Mixed Multiple Dependent State Sampling Plan Using Exponentially Weighted Moving Average

2016 ◽  
Vol 13 (3) ◽  
pp. 1649-1655 ◽  
Author(s):  
Saeed Ahmad Dobbah ◽  
Amna Hafeez ◽  
Muhammad Aslam ◽  
Nasrullah Khan ◽  
Khushnoor Khan
Keyword(s):  
Moving Average ◽  
Sampling Plan ◽  
Exponentially Weighted Moving Average ◽  
Dependent State ◽  
Weighted Moving Average ◽  
Exponentially Weighted

scholarly journals A New X-bar Control Chart for Multiple Dependent State Sampling Using Neutrosophic Exponentially Weighted Moving Average Statistics with Application to Monitoring Road Accidents and Road Injuries

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Nasrullah Khan ◽  
Liaquat Ahmad ◽  
G. Srinivasa Rao ◽  
Muhammad Aslam ◽  
Ali Hussein AL-Marshadi
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Road Accidents ◽  
Exponentially Weighted Moving Average ◽  
Simulation Methodology ◽  
And Performance ◽  
Run Lengths ◽  
Dependent State ◽  
Weighted Moving Average ◽  
Exponentially Weighted

AbstractIn this article, an efficient mean chart for symmetric data have been presented for multiple dependent state (MDS) sampling using neutrosophic exponentially weighted moving average (NEWMA) statistics. The existing neutrosophic exponentially weighted moving average charts are not capable of seizure the unusual changes threatened to the manufacturing processes. The control chart coefficients have been estimated using the symmetry property of the Gaussian distribution for the uncertain environment. The neutrosophic Monte Carlo simulation methodology has been developed to check the efficiency and performance of the proposed chart by calculating the neutrosophic average run lengths and neutrosophic standard deviations. The proposed chart has been compared with the counterpart charts for confirmation of the proposed technique and found to be a robust chart.

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