1
Department of Statistics, PSG College of Arts & Science, Coimbatore, TamilNadu, India
(amug072000@gmail.com)
2
Department of Statistics, PSG College of Arts & Science, Coimbatore, TamilNadu, India
(nandhitha.siva@gmail.com)
Abstract :
In Statistical Quality Control (SQC), the judgement of the accepting the lot or rejecting the lot s carried out with the help of acceptance sampling plans in the inspection process of any manufacturing industry. Based on the predefined risk the output is attained with minimum inspection cost based on the optimum sample size. Generally Classical statistics is used based on the deterministic nature of the information and measurements. In some circumstances, the quality characteristics may not be certain enough leading to vagueness or impreciseness situation. Accordingly, in past few decades Fuzzy logic is one of the most popular techniques to model the uncertainty in the manufacturing industries. As an advent of technology and knowledge data era, an extension of Fuzzy, a new concept known as Neutrosophic Logic is in progress to apply to achieve these uncertainties. In this, such vagueness, imprecise is called as indeterminants. Thus, Neutrosophic Logic taken its role in Acceptance Sampling Plans with Probability distributions for various plan parameters such as AQL, LQL and Neutrosophic defection status are offered for Poisson distribution in the first time. The chief formulations of Acceptance Sampling plans for Single Sampling were derived based on Neutrosophic Statistics. As an advanced step the mixture of Acceptance Sampling plans with the shifting ruling for swapping from one plan to another plan are named as Sampling System and one such system is Quick Switching System the most widely applicable to safeguard from bad quality which give high level protection as well as to reduce the cost of inspection and time. In this study, Quick Switching System (QSS) with Single Sampling Plan (SSP) as reference plan is constructed based on Neutrosophic sets on Poisson distribution as baseline distribution. The procedures, OC Curves and tables have been redesigned and presented with numerical example.
Keywords :
Fuzzy logic; NS; SQC; QSS; SSP; OC; Poisson distribution; Classical Statistics.
References :
[1] Arumainayagam and Soundararajan (1995); Construction and Selection of Quick Switching System sample size tightening, Journal of Applied Statistics, Vol.22, No.2, pp.105-119.
[2] Aslam (2019); A new attribute sampling plan using Neutrosophic Statistical Interval method, Complex and intelligent systems, 1-6.
[3] Aslam (2019); A Variable Acceptance Sampling Plan under Neutrosophic Statistical Interval method, Symmetry, p.114.
[4] Aslam (2018); Design of sampling plan for exponential distribution under Neutrosophic Statistical Interval Method, IEEE Access, pp.64153-64158.
[5] Bahram Sadeghpour- Gildeh, Gholamhossein Yari, Ezzatallah Balouli Jamkhaneh (2008); Acceptance double sampling plan with fuzzy parameter, Proceedings of the 11th Joint Conference on Information Sciences.
[6] Chakraborty (1992); A class of Single Sampling Plans based on fuzzy optimization, Opsearch, 29(1), 11-20.
[7] Dodge and Romig (1956); Sampling inspection tables, John Wiley Co.,New York, U.S.sA
[8] Divya (2012); Quality interval acceptance single sampling plan with fuzzy parameter using Poisson distribution, Int.J.Adv.Res.Technol,1(3),pp.115-125.
[9] Ezzatallah Balouli Jamkhaneh, Bahram Sadeghpour- Gildeh ,Gholamhossein(2009);Acceptance Single Sampling plan with fuzzy parameter with the use of Poisson distribution , World Academy of Science, Engineering and Technology,3,01-20.
[10] Schilling (1982): Acceptance Sampling Quality Control, Dekker, New York.
[11] Isik and Kaya (2020); Analyzing Single Acceptance Sampling Plans based on Neutrosophic Poisson distribution, International Marmara Science and Social Sciences Congress (IMASCON), 594-604.
[12] Jamkhaneh, Gildeh and Yari (2011): Acceptance Single Sampling plan with fuzzy parameter, Iranian Journal of Fuzzy Systems, pp.47-55.
[13] Kanagawa and Ohta (1990); A design for Single Sampling Attribute Plan based on fuzzy sets theory, Fuzzy Sets and Systems, 37, 173-181.
[14] Kahraman, Bekar and Senvar (2016); A fuzzy design of Single and Double Acceptance Sampling plans, Intelligent Decision Making in Quality Management Switzerland, Springer, pp. 179-211
[15] Montgomery (2007); Introduction to Statistical Quality Control, Wiley, New York.
[16] Nandhinidevi, Uma and Manjula (2020); An impact of Fuzzy logic on Quick Switching Single Double Sampling Plan – Acceptance Number Tightening, The International Journal of analytical and experimentalmodal analysis. VolXII, Issue 1.
[17] Ohta. H and H. Ichihashi (1988); Determination of Single sampling – attribute plans based on membership frunctions, International Journal of Production Research, 26(9), 1477-1485.
[18] Ramya and Uma (2017); Determination of Quick Switching Double Sampling System by Attributes under .Fuzzy Binomial Distribution – Sample Size tightening, ICTACT journal on Soft Computing, 8(1), 1539-1543.
[19] Romboski (1969); An investigation of Quick Switching Acceptance Sampling Systems, Doctoral Dissertation, (New Brunwick, NJ, The Statistics center, Rutgers- The State University)
[20] Smarandache (2014), Introduction to Neutrosophic Statistics, Infinite study.
[21] Smarandache, Zhang and Sunderraman (2005); Interval Neutrosophic sets and logic: Theory and Applications in Computing, Infinkite Study.
[22] Smarandache, Zhang and Sunderraman (2010); Single valued neutrosophic sets. Infinite Study,10-14.
[23] Tamaki, Kanagawa, Ohta (1991); A Fuzzy design of sampling inspection plans by attributes, Japanese journal of fuzzy theory and systems,3,315-327.
[24] Uma and Arumainayagam (2009); Construction of Matched Quick Switching Single Double and Multiple Sampling Systems, International Journal of Statistics & Systems, Vol.3, Issue 2, pp.147.
[25] Uma ad Ramya (2015); Impact of Fuzzy Logic on Acceptance Sampling Plans- A Review, CiiT International Journal of Automation and Autonomous System, 7(7), 181-185.
| Style | # |
|---|---|
| MLA | Uma G., Nandhitha S.. "Analyzing and Evaluation of Quick Switching System using Neutrosophic Poisson Distribution." International Journal of Neutrosophic Science, Vol. 20, No. 4, 2023 ,PP. 119-127 (Doi : https://doi.org/10.54216/IJNS.200409) |
| APA | Uma G., Nandhitha S.. (2023). Analyzing and Evaluation of Quick Switching System using Neutrosophic Poisson Distribution. Journal of International Journal of Neutrosophic Science, 20 ( 4 ), 119-127 (Doi : https://doi.org/10.54216/IJNS.200409) |
| Chicago | Uma G., Nandhitha S.. "Analyzing and Evaluation of Quick Switching System using Neutrosophic Poisson Distribution." Journal of International Journal of Neutrosophic Science, 20 no. 4 (2023): 119-127 (Doi : https://doi.org/10.54216/IJNS.200409) |
| Harvard | Uma G., Nandhitha S.. (2023). Analyzing and Evaluation of Quick Switching System using Neutrosophic Poisson Distribution. Journal of International Journal of Neutrosophic Science, 20 ( 4 ), 119-127 (Doi : https://doi.org/10.54216/IJNS.200409) |
| Vancouver | Uma G., Nandhitha S.. Analyzing and Evaluation of Quick Switching System using Neutrosophic Poisson Distribution. Journal of International Journal of Neutrosophic Science, (2023); 20 ( 4 ): 119-127 (Doi : https://doi.org/10.54216/IJNS.200409) |
| IEEE | Uma G., Nandhitha S., Analyzing and Evaluation of Quick Switching System using Neutrosophic Poisson Distribution, Journal of International Journal of Neutrosophic Science, Vol. 20 , No. 4 , (2023) : 119-127 (Doi : https://doi.org/10.54216/IJNS.200409) |