New intuitionistic fuzzy parametric divergence measures and score function-based CoCoSo method for decision-making problems
DOI:
https://doi.org/10.31181/dmame0318102022tKeywords:
Intuitionistic fuzzy sets, combined compromise solution, medical decision-making, divergence measure, score functionAbstract
The present study introduces a decision-making approach with the combined compromise solution (CoCoSo) under intuitionistic fuzzy sets (IFSs) named as the IF-CoCoSo method based on proposed divergence measures and score function. The aim of the presented approach is to obtain an effective solution for multi-criteria decision-making problems on IFSs context. In this line, a new procedure is presented to derive the criteria weights using generalized score function and parametric divergence measures of IFSs. To compute the criteria weight, a generalized score function and parametric divergence measures are developed on IFSs and discussed some interesting properties. Further, the presented approach is applied to rank and evaluate the therapies for medical decision making problems, which demonstrates its applicability and feasibility. Finally, comparative and sensitivity analyses are discussed for validating the developed method.
Downloads
References
Ansari, M.D., Mishra, A.R., & Ansari, F.T. (2018). New divergence and entropy measures for intuitionistic fuzzy sets on edge detection. International Journal of Fuzzy Systems, 20(2), 474-487.
Arora, H., & Naithani, A. (2022). Significance of TOPSIS approach to MADM in computing exponential divergence measures for pythagorean fuzzy sets . Decision Making: Applications in Management and Engineering, 5(1), 246-263.
Atanassov, K.T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96.
Bahadori, M., Teymourzadeh, E., Faizy Bagejan, F., Ravangard, R., Raadabadi, M., & Hosseini, S.M. (2018). Factors affecting the effectiveness of quality control circles in a hospital using a combination of fuzzy VIKOR and Grey Relational Analysis. Proceedings of Singapore Healthcare, 27(3), 180-186.
Bai L., Garcia, F.J.S., & Mishra, A.R. (2022), Adoption of the sustainable circular supply chain under disruptions risk in manufacturing industry using an integrated fuzzy decision‑making approach, Operations Management Research, https://doi.org/10.1007/s12063-022-00267-7.
Basile, L. J., Carbonara, N., Pellegrino, R., & Panniello, U. (2022). Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making. Technovation, 8, 102482; https://doi.org/10.1016/j.technovation.2022.102482.
Batool, B., Abosuliman, S. S., Abdullah, S., & Ashraf, S. (2021). EDAS method for decision support modeling under the Pythagorean probabilistic hesitant fuzzy aggregation information. Journal of Ambient Intelligence and Humanized Computing, https://doi.org/10.1007/s12652-021-03181-1.
Bhattacharya, P. P., Bhattacharya, K., & De, S. K. (2022). A study on pollution sensitive sponge iron based production transportation model under fuzzy environment. Decision Making: Applications in Management and Engineering, 5(1), 225-245.
Bozanic, D., Tešić, D., & Kočić, J. (2019). Multi-criteria FUCOM – Fuzzy MABAC model for the selection of location for construction of single-span bailey bridge. Decision Making: Applications in Management and Engineering, 2(1), 132-146.
Chaira, T. (2014). Enhancement of medical images in an Atanassov's't intuitionistic fuzzy domain using an alternative intuitionistic fuzzy generator with application to image segmentation. Journal of Intelligent & Fuzzy Systems, 27(3), 1347-1359.
Chen, S.M., & Tan, J.M. (1994), Handling multi-criteria fuzzy decision making problems based on vague set theory. Fuzzy Sets Syst. 67, 163–172.
Chen, T.-Y. (2013). An interactive method for multiple criteria group decision analysis based on interval type-2 fuzzy sets and its application to medical decision making. Fuzzy Optimization and Decision Making, 12(3), 323-356.
Chen, T.-Y. (2015). Likelihoods of interval type-2 trapezoidal fuzzy preference relations and their application to multiple criteria decision analysis. Information Sciences, 295, 303-322.
Chen, T.-Y. (2017). A likelihood-based assignment method for multiple criteria decision analysis with interval type-2 fuzzy information. Neural Computing and Applications, 28(12), 4023-4045.
Chen, T.-Y., Chang, C.-H. & Rachel Lu, J.-F. (2013). The extended QUALIFLEX method for multiple criteria decision analysis based on interval type-2 fuzzy sets and applications to medical decision making. European Journal of Operational Research, 226(3), 615-625.
Greene, S.M., Tuzzio, L. & Cherkin, D. (2012). A framework for making patient-centered care front and center. The Permanente journal, 16(3), 49-53.
Hezam, I. M., Mishra, A.R., Rani, P., Cavallaro, F., Saha, A., Ali, J., Strielkowski, W., & Štreimikienė, D. (2022). A hybrid intuitionistic fuzzy-MEREC-RS-DNMA method for assessing the alternative fuel vehicles with sustainability perspectives. Sustainability, 14, 5463. https://doi.org/10.3390/su14095463
Honarbakhsh, M., Jahangiri, M., Ghaem, H., & Farhadi, P. (2018). Respiratory Protection Program Compliance in Iranian Hospitals: Application of Fuzzy Analytical Hierarchy Process. Workplace Health & Safety, 66(4), 173-182.
Hong, D.H., & Choi, C.H. (2000). Multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets and Systems, 114(1), 103-113.
Hsieh, M.-C., Wang, E.M.-Y., Lee, W.-C., Li, L.-W., Hsieh, C.-Y., Tsai, W., Wang, C.-P., Huang, J.-L. & Liu, T.-C. (2018). Application of HFACS, fuzzy TOPSIS, and AHP for identifying important human error factors in emergency departments in Taiwan. International Journal of Industrial Ergonomics, 67, 171-179.
Hu, J., Chen, P., & Yang, Y. (2019c). An Interval Type-2 Fuzzy Similarity-Based MABAC Approach for Patient-Centered Care. Mathematics, 7(2), 140.
Hu, J., Chen, P., Yang, Y., Liu, Y., & Chen, X. (2019b). The Fruit Fly Optimization Algorithms for Patient-Centered Care Based on Interval Trapezoidal Type-2 Fuzzy Numbers. International Journal of Fuzzy Systems, 21, 1270-1287.
Hu, J., Pan, L., Yang, Y., & Chen, H. (2019a). A group medical diagnosis model based on intuitionistic fuzzy soft sets. Applied Soft Computing, 77, 453-466.
Joshi, D., & Kumar, S. (2014). Intuitionistic fuzzy entropy and distance measure based TOPSIS method for multi-criteria decision making. Egyptian Informatics Journal, 15(2), 97-104.
Khatibi, V., & Montazer, G.A. (2009). Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition. Artificial intelligence in medicine, 47(1), 43-52.
Kirkire, M.S., Rane, S.B., & Singh, S.P. (2018). Integrated SEM-FTOPSIS framework for modeling and prioritization of risk sources in medical device development process. Benchmarking: An International Journal, 25(1), 178-200.
Kumari, R., &Mishra A. R. (2020). Multi-criteria COPRAS method based on parametric measures for intuitionistic fuzzy sets: application of green supplier selection. Iran J Sci Technol Trans Electr Engg., 44, 1645-1662.
Kushwaha, D. K., Panchal, D., & Sachdeva, A. (2020). Risk analysis of cutting system under intuitionistic fuzzy environment. Reports in Mechanical Engineering, 1(1), 162-173.
Lee, Y.-Y., & Lin, J. L. (2010). Do patient autonomy preferences matter? Linking patient-centered care to patient–physician relationships and health outcomes. Social Science & Medicine, 71(10), 1811-1818.
Li, D.-P., He, J.-Q., Cheng, P.-F., Wang, J.-Q. & Zhang, H.-Y. (2018). A Novel Selection Model of Surgical Treatments for Early Gastric Cancer Patients Based on Heterogeneous Multicriteria Group Decision-Making. Symmetry, 10(6), 223.
Liu, H.-W., & Wang, G.-J. (2007). Multi-criteria decision-making methods based on intuitionistic fuzzy sets. European Journal of Operational Research, 179(1), 220-233.
Liu, P., Rani, P., & Mishra, A.R. (2021). A novel Pythagorean fuzzy combined compromise solution framework for the assessment of medical waste treatment technology, Journal of Cleaner Production 292, https://doi.org/10.1016/j.jclepro.2021.126047.
Luo, X., & Wang, X.Z. (2017). Extended VIKOR Method for Intuitionistic Fuzzy Multiattribute Decision-Making Based on a New Distance Measure. Mathematical Problems in Engineering, Article ID 4072486, https://doi.org/10.1155/2017/4072486.
Lutz, B. J., & Bowers, B. J. (2000). Patient-centered care: understanding its interpretation and implementation in health care. Research and Theory for Nursing Practice, 14(2), 165-183.
Ma, Y.-X., Wang, J.-Q., Wang, J. & Wu, X.-H. (2017). An interval neutrosophic linguistic multi-criteria group decision-making method and its application in selecting medical treatment options. Neural Computing and Applications, 28(9), 2745-2765.
Malekpoor, H., Mishra, N. & Kumar, S. (2022). A novel TOPSIS–CBR goal programming approach to sustainable healthcare treatment. Annals of Operations Research, 312, 1403-1425.
Mishra, A. R., & Rani P. (2021). Assessment of sustainable third party reverse logistic provider using the single-valued neutrosophic combined compromise solution framework. Cleaner and Responsible Consumption, 02, https://doi.org/10.1016/j.clrc.2021.100011.
Mishra, A. R., Rani, P., Saha, A., Hezam, I.M., Pamucar, D., & Marinivic, M. (2022a). Assessing the Adaptation of Internet of Things (IoT) Barriers for Smart Cities’ Waste Management Using Fermatean Fuzzy Combined Compromise Solution Approach. IEEE Access, 10, 37109-37130.
Mishra, A. R., Rani, P., Cavallaro, F., & Mardani, A (2022b). A similarity measure-based Pythagorean fuzzy additive ratio assessment approach and its application to multi-criteria sustainable biomass crop selection. Applied Soft Computing, 125, 109201, https://doi.org/10.1016/j.asoc.2022.109201
Mishra, A. R., Chen, S.-M., & Rani, P. (2022c). Multi-attribute decision making based on Fermatean hesitant fuzzy sets and modified VIKOR method, Information Sciences, 607, 1532–1549.
Mishra, A.R., Kumari, R. & Sharma, D. (2019). Intuitionistic fuzzy divergence measure-based multi-criteria decision-making method. Neural Computing and Applications, 31, 2279-2294.
Mishra, A.R., Singh, R.K. & Motwani, D. (2019a). Multi-criteria assessment of cellular mobile telephone service providers using intuitionistic fuzzy WASPAS method with similarity measures. Granular Computing, 04, 511-529.
Montes, I., Pal, N.R., Janiš, V. & Montes, S. (2015). Divergence measures for intuitionistic fuzzy sets. IEEE Transactions On Fuzzy Systems, 23(2), 444-456.
Narang, M., Joshi, M. C., Bisht, K., & Pal, A. (2022). Stock portfolio selection using a new decision-making approach based on the integration of fuzzy CoCoSo with Heronian mean operator. Decision Making: Applications in Management and Engineering, 5(1), 90-112.
Pal, N.R. (1993). Some new information measures for fuzzy-sets. Information Sciences, 67(3), 209-228.
Parkash, O., & Kumar, M. (2011). Two new symmetric divergence measures and information inequalities. International Journal of Mathematics and Applications, 4, 165-179.
Pelzang, R. (2010). Time to learn: understanding patient-centred care. British journal of nursing, 19(14), 912-917.
Rahman, K. (2022). Decision-Making Problem Based on Confidence Intuitionistic Trapezoidal Fuzzy Einstein Aggregation Operators and Their Application. New Mathematics and Natural Computation 18(1), 219-250.
Rani, P., Mishra, A.R., Ansari, M.D., & Ali, J. (2021). Assessment of performance of telecom service providers using intuitionistic fuzzy grey relational analysis framework (IF-GRA). Soft Computing, 25, 1983-1993.
Rani, P., & Mishra, A.R. (2020). Novel single-valued neutrosophic combined compromise solution approach for sustainable waste electrical and electronics equipment recycling partner selection. IEEE Transactions of Engineering Management, https://doi.org/10.1109/TEM.2020.3033121.
Rani, P., Mishra, A. R., & Mardani A. (2020), 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, 106441, https://doi.org/10.1016/j.asoc.2020.106441.
Rudnik, K., Bocewicz, G., Kucińska-Landwójtowicz, A., & Czabak-Górska, I. D. (2021). Ordered fuzzy WASPAS method for selection of improvement projects. Expert Systems with Applications, 169, https://doi.org/10.1016/j.eswa.2020.114471.
Shang, X.-G., & Jiang, W.-S. (1997). A note on fuzzy information measures. Pattern Recognition Letters, 18(5), 425-432.
Son, L. H., Ngan, R. T., Ali, M., Fujita, H., Abdel-Basset, M., Giang, N. L., Manogaran, G., & Priyan, M. K. (2020). A New Representation of Intuitionistic Fuzzy Systems and Their Applications in Critical Decision Making. IEEE Intelligent Systems 35(1), 6-17.
Stanujkić, D., & Karabašević, D. (2018). An extension of the WASPAS method for decision-making problems with intuitionistic fuzzy numbers: a case of website evaluation. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 29-39.
Szmidt, E. & Kacprzyk, J. (2001). Intuitionistic fuzzy sets in some medical applications. In: International Conference on Computational Intelligence (pp. 148-151): Springer.
Szmidt, E. & Kacprzyk, J. (2004). A similarity measure for intuitionistic fuzzy sets and its application in supporting medical diagnostic reasoning. In: L. Rutkowski, J. Siekmann, R. Tadeusiewicz & L.A. Zadeh, Artificial Intelligence and Soft Computing - Icaisc 2004 (Vol. 3070, pp. 388-393).
Tavana, M., Shaabani, A., Caprio, D. D., & Bonyani, A. (2021). An integrated group fuzzy best-worst method and combined compromise solution with Bonferroni functions for supplier selection in reverse supply chains. Cleaner Logistics and Supply Chain, 2, https://doi.org/10.1016/j.clscn.2021.100009.
Thokala, P., Devlin, N., Marsh, K., Baltussen, R., Boysen, M., Kalo, Z., Longrenn, T., Mussen, F., Peacock, S., Watkins, J., & Ijzerman, M. (2016). Multiple criteria decision analysis for health care decision making—An introduction: Report 1 of the ISPOR MCDA emerging good practices task force. Value in Health, 19(1), 1-13.
Torğul, B., Demiralay, E., & Paksoy, T. (2022). Training aircraft selection for department of flight training in fuzzy environment. Decision Making: Applications in Management and Engineering, 5(1), 264-289.
Vahdani, B., & Hadipour, H. (2011). Extension of the ELECTRE method based on interval-valued fuzzy sets. Soft Computing, 15(3), 569-579.
Vlachos, I.K., & Sergiadis, G.D. (2007). Intuitionistic fuzzy information–applications to pattern recognition. Pattern Recognition Letters, 28(2), 197-206.
Wang, C.-Y., & Chen, S.-M. (2018). A new multiple attribute decision making method based on linear programming methodology and novel score function and novel accuracy function of interval-valued intuitionistic fuzzy values. Information Sciences, 438, 145-155.
Wei, P., & Ye, J. (2010). Improved intuitionistic fuzzy cross-entropy and its application to pattern recognitions. In: 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering (pp. 114-116): IEEE.
Wen, Z., Liao, H., Zavadskas, E. K., & Al-Barakati, A. (2019). Selection third-party logistics service providers in supply chain finance by a hesitant fuzzy linguistic combined compromise solution method. Economic Research-Ekonomska Istraživanja, 32(1), 4033-4058.
Xiao, F. (2018). A novel multi-criteria decision making method for assessing health-care waste treatment technologies based on D numbers. Engineering Applications of Artificial Intelligence, 71, 216-225.
Xiao, F. (2019). Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy. Information Fusion, 46, 23-32.
Xu, Z.S. (2007). Intuitionistic fuzzy aggregation operators. IEEE Trans Fuzzy Syst., 15(6), 1179–1187.
Yager, R.R. (2004). OWA aggregation over a continuous interval argument with applications to decision making. Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics, 34(5), 1952-1963.
Yazdani, M., Wen, Z., Liao, H., Banaitis, A., & Turskis, Z. (2019b). A grey combined compromise solution (CoCoSo-G) method for supplier selection in construction management”, Journal of Civil Engineering and Management, 25(8), 858-874.
Yazdani, M., Zarate, P., Zavadskas, E. K., & Turskis, Z. (2019a). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems”, Management Decision, 57(9), 2501-2519.
Zadeh, L.A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Zeng, S., Chen, S.-M., & Kuo, L.-W. (2019). Multiattribute decision making based on novel score function of intuitionistic fuzzy values and modified VIKOR method. Information Sciences, 488, 76-92.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Decision Making: Applications in Management and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
