Analyzing of process capability indices under uncertain information and hesitancy by using Pythagorean fuzzy sets

Document Type : Original Manuscript

Authors

1 Yıldız Technical University Department of Industrial Engineering

2 Department of Industrial Engineering, Beykent University, Sarıyer, Istanbul

Abstract

Process capability analysis (PCA) is a completely effective statistical tool for ability of a process to meet predetermined specification limits (SLs). Unfortunately, especially the real case problems include many uncertainties, it is one of the critical necessities to define the parameters of PCIs by using crisp numbers. So, the results obtained may be incorrect, if the PCIs are calculated without taking into account the uncertainty. To overcome this problem, the fuzzy set theory (FST) has been successfully used to design of PCA. We also know that fuzzy set extensions have an important role in modelling the case that include uncertainty, incomplete and inconsistent information and they are more powerful than traditional FST to model uncertainty. Defining of main parameters of PCIs such as SLs, mean (µ) and variance (σ2) by using the flexible of fuzzy set extensions rather than precise values due to uncertainty, time, cost, inspectors hesitancy and the results based on fuzzy sets for PCIs contain more, flexible and  sensitive information. In this study, two of well-known PCIs called Cp and Cpk have been re-designed at the first time by using one of fuzzy set extensions named Pythagorean fuzzy sets (PFSs). Defining PCIs with more than one membership function instead of an only one membership function is enabling to evaluate the process more  broadly more flexibility. For this aim, the main parameters of PCIs have been defined  and analyzed by using PFSs. Finally, four new PCIs based on PFSs such as Csp, Cspk, Cfp and Cfpk have been derived. The proposed new PCIs based on PFSs have been also applied on manufacturing process and capability for gears have been analyzed. It is shown that the flexibility of the PFSs on PCIs enables the PCA to give more realistic,  more sensitive, and more comprehensive results.  

Keywords


[1] M. Aghamohagheghi, S. M. Hashemi, R. Tavakkoli-Moghaddam, An advanced decision support framework to assess sustainable transport projects using a new uncertainty modeling tool: Interval-valued Pythagorean trapezoidal fuzzy numbers, Iranian Journal of Fuzzy Systems, 18(1) (2021), 53-73.
[2] M. F. Ak, M. Gul, AHP–TOPSIS integration extended with Pythagorean fuzzy sets for information security risk analysis, Complex and Intelligent Systems, 5(2) (2019), 113-126.
[3] M. Akram, I. Ullah, T. Allahviranloo, S. A. Edalatpanah, Fully Pythagorean fuzzy linear programming problems with equality constraints, Computational and Applied Mathematics, 40(4) (2021), 1-20.
[4] M. Albing, Process capability analysis with focus on indices for one-sided specification limits, Doctoral Dissertation, Lule˚a University of Technology, 2006.
[5] T. T. Allen, Introduction to engineering statistics and six sigma: Statistical quality control and design of experiments and systems, Berlin/Heidelberg: Springer Science and Business Media, 2006.
[6] M. Aslam, M. Albassam, Inspection plan based on the process capability index using the neutrosophic statistical method, Mathematics, 7(7) (2019), 1-10.
[7] K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20(1) (1986), 87-96.
[8] S. Aydin, C. Kahraman, M. Kabak, Development of harmonic aggregation operator with trapezoidal Pythagorean fuzzy numbers, Soft Computing, 24(15) (2020), 11791-11803.
[9] Y. Cao, Z. Wu, T. Liu, Z. Gao, J. Yang, Multivariate process capability evaluation of cloud manufacturing resource based on intuitionistic fuzzy set, The International Journal of Advanced Manufacturing Technology, 84(1-4) (2016), 227-237.
[10] S. M. Chen, T. M. Hung, What can fuzziness do for capability analysis based on fuzzy data, Scientia Iranica, 28(2) (2021), 1049-1064.
[11] E. Haktanr, C. Kahraman, Design for six sigma and process capability using penthagorean fuzzy sets, In Intelligent and Fuzzy Techniques: Smart and Innovative Solutions: Proceedings of the INFUS 2020 Conference, Istanbul, Turkey, July 2020.
[12] E. Haktanr, C. Kahraman, Process design and capability analysis using penthagorean fuzzy sets: Surgical mask production machines comparison, Journal of Intelligent and Fuzzy Systems, 42(1) (2022), 477-489.
[13] G. Hesamian, M. G. Akbari, A process capability index for normal random variable with intuitionistic fuzzy information, Operational Research, 21(2) (2021), 951-964.
[14] C. Kahraman, E. Boltürk, S. C. Onar, B. Oztaysi, Modeling humanoid robots facial expressions using Pythagorean fuzzy sets, Journal of Intelligent and Fuzzy Systems, 39(5) (2020), 6507-6515.
[15] C. Kahraman, A. Parchami, S. Cevik Onar, B. Oztaysi, Process capability analysis using intuitionistic fuzzy sets, Journal of Intelligent and Fuzzy Systems, 32(3) (2017), 1659-1671.
[16] V. E. Kane, Process capability indices, Journal of Quality Technology, 18(1) (1986), 41-52.
[17] İ. Kaya, M. Çolak, A literature review on fuzzy process capability analysis, Journal of Testing and Evaluation, 48(5) (2020), 3963-3985.
[18] İ. Kaya, C. Kahraman, A new perspective on fuzzy process capability indices: Robustness, Expert Systems with Applications, 37(6) (2010), 4593-4600.
[19] İ. Kaya, C. Kahraman, Fuzzy process capability indices with asymmetric tolerances, Expert Systems with Application, 38(12) (2011), 14882-14890.
[20] İ. Kaya, C. Kahraman, Process capability analyses based on fuzzy measurements and fuzzy control charts, Expert Systems with Application, 38(4) (2011), 3172-3184.
[21] S. Kotz, N. L. Johnson, Process capability indices-A review, 1992-2000, Journal of Quality Technology, 34(1) (2002), 2-19.
[22] R. Kumar, S. A. Edalatpanah, S. Jha, R. Singh, A Pythagorean fuzzy approach to the transportation problem, Complex and Intelligent Systems, 5(2) (2019), 255-263.
[23] A. Luqman, M. Akram, J. C. R. Alcantud, Digraph and matrix approach for risk evaluations under Pythagorean fuzzy information, Expert Systems with Applications, 170(1) (2021), 114518.
[24] D. C. Montgomery, Introduction to statistical quality control, Fifth Edition, John Wiley and Sons, 2005.
[25] A. Parchami, S. Ç. Onar, B. Öztayşi, K. Kahraman, Process capability analysis using interval type-2 fuzzy sets, International Journal of Computational Intelligence Systems, 10(1) (2017), 721-733.
[26] X. Peng, Y. Yang, Some results for Pythagorean fuzzy sets, International Journal of Intelligent Systems, 30(11) (2015), 1133-1160.
[27] L. J. Porter, J. S. Oakland, Process capability indices-an overview of theory and practice, Quality and Reliability Engineering International, 7(6) (1991), 437-448.
[28] P. Rani, A. R. Mishra, A. Mardani, An extended Pythagorean fuzzy complex proportional assessment approach with new entropy and score function: Application in pharmacological therapy selection for type 2 diabetes, Applied Soft Computing, 94 (2020), 106441.
[29] O. Senvar, C. Kahraman, Type-2 fuzzy process capability indices for non-normal processes, Journal of Intelligent and Fuzzy Systems, 27(2) (2014), 769-781.
[30] M. Shakeel, S. Abdullah, M. S. Ali Khan, K. Rahman, Averaging aggregation operators with interval Pythagorean trapezoidal fuzzy numbers and their application to group decision making, Punjab University Journal of Mathematics, 50(2) (2020), 147-170.
[31] M. Shakeel, S. Abdullah, M. Shahzad, N. Siddiqui, Geometric aggregation operators with interval-valued Pythagorean trapezoidal fuzzy numbers based on Einstein operations and their application in group decision making, International Journal of Machine Learning and Cybernetics, 10(10) (2019), 2867-2886.
[32] R. R. Yager, Pythagorean fuzzy subsets, in Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, Edmonton, AB, Canada, 2013.
[33] S. Yalçın, İ. Kaya, Design and analysis of process capability indices Cpm and Cpmk by neutrosophic sets, Iranian Journal of Fuzzy Systems, 19(1) (2022), 13-30.
[34] S. Yalçın, İ. Kaya, Analyzing of process capability indices based on neutrosophic sets, Computational and Applied Mathematics, 41 (2022), 287.
[35] S. Yalçın, İ. Kaya, Two-dimensional uncertainty analysis for Cp and Cpk process capability indices, 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), Sakheer, Bahrain, (2022), 419-423.
[36] S. Yalçın, İ. Kaya, Design and analysis of Cpm and Cpmk indices for uncertainty environment by using Pythagorean fuzzy sets, 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Tech[1]nologies (3ICT), Sakheer, Bahrain, (2022), 293-297.
[37] L. A. Zadeh, Fuzzy sets, Information and Control, 8(3) (1965), 338-353.
[38] X. Zhang, Z. Xu, Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets, International Journal of Intelligent Systems, 29(12) (2014), 1061-1078.