Sustainable medical supplier selection based on multi-granularity probabilistic linguistic term sets
Abstract
The sustainable medical supplier selection (SMSS) is an important issue facing the medical industry in the context of sustainable development, which can be regarded as a typical multi-attribute group decision making (MAGDM) problem. In the MAGDM process, linguistic term set (LTS) is particularly natural and convenient for decision makers (DMs) to express evaluation information. Especially, probabilistic linguistic term set (PLTS) is a very critical and effective tool, which can reflect the importance of different linguistic terms. Due to the different preferences and experience of different DMs, they may use multi-granularity probabilistic linguistic term sets (MGPLTSs) to represent different linguistic information. In this article, in order to study the comparison method of MGPLTSs, a new possibility degree formula is firstly proposed and its properties is proved. Then, in order to build a weight model, a possibility degree-based Best-Worst method (BWM) and a probability degree based-maximizing deviation method are established to calculate the subjective weights and objective weights of attributes, respectively. Where after, a MAGDM method is proposed by combining the ELimination Et Choix Traduisant la REalite (ELECTRE) method with Evaluation based on Distance from Average Solution (EDAS) method in the multi-granularity probabilistic linguistic information environment. Finally, the created MAGDM method is applied to the SMSS, and its effectiveness and advantages compared with other existing methods are verified.
First published online 17 January 2022
Keyword : multi-attribute group decision making, sustainable medical supplier selection, multi-granularity probabilistic linguistic term sets, possibility degree, Best-Worst method, Maximizing deviation method, ELECTRE-EDAS method
How to Cite
Liu, P., Wang, X., Wang, P., Wang, F., & Teng, F. (2022). Sustainable medical supplier selection based on multi-granularity probabilistic linguistic term sets. Technological and Economic Development of Economy, 28(2), 381–418. https://doi.org/10.3846/tede.2022.15940
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Gou, X. J., & Xu, Z. S. (2016). Novel basic operational laws for linguistic terms, hesitant fuzzy linguistic term sets and probabilistic linguistic term sets. Information Sciences, 372, 407–427. https://doi.org/10.1016/j.ins.2016.08.034
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Haurant, P., Oberti, P., & Muselli, M. (2011). Multicriteria selection aiding related to photovoltaic plants on farming fields on Corsica island: A real case study using the ELECTRE outranking framework. Energy Policy, 39(2), 676–688. https://doi.org/10.1016/j.enpol.2010.10.040
He T. T., Wei, G. W., Lu, J. P., Wei, C., & Lin, R. (2019). Pythagorean 2-tuple linguistic Taxonomy method for supplier selection in medical instrument industries. International Journal of Environmental Research and Public Health, 16(23), 4875. https://doi.org/10.3390/ijerph16234875
Jia, F., Liu, Y. Y., & Wang, X. Y. (2019). An extended MABAC method for multi-criteria group decision making based on intuitionistic fuzzy rough numbers. Expert Systems with Applications, 127, 241–255. https://doi.org/10.1016/j.eswa.2019.03.016
Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435–451. https://doi.org/10.15388/Informatica.2015.57
Khaksar, E., Abbasnejad, T., Esmaeili, A., & Tamosaitiene, J. (2016). The effect of green supply chain management practices on environmental performance and competitive advantage: A case study of the cement industry. Technological and Economic Development of Economy, 22(2), 293–308. https://doi.org/10.3846/20294913.2015.1065521
Lei, F., Wei, G. W., Gao, H., Wu, J., & Wei, C. (2020). TOPSIS method for developing supplier selection with probabilistic linguistic information. International Journal of Fuzzy Systems, 22(3), 749–759. https://doi.org/10.1007/s40815-019-00797-6
Li, J., Wang, J. Q., & Hu, J. H. (2019a). Multi-criteria decision-making method based on dominance degree and BWM with probabilistic hesitant fuzzy information. International Journal of Machine Learning & Cybernetics, 10(7), 1671–1685. https://doi.org/10.1007/s13042-018-0845-2
Li, J., Fang, H., & Song, W. Y. (2019b). Sustainable supplier selection based on SSCM practices: A rough cloud TOPSIS approach. Journal of Cleaner Production, 222, 606–621. https://doi.org/10.1016/j.jclepro.2019.03.070
Liao, H. C., Jiang, L. S., Lev, B., & Fujita, H. (2019). Novel operations of PLTSs based on the disparity degrees of linguistic terms and their use in designing the probabilistic linguistic ELECTRE III method. Applied Soft Computing, 80, 450–464. https://doi.org/10.1016/j.asoc.2019.04.018
Liao, H. C., Peng, X. Y., & Gou, X. J. (2020). Medical supplier selection with a group decision-making method based on incomplete probabilistic linguistic preference relations. International Journal of Fuzzy Systems, 23(1), 280–294. https://doi.org/10.1007/s40815-020-00885-y
Lin, M. W., Chen, Z. Y., Liao, H. C., & Xu, Z. S. (2019). ELECTRE II method to deal with probabilistic linguistic term sets and its application to edge computing. Nonlinear Dynamics, 96(3), 2125–2143. https://doi.org/10.1007/s11071-019-04910-0
Liu, P. D., & Li, Y. (2018). The PROMTHEE II method based on probabilistic linguistic information and their application to decision making. Informatica, 29(2), 303–320. https://doi.org/10.15388/Informatica.2018.169
Liu, P. D., & Teng, F. (2018). Some Muirhead mean operators for probabilistic linguistic term sets and their applications to multiple attribute decision-making. Applied Soft Computing, 68, 396–431. https://doi.org/10.1016/j.asoc.2018.03.027
Liu, P. D., & Teng, F. (2019). Probabilistic linguistic TODIM method for selecting products through online product reviews. Information Sciences, 485, 441–455. https://doi.org/10.1016/j.ins.2019.02.022
Liu, P. D., Wang, P., & Pedrycz, W. (2020). Consistency- and consensus-based group decision-making method with incomplete probabilistic linguistic preference relations. IEEE Transactions on Fuzzy Systems, 29(9), 2565–2579. https://doi.org/10.1109/TFUZZ.2020.3003501
Liu, P. D, Wang, X. Y., & Teng, F. (2021). Online teaching quality evaluation based on multi-granularity probabilistic linguistic term sets. Journal of Intelligent & Fuzzy Systems, 40(5), 9915–9935. https://doi.org/10.3233/JIFS-202543
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