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Article type: Research Article
Authors: Aslam, Muhammada; * | Khan, Nasrullahb
Affiliations: [a] Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia | [b] Department of Statistics, University of Veterinary and Animal Sciences (Jhang Campus), Lahore, Pakistan
Correspondence: [*] Corresponding author. Muhammad Aslam, Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia. E-mail: magmuhammad@kau.edu.sa.
Abstract: The existing variable control charts using the classical statistics are designed under the assumption that no uncertainty, fuzziness or indeterminate observations or parameters. In practice, the data obtained from the measurement process may have indeterminate or unclear values. In later the situation, it may not possible to apply the variable control chart designed using the classical statistics. The neutrosophic statistics which is the extension of the classical statistics is more suitable to deal with uncertainty/indeterminate observations/parameters issues in the industry. In this paper, we will originally design a variable control chart for the monitoring of data having indeterminate observations using the neutrosophic interval statistics method. The neutrosophic control charts coefficients will be determined and used to calculate the neutrosophic average run length (NARL). A simulation is study is also given to show the efficiency of control chart designed using neutrosophic interval statistics method over the control chart designed using the classical statistics. A real example from the industry is also given. From the comparative study, it is concluded that the proposed control chart under the neutrosophic interval statistics method is more effective and adequate than the chart based on the classical statistics under the uncertainty environment.
Keywords: Classical statistics, neutrosophic statistics, indeterminate observations, neutrosophic average run length
DOI: 10.3233/JIFS-181767
Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2615-2623, 2019
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