ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Determination of Quick Switching Double Sampling System by Attributes Under Fuzzy Binomial Distribution–Sample Size Tightening


Affiliations
1 Department of Statistics, PSG College of Arts and Science, India
     

   Subscribe/Renew Journal


Acceptance sampling is concerned with norms of deciding about the acceptance or rejection of the lots based on the quality of the product during inspection. Dodge and Romig popularized the acceptance sampling as a major field of SQC. Various Sampling plans, systems and schemes are developed as per the need of the industry. Quick Switching Systems is a system which requires fewer samples for processes running at low levels of defects. Quick Switching Systems are compared with single, double, multiple, chain and variable sampling plans as well as MIL STD 105 E switching systems and concluded its advantages. This article presents the Quick Switching Double Sampling System (QSDSS) when the fraction of non-conforming items is a fuzzy number and being modeled based on the fuzzy Binomial distribution. Operating Characteristic (OC) curves of the fuzzy system is like a band having high and low bounds whose width depends on the ambiguity proportion parameter in the lot when that sample size and acceptance numbers is fixed. Tables are constructed and numerical illustrations are given to describe the determination of QSDSS for sample size tightening with fuzzy Binomial distribution with its OC curve.

Keywords

SQC, SSP, DSP, QSDSS, OC, Fuzzy Binomial Distribution.
Subscription Login to verify subscription
User
Notifications
Font Size

  • S.D. Arumainayagam and V. Soundararajan, “Quick Switching Double Sampling System (QSDSS)-Acceptance Number Tightening”, Communications in Statistics-Simulation and Computation, Vol. 24, No. 3, pp. 2079-2100, 1995.

  • S.D. Arumainayagam and V. Soundararajan, “Construction and Selection of Quick Switching Double Sampling System Size Tightening”, Journal of Applied Statistics, Vol. 22, No. 1, pp. 105-119, 1995.

  • B.S. Gildeh, G. Yari and E.B. Jamkhaneh, “Acceptance Double Sampling Plan with Fuzzy Parameter”, Proceedings of 11th Joint Conference on Information Sciences, pp. 1-9, 2008.

  • E.B. Jamkhaneh, B.S. Gildeh and G. Yari, “Acceptance Single Sampling Plan with Fuzzy Parameter”, Iranian Journal of Fuzzy Systems, Vol. 8, No. 2, pp. 47-55, 2009.

  • T.K. Chakraborty, “A Class of Single Sampling Plans based on Fuzzy Optimization”, Fuzzy Sets and Systems, Vol. 63, No. 1, pp. 35-43, 1994.

  • T.K. Chakraborty, “Possibilistic Parameter Single Sampling Inspection Plans”, Opsearch, Vol. 31, No. 2, pp. 108-126, 1994.

  • S.D. Arumainayagam and G. Uma, “Construction and Selection of Quick Switching Multiple Sampling System-Sample Size Tightening”, Quality Control and Applied Statistics, Vol. 34, No. 2, pp. 115-119, 2011.

  • P.R. Divya, “Quality Interval Acceptance Single Sampling Plan with Fuzzy Parameter using Poisson Distribution”, International Journal of Advancements in Research and Technology, Vol. 1, No. 3, pp. 1-11, 2012.

  • H.F. Dodge and H.G. Romig, “Sampling Inspection Tables”, John Wiley and Sons, 1967.

  • E.B. Jamkhaneh, B.S. Gildeh and G. Yari, “Acceptance Single Sampling Plan with Fuzzy Parameter with the using of Poisson Distribution”, World Academy of Science, Engineering and Technology, Vol. 3, No. 1, pp. 1-20, 2009.

  • E. Turanoglu, I. Kaya and C. Kahraman, “Fuzzy Acceptance Sampling and Characteristic Curves”, International Journal of Computational Intelligence Systems, Vol. 5, No. 1, pp. 13-29, 2012.

  • E.B. Jamkhaneh and B.S. Gildeh, “Acceptance Double Sampling Plan using Fuzzy Poisson Distribution”, World Applied Sciences Journal, Vol. 16, No. 11, pp. 1578-1588, 2012.

  • R. Sampath Kumar, R. Vijaya Kumar and R. Radhakrishnan, “Selection of Mixed Sampling Plans with Conditional Double Sampling Plan as Attribute Plan Indexed through MAPD and LQL using IRPD”, International Journal of Computational Engineering Research, Vol. 2, No. 2, pp. 306-313, 2012.

  • O. Hryniewisz, “Statistical Decisions with Imprecise Data and Requirements”, Proceedings of International Conference on Systems Analysis and Decisions Support in Economics and Technology, pp. 135-143, 1994.

  • O. Hryniewisz, “Statistics with Fuzzy Data in Statistical Quality Control”, Soft Computing, Vol. 12, No. 3, pp. 229-234, 2008.

  • A. Kanagawa and H. Ohta, “A Design for Single Sampling Attribute Plan based on Fuzzy Sets Theory”, Fuzzy Sets and Systems, Vol. 37, No. 2, pp. 173-181, 1990.

  • A. Kaufmann and M.M. Gupta, “Fuzzy Mathematical Models in Engineering and Management Science”, Elsevier, 1988.

  • V. Palanivel and M. Soundararajan, “Quick Switching Variables Single Sampling (QSVSS) System Indexed by AQL and AOQL”, Journal of Applied Statistics, Vol. 27, No. 6, pp. 771-778, 2000.

  • S. Muthulakshmi and D. Malathi, “Special Double Sampling Plan with Fuzzy Parameter”, Indian Journal of Applied Research, Vol. 2, No. 1, pp. 141-143, 2012.

  • H. Ohta and H. Ichihashi, “Determination of Single Sampling-Attributes Plans based on Membership Functions”, International Journal of Production Research, Vol. 26, No. 9, pp. 1477-1485, 1988.

  • Palanivel, “Results and Tables relating to QSVSS-Acceptance Criterion Tightening, Sample Size Tightening”, Ph.D Dissertation, Bharathiar University, 1999.

  • L.D. Romboski, “An Investigation of Quick Switching Acceptance Sampling System”, Ph.D Dissertation, Rutgers-The State University, 1969.

  • E.G. Schiling and D.V. Neubauer, “Acceptance Sampling Quality Control”, 3rd Edition, CRC Press, 2017.

  • K.K. Suresh and S. Jayalakshmi, “Selection of Quick Switching System with Special Type Double Sampling Plans through MAPD and MAAOQ”, International Journal of Statistics and Management Systems, Vol. 3, No. 12, pp. 93-100, 2008.

  • F. Tamaki, A. Kanagawa and H. Ohta, “A Fuzzy Design of Sampling Inspection Plans by Attributes”, Japanese journal of Fuzzy Theory and Systems, Vol. 3, No. 4, pp. 315-327, 1991.

  • Uma and S.D. Arumainayagam, “Quick Switching Multiple Sampling System-QSMSS”, Proceedings of 25th Conference of Indian Society for Probability and Statistics and Annual Meeting of Indian Bayesian Society, pp. 14-18, 2005.

  • G. Uma and Komaladevi, “Designing Quick Switching System with Fuzzy Parameter”, Proceedings of UGC sponsored National Conference on Advances in Statistical Theory, Modeling and Applications, pp. 23-26, 2014.

  • G. Uma and R. Nandhini Devi, “Determination of Quick Switching System by Attributes under Fuzzy Poisson Distribution”, Automation and Autonomous System, Vol. 7, No. 7, pp. 122-126, 2015.


Abstract Views: 369

PDF Views: 4




  • Determination of Quick Switching Double Sampling System by Attributes Under Fuzzy Binomial Distribution–Sample Size Tightening

Abstract Views: 369  |  PDF Views: 4

Authors

K. Ramya
Department of Statistics, PSG College of Arts and Science, India
G. Uma
Department of Statistics, PSG College of Arts and Science, India

Abstract


Acceptance sampling is concerned with norms of deciding about the acceptance or rejection of the lots based on the quality of the product during inspection. Dodge and Romig popularized the acceptance sampling as a major field of SQC. Various Sampling plans, systems and schemes are developed as per the need of the industry. Quick Switching Systems is a system which requires fewer samples for processes running at low levels of defects. Quick Switching Systems are compared with single, double, multiple, chain and variable sampling plans as well as MIL STD 105 E switching systems and concluded its advantages. This article presents the Quick Switching Double Sampling System (QSDSS) when the fraction of non-conforming items is a fuzzy number and being modeled based on the fuzzy Binomial distribution. Operating Characteristic (OC) curves of the fuzzy system is like a band having high and low bounds whose width depends on the ambiguity proportion parameter in the lot when that sample size and acceptance numbers is fixed. Tables are constructed and numerical illustrations are given to describe the determination of QSDSS for sample size tightening with fuzzy Binomial distribution with its OC curve.

Keywords


SQC, SSP, DSP, QSDSS, OC, Fuzzy Binomial Distribution.

References