation through by adopting this methodology is a great help toward identifying critical subsystems and heir improvement at the design stage . 
 
  

Abstract According to previous research reviews, most studies on the evaluation of sustainable supply chain capabilities are limited to statistical variables. Based on previous research reviews in this paper we prepare a list of capabilities of sustainable supply chain management, in cooperation with an expert team we short listed this capability. Data collection tools, questionnaires and interviews, and data analysis methods including fuzzy Delphi method, fuzzy expert system were used. Also, we applied some data from formal sites in data gathering steps. In order to clustering the variables, in next step we defined limitations of decision variables. Then by utilization of quantitative and fuzzy technique we presented an analytical approach. Simulink tool was used to simulate and integrate the designed fuzzy systems. Levels for sustainable supply chain capabilities are measured and at the end results delivered. The results shows that there are four competencies with high priority, including competitiveness, operational, technology and resilience. 
 

Abstract In this paper, an economic statistical design of control chart is presented for individual qualitative characteristics that have an exponential distribution. For this purpose, we first convert the exponential distribution of individual observations to the normal distribution using the approximation proposed by Nelson. Then, using Costa and Rahim economic model, we have obtained an economic statistical design for the transformed observations. In order to optimize the model parameters that are calculated based on the design parameters, we have obtained the optimal values of the design parameters using the honey bee algorithm. Finally, the results of the economic statistical design of the control chart are compared with the economic design and the positive performance of the economic statistical design compared to the economic design is shown. Also, by simulated data from the exponential distribution, the efficiency and performance of the introduced control chart in comparison with the case that the distribution of the qualitative characteristics is normal, has been investigated. 
 
 

Abstract  Stroke and heart attack are the most important causes of mortality in the world. Identifying communities at risk for stroke and heart attack is an important step in improving the care systems of these patients, strokes and heart attacks are important to the emergency department, The aim of this study is to improve emergency department performance and improve the quality of emergency and hospitals resource allocation. Therefore, the spatial autocorrelation of stroke and heart attack was investigated using spatial statistics. Distribution of these two types of complications in different parts of Tehran was identified by using hot spots analysis and Moran's local autocorrelation index. Also, considering the spatial autocorrelation, to investigate the factors affecting the occurrence of this event, the relationship between the incidence rate of stroke and heart attack with AQI air pollution index And the level of development in different region of Tehran was investigated using Spearman correlation coefficient. 
 

Abstract In this paper, the Bayesian estimation of the reliability parameter, R = P (X <Y), in the two-parameter Riley distribution, is investigated under increasingly censored bond samples. This issue is studied in three different ways. In the first case, assuming that the variables stress, X, and resistance, Y, both have a common location parameter and non-common scale parameters, and all of these parameters are unknown, the Bayesian estimate R is examined. Since in this case Bayesian estimation does not have a closed form, it is approximated by both Lindley and MCMC methods. In the second case, assuming that the stress and resistance variables have a known common place parameter and the common and unknown scale parameters, the exact Bayesian estimate for R is calculated. In the third case, assuming that all parameters are different and unknown, the Bayesian R estimate is calculated using the MCMC approximate method. Bayesian belief intervals are also obtained in all methods. Finally, using Monte Carlo simulations, the performance of different estimators is compared.