A new attribute sampling plan using neutrosophic statistical interval method

In the classical statistics, for the inspection of a final lot of the product, a decision is made either the lot has good quality or not using an acceptance sampling plan. The acceptance of sampling using the classical statistics assumes that data are determinate. In some situations, the data may im...

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Published in:	Complex & intelligent systems Vol. 5; no. 4; pp. 365 - 370
Main Author:	Aslam, Muhammad
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.12.2019 Springer Nature B.V
Subjects:	Binomial distribution Complexity Computational Intelligence Data Structures and Information Theory Engineering Inspection Original Article Sampling Sampling techniques Statistics Lot Neutrosophic Statistics Distribution
ISSN:	2199-4536, 2198-6053
Online Access:	Get full text
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Abstract	In the classical statistics, for the inspection of a final lot of the product, a decision is made either the lot has good quality or not using an acceptance sampling plan. The acceptance of sampling using the classical statistics assumes that data are determinate. In some situations, the data may imprecise and even intermediate which leads to indecision about the quality of the submitted lot of the product. In this situation, neutrosophic statistics can be applied for the lot sentencing. In this paper, a new attribute sampling plan is proposed using the neutrosophic interval method. The lot acceptance, rejection, and indeterminate probabilities are computed using the neutrosophic binomial distribution at various specified parameters such as sample size and acceptance number. The efficiency of the proposed sampling plan is also discussed. A real example is also added to explain the proposed sampling plan.
AbstractList	In the classical statistics, for the inspection of a final lot of the product, a decision is made either the lot has good quality or not using an acceptance sampling plan. The acceptance of sampling using the classical statistics assumes that data are determinate. In some situations, the data may imprecise and even intermediate which leads to indecision about the quality of the submitted lot of the product. In this situation, neutrosophic statistics can be applied for the lot sentencing. In this paper, a new attribute sampling plan is proposed using the neutrosophic interval method. The lot acceptance, rejection, and indeterminate probabilities are computed using the neutrosophic binomial distribution at various specified parameters such as sample size and acceptance number. The efficiency of the proposed sampling plan is also discussed. A real example is also added to explain the proposed sampling plan.
Author	Aslam, Muhammad
Author_xml	– sequence: 1 givenname: Muhammad orcidid: 0000-0003-0644-1950 surname: Aslam fullname: Aslam, Muhammad email: aslam_ravian@hotmail.com organization: Department of Statistics, Faculty of Science, King Abdulaziz University
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Cites_doi	10.3233/IFS-151973 10.1016/j.future.2018.03.014 10.1007/s40815-018-0560-x 10.1109/ACCESS.2018.2877923 10.1080/03610926.2011.563019 10.3390/sym10060226 10.1016/j.measurement.2018.04.001 10.4028/www.scientific.net/AMM.859.59 10.3233/JIFS-171952 10.1016/j.future.2018.06.024 10.1007/s00158-010-0579-6 10.3390/sym10040116 10.3390/sym10040106 10.1007/s10617-018-9203-6 10.1007/s00521-018-3404-6 10.3390/sym9100208 10.3390/sym9070123 10.1108/02635570710758761 10.1007/978-3-319-24499-0_7 10.1007/978-3-319-16598-1_4 10.1109/IFSA-SCIS.2017.8023269 10.1109/SSCI.2016.7850151 10.3233/JIFS-17981 10.3233/IFS-120684 10.3233/JIFS-161912 10.3390/sym10090403 10.3390/sym10050132 10.1080/18756891.2012.670518
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References	AfshariRSadeghpour GildehBSarmadMFuzzy multiple deferred state attribute sampling plan in the presence of inspection errorsJ Intell Fuzzy Syst201733150351410.3233/JIFS-161912 JamkhanehEBSadeghpour-GildehBYariGInspection error and its effects on single sampling plans with fuzzy parametersStruct Multidiscip Optim201143455556010.1007/s00158-010-0579-6 Abdel-BassetMA hybrid approach of neutrosophic sets and DEMATEL method for developing supplier selection criteriaDes Autom Embed Syst201810.1007/s10617-018-9203-6 SmarandacheFIntroduction to neutrosophic statistics2014Ann ArborInfinite Study1286.62006 MajumdarPAcharjyaDDehuriSSanyalSNeutrosophic sets and its applications to decision makingComputational intelligence for big data analysis2015ChamSpringer9711510.1007/978-3-319-16598-1_4 Sadeghpour GildehBBaloui JamkhanehEYariGAcceptance single sampling plan with fuzzy parameterIran J Fuzzy Syst201182475528833181260.62089 BroumiSShortest path problem under bipolar neutrosphic settingAppl Mech Mater2017859596610.4028/www.scientific.net/AMM.859.59 TuranoğluEKayaİKahramanCFuzzy acceptance sampling and characteristic curvesInt J Comput Intell Syst201251132910.1080/18756891.2012.670518 Abdel-BassetMA group decision making framework based on neutrosophic VIKOR approach for e-government website evaluationJ Intell Fuzzy Syst20183464213422410.3233/JIFS-171952 BroumiSComputing operational matrices in neutrosophic environments: a Matlab toolboxNeutrosophic Sets Syst2017185866 AiwuZJianguoDHongjunGInterval valued neutrosophic sets and multi-attribute decision-making based on generalized weighted aggregation operatorJ Intell Fuzzy Syst20152962697270610.3233/IFS-151973 Abdel-BassetMMohamedMSmarandacheFAn extension of neutrosophic AHP–SWOT analysis for strategic planning and decision-makingSymmetry201810411610.3390/sym10040116 Abdel-BassetMMohamedMThe role of single valued neutrosophic sets and rough sets in smart city: imperfect and incomplete information systemsMeasurement2018124475510.1016/j.measurement.2018.04.001 KahramanCBekarETSenvarOKahramanCYanikSA fuzzy design of single and double acceptance sampling plansIntelligent decision making in quality management2016ChamSpringer17921110.1007/978-3-319-24499-0_7 DivyaPQuality interval acceptance single sampling plan with fuzzy parameter using poisson distributionInt J Adv Res Technol201213115125 Smarandache F (2010) Neutrosophic logic-a generalization of the intuitionistic fuzzy logic. In: Multispace & multistructure. Neutrosophic transdisciplinarity (100 collected papers of science), vol 4, p 396 ChenJYeJDuSScale effect and anisotropy analyzed for neutrosophic numbers of rock joint roughness coefficient based on neutrosophic statisticsSymmetry201791020810.3390/sym9100208 Abdel-BassetMMohamedMSmarandacheFA hybrid neutrosophic group ANP-TOPSIS framework for supplier selection problemsSymmetry201810622610.3390/sym10060226 Broumi S et al (2016) Application of Dijkstra algorithm for solving interval valued neutrosophic shortest path problem. In: 2016 IEEE symposium series on computational intelligence (SSCI). IEEE AslamMDesign of sampling plan for exponential distribution under neutrosophic statistical interval methodIEEE Access20186641536415810.1109/ACCESS.2018.2877923 Wang H et al (2005) Single valued neutrosophic sets. In: Proceedings of 10th 476 international conference on fuzzy theory and technology. Citeseer Abdel-BassetMThree-way decisions based on neutrosophic sets and AHP-QFD framework for supplier selection problemFuture Gener Comput Syst201889193010.1016/j.future.2018.06.024 JamkhanehEBGildehBSAcceptance double sampling plan using fuzzy poisson distributionWorld Appl Sci J2012161115781588 AslamMA new sampling plan using neutrosophic process loss considerationSymmetry2018105132378720010.3390/sym10050132 JamkhanehEBGildehBSSequential sampling plan using fuzzy SPRTJ Intell Fuzzy Syst201325378579130791721291.62143 Afshari R, Gildeh BS (2017) Construction of fuzzy multiple deferred state sampling plan. In: Fuzzy systems association and 9th international conference on soft computing and intelligent systems (IFSA-SCIS), 2017 joint 17th World Congress of International. IEEE AslamMTwo-stage variables acceptance sampling plans using process loss functionsCommun Stat Theory Methods201241203633364710.1080/03610926.2011.563019 AslamMRazaMADesign of new sampling plans for multiple manufacturing lines under uncertaintyInt J Fuzzy Syst201810.1007/s40815-018-0560-x AslamMArifOTesting of grouped product for the Weibull distribution using neutrosophic statisticsSymmetry201810940310.3390/sym10090403 AfshariRGildehBSSarmadMMultiple deferred state sampling plan with fuzzy parameterInt J Fuzzy Syst201720191376.62123 Abdel-BassetMMohamedMChangVNMCDA: a framework for evaluating cloud computing servicesFuture Gener Comput Syst201886122910.1016/j.future.2018.03.014 Abdel-BassetMMulti-criteria group decision making based on neutrosophic analytic hierarchy processJ Intell Fuzzy Syst20173364055406610.3233/JIFS-17981 VenkatehAElangoSAcceptance sampling for the influence of TRH using crisp and fuzzy gamma distributionAryabhatta J Math Inform201461119124 BroumiSA Matlab toolbox for interval valued neutrosophic matrices for computer applicationsUluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi201711121 ChenJExpressions of rock joint roughness coefficient using neutrosophic interval statistical numbersSymmetry20179712310.3390/sym9070123 ChengS-RHsuB-MShuM-HFuzzy testing and selecting better processes performanceInd Manag Data Syst2007107686288110.1108/02635570710758761 BroumiSShortest path problem under interval valued neutrosophic settingJ Fundam Appl Sci2018104S168174 Abdel-BassetMGunasekaranMMohamedMA novel method for solving the fully neutrosophic linear programming problemsNeural Comput Appl201810.1007/s00521-018-3404-6 ElangoSVenkateshASivakumarGA fuzzy mathematical analysis for the effect of TRH using acceptance sampling plansInt J Pure Appl Math201711717 MontgomeryDCIntroduction to statistical quality control2007New YorkWiley0997.62503 Abdel-BassetMNeutrosophic association rule mining algorithm for big data analysisSymmetry201810410610.3390/sym10040106 P Divya (88_CR8) 2012; 1 E Turanoğlu (88_CR6) 2012; 5 M Abdel-Basset (88_CR30) 2018 J Chen (88_CR20) 2017; 9 S Broumi (88_CR39) 2017; 18 M Abdel-Basset (88_CR31) 2018; 89 M Abdel-Basset (88_CR24) 2018; 10 R Afshari (88_CR13) 2017; 20 J Chen (88_CR21) 2017; 9 M Abdel-Basset (88_CR29) 2018 S Broumi (88_CR40) 2017; 1 R Afshari (88_CR12) 2017; 33 S Broumi (88_CR37) 2018; 10 F Smarandache (88_CR19) 2014 M Aslam (88_CR34) 2018 M Abdel-Basset (88_CR28) 2018; 124 M Aslam (88_CR33) 2018; 10 A Venkateh (88_CR9) 2014; 6 S Broumi (88_CR36) 2017; 859 B Sadeghpour Gildeh (88_CR4) 2011; 8 C Kahraman (88_CR10) 2016 S-R Cheng (88_CR2) 2007; 107 M Aslam (88_CR35) 2018; 6 M Abdel-Basset (88_CR23) 2018; 10 S Elango (88_CR14) 2017; 117 88_CR11 M Aslam (88_CR41) 2012; 41 DC Montgomery (88_CR1) 2007 Z Aiwu (88_CR18) 2015; 29 M Abdel-Basset (88_CR26) 2018; 86 P Majumdar (88_CR16) 2015 88_CR17 M Abdel-Basset (88_CR27) 2018; 34 88_CR15 M Abdel-Basset (88_CR25) 2018; 10 88_CR38 M Abdel-Basset (88_CR22) 2017; 33 EB Jamkhaneh (88_CR5) 2012; 16 EB Jamkhaneh (88_CR3) 2011; 43 M Aslam (88_CR32) 2018; 10 EB Jamkhaneh (88_CR7) 2013; 25
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IEEE – reference: AiwuZJianguoDHongjunGInterval valued neutrosophic sets and multi-attribute decision-making based on generalized weighted aggregation operatorJ Intell Fuzzy Syst20152962697270610.3233/IFS-151973 – reference: TuranoğluEKayaİKahramanCFuzzy acceptance sampling and characteristic curvesInt J Comput Intell Syst201251132910.1080/18756891.2012.670518 – reference: VenkatehAElangoSAcceptance sampling for the influence of TRH using crisp and fuzzy gamma distributionAryabhatta J Math Inform201461119124 – reference: ChenJExpressions of rock joint roughness coefficient using neutrosophic interval statistical numbersSymmetry20179712310.3390/sym9070123 – reference: Abdel-BassetMMohamedMChangVNMCDA: a framework for evaluating cloud computing servicesFuture Gener Comput Syst201886122910.1016/j.future.2018.03.014 – reference: Abdel-BassetMMulti-criteria group decision making based on neutrosophic analytic hierarchy processJ Intell Fuzzy Syst20173364055406610.3233/JIFS-17981 – reference: JamkhanehEBSadeghpour-GildehBYariGInspection error and its effects on single sampling plans with fuzzy parametersStruct Multidiscip Optim201143455556010.1007/s00158-010-0579-6 – reference: Broumi S et al (2016) Application of Dijkstra algorithm for solving interval valued neutrosophic shortest path problem. In: 2016 IEEE symposium series on computational intelligence (SSCI). IEEE – reference: AslamMA new sampling plan using neutrosophic process loss considerationSymmetry2018105132378720010.3390/sym10050132 – reference: Smarandache F (2010) Neutrosophic logic-a generalization of the intuitionistic fuzzy logic. In: Multispace & multistructure. Neutrosophic transdisciplinarity (100 collected papers of science), vol 4, p 396 – reference: KahramanCBekarETSenvarOKahramanCYanikSA fuzzy design of single and double acceptance sampling plansIntelligent decision making in quality management2016ChamSpringer17921110.1007/978-3-319-24499-0_7 – reference: AslamMDesign of sampling plan for exponential distribution under neutrosophic statistical interval methodIEEE Access20186641536415810.1109/ACCESS.2018.2877923 – reference: Sadeghpour GildehBBaloui JamkhanehEYariGAcceptance single sampling plan with fuzzy parameterIran J Fuzzy Syst201182475528833181260.62089 – reference: ChenJYeJDuSScale effect and anisotropy analyzed for neutrosophic numbers of rock joint roughness coefficient based on neutrosophic statisticsSymmetry201791020810.3390/sym9100208 – reference: Abdel-BassetMGunasekaranMMohamedMA novel method for solving the fully neutrosophic linear programming problemsNeural Comput Appl201810.1007/s00521-018-3404-6 – reference: Abdel-BassetMMohamedMSmarandacheFA hybrid neutrosophic group ANP-TOPSIS framework for supplier selection problemsSymmetry201810622610.3390/sym10060226 – reference: ElangoSVenkateshASivakumarGA fuzzy mathematical analysis for the effect of TRH using acceptance sampling plansInt J Pure Appl Math201711717 – reference: Abdel-BassetMMohamedMSmarandacheFAn extension of neutrosophic AHP–SWOT analysis for strategic planning and decision-makingSymmetry201810411610.3390/sym10040116 – reference: Abdel-BassetMNeutrosophic association rule mining algorithm for big data analysisSymmetry201810410610.3390/sym10040106 – reference: DivyaPQuality interval acceptance single sampling plan with fuzzy parameter using poisson distributionInt J Adv Res Technol201213115125 – reference: ChengS-RHsuB-MShuM-HFuzzy testing and selecting better processes performanceInd Manag Data Syst2007107686288110.1108/02635570710758761 – reference: Wang H et al (2005) Single valued neutrosophic sets. In: Proceedings of 10th 476 international conference on fuzzy theory and technology. Citeseer – reference: BroumiSShortest path problem under bipolar neutrosphic settingAppl Mech Mater2017859596610.4028/www.scientific.net/AMM.859.59 – reference: AslamMRazaMADesign of new sampling plans for multiple manufacturing lines under uncertaintyInt J Fuzzy Syst201810.1007/s40815-018-0560-x – reference: MajumdarPAcharjyaDDehuriSSanyalSNeutrosophic sets and its applications to decision makingComputational intelligence for big data analysis2015ChamSpringer9711510.1007/978-3-319-16598-1_4 – reference: BroumiSShortest path problem under interval valued neutrosophic settingJ Fundam Appl Sci2018104S168174 – reference: JamkhanehEBGildehBSAcceptance double sampling plan using fuzzy poisson distributionWorld Appl Sci J2012161115781588 – reference: JamkhanehEBGildehBSSequential sampling plan using fuzzy SPRTJ Intell Fuzzy Syst201325378579130791721291.62143 – reference: SmarandacheFIntroduction to neutrosophic statistics2014Ann ArborInfinite Study1286.62006 – reference: Abdel-BassetMA hybrid approach of neutrosophic sets and DEMATEL method for developing supplier selection criteriaDes Autom Embed Syst201810.1007/s10617-018-9203-6 – reference: AslamMTwo-stage variables acceptance sampling plans using process loss functionsCommun Stat Theory Methods201241203633364710.1080/03610926.2011.563019 – reference: Abdel-BassetMMohamedMThe role of single valued neutrosophic sets and rough sets in smart city: imperfect and incomplete information systemsMeasurement2018124475510.1016/j.measurement.2018.04.001 – reference: MontgomeryDCIntroduction to statistical quality control2007New YorkWiley0997.62503 – reference: AslamMArifOTesting of grouped product for the Weibull distribution using neutrosophic statisticsSymmetry201810940310.3390/sym10090403 – reference: AfshariRGildehBSSarmadMMultiple deferred state sampling plan with fuzzy parameterInt J Fuzzy Syst201720191376.62123 – reference: Abdel-BassetMThree-way decisions based on neutrosophic sets and AHP-QFD framework for supplier 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SubjectTerms	Binomial distribution Complexity Computational Intelligence Data Structures and Information Theory Engineering Inspection Original Article Sampling Sampling techniques Statistics
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Title	A new attribute sampling plan using neutrosophic statistical interval method
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