On scale parameter monitoring of the Rayleigh distributed data using a new design

In recent years, many supplementary designs have been developed incorporating the assumption that data follow the particular non-normal distribution. The VR-control chart is one such design proposed to monitor the parameter 2 of the single parameter Rayleigh distributed data. Commonly, in authentic situations, practitioners need to estimate the scale parameter in the observed processes instead of 2. However, the positive square root of the VR-statistic used in the existing design of VR-control chart is not anunbiased estimator of and thus could not be practiced to monitor the scale parameter of the Rayleigh distributed process. A new structure of the VR-control chart namely VSQR for monitoring the scale parameter of the Rayleigh distributed data has been originally developed in this study. The statistical basis of this newly VSQR design in terms of average run length .ARL/, characteristic function and power curve have been derived. The analytical results are utilized further to determine the parameters of VSQR-chart and in comparing the performance of the proposed control chart with existing competitors. Comparative results illustrate the effectiveness of the proposed design in view of statistical power. Finally, the computational procedure of this newly VSQR-chart has been demonstrated using simulated data and real data on the breaking strength of carbon fibers

DataSheet1_q-Rung orthopair fuzzy hypersoft ordered aggregation operators and their application towards green supplier.PDF

Green Supply Chain Management (GSCM) is essential to ensure environmental compliance and commercial growth in the current climate. Businesses constantly look for fresh concepts and techniques for ensuring environmental sustainability. To keep up with the new trends in environmental concerns related to company management and procedures, Green Supplier Selection (GSS) criteria are added to the traditional supplier selection processes. This study aims to identify general and environmental supplier selection criteria to provide a framework that can assist decision-makers in choosing and prioritizing appropriate green supplier selection. The development and implementation of decision support systems aimed to solve these difficulties at a rapid rate. In order to manage inaccurate data and simulate decision-making problems. Fuzzy sets introduced by Zadeh, are a useful technique to handle the imperfectness and uncertainty in different problems. Although fuzzy sets can handle incomplete information in different real worlds problems, but its cannot handle all type of uncertainty such as incomplete and indeterminate data. Therefore different extensions of fuzzy sets such as intuitionistic fuzzy, pythagorean fuzzy and q-rung orthopair fuzzy sets introduced to address the problems of uncertainty by considering the membership and non-membership grade. However, these concepts have some shortcomings in the handling uncertainty with sub-attributes. To overcome this difficulties Khan et al. developed the structure of q-rung orthopair fuzzy hypersoft sets by combining q-rung orthopair fuzzy sets with hypersoft sets. A remarkable and beneficial research work is done in the field of q-rung orthopair fuzzy hypersoft sets, and then we think about the application. In this paper, we use the structure of q-rung orthopair fuzzy hypersoft in multi-criteria supplier selection problems. For this, we present aggregation operator to solve multi-criteria decision-making (MCDM) problems with q-rung orthopair fuzzy hypersoft (q-ROFH) information, known as ordered weighted geometric aggregation operator. Since the uncertainty and vagueness is an unavoidable feature of multi-criteria decision-making problems, the proposed structure can be a useful tool for decision making in an uncertain environment. Further, the expert opinions were investigated using the multi-criteria decision-making (MCDM) technique, which helped identify interrelationship and causal preference of green supplier evaluation aspects that used aggregation operators. Finally, a numerical example of the proposed method for the task of Green Supplier Selection is presented.</p