Innovative Bipolar Fuzzy Sine Trigonometric Aggregation Operators and SIR Method for Medical Tourism Supply Chain

Abstract

Bipolar fuzzy sets (BFSs) are effective tool for dealing with bipolarity and fuzziness. The sine trigonometric functions having two significant features, namely, periodicity and symmetry about the origin, are helping in decision analysis and information analysis. Taking the advantage of sine trigonometric functions and significance of BFSs, innovative sine trigonometric operational laws (STOLs) are proposed. New aggregation operators (AOs) are developed based on proposed operational laws to aggregate bipolar fuzzy information. Certain characteristics of these operators are also discussed, such as boundedness, monotonicity, and idempotency. Moreover, a modified superiority and inferiority ranking (SIR) method is proposed to cope with multicriteria group decision-making (MCGDM) with bipolar fuzzy (BF) information. To exhibit the relevance and feasibility of this methodology, a robust application of best medical tourism supply chain is presented. Finally, a comprehensive comparative and sensitivity analysis is evaluated to validate the efficiency of suggested methodology.

Analyzing the effect of different types of pollution with bipolar complex fuzzy power Bonferroni mean operators

“…The results indicate that existing theories, for example, Hu et al ( 2019), Riaz et al (2022), Wei et al (2018), and Liu and Li (2017), cannot solve the DM issue because the data form the structure of the BCFS. Hu et al (2019) can merely solve the data in the model of CFS and cannot tackle the negative aspects, while Riaz et al (2022) and Wei et al (2018) merely resolve the information in the setting of the BFS and cannot overcome the second dimension, and Liu and Li (2017) merely resolve the information in the model of IVIFS and cannot address the second dimension and negative aspects.…”

“…The results indicate that existing theories, for example, Hu et al ( 2019), Riaz et al (2022), Wei et al (2018), and Liu and Li (2017), cannot solve the DM issue because the data form the structure of the BCFS. Hu et al (2019) can merely solve the data in the model of CFS and cannot tackle the negative aspects, while Riaz et al (2022) and Wei et al (2018) merely resolve the information in the setting of the BFS and cannot overcome the second dimension, and Liu and Li (2017) merely resolve the information in the model of IVIFS and cannot address the second dimension and negative aspects. Furthermore, the results show that BM AO (BCF Bonferroni mean AO) (Mahmood et al, 2022b) for the BCFS and the DM approach handle the data and give us the result that M BCFS−4 is the finest choice, but anyone can say that this decision or selection is biased and not fair because of the weight vector that the decision analyst provides to each attribute on his own choice, for example, in this case, we take the weight vector (0.08, 0.06, 0.06, 0.8) and u v 2.…”

“…The introduced PBM operators (BCFPBM, BCFWPBM, BCFPGBM, and BCFWPGBM) and a DM procedure in the environment of the BCFS are more effective than other prevailing operators. We now take prevailing theories, including the CF power and DM procedure defined by Hu et al (2019), sin trigonometric AOs and the DM procedure in the environment of the BF set defined by Riaz et al (2022), Hamacher AOs and the DM procedure in the environment of the BF set established by Wei et al (2018), PBM AOs and the DM procedure in the setting of the interval-valued intuitionistic FS (IVIFS) introduced by Liu and Li (2017), and BM AOs and the DM procedure for the BCFS introduced by Mahmood et al (2022b). We consider the data provided in Table 1 and aggregate it with the assistance of these prevailing theories and the operators and the DM procedure introduced in this article.…”

“…The multi-criteria DM (MCDM) technique for BF was created by Rajalakshmi and Mary (2022) and Garai et al (2022). Riaz et al (2022) defined sine trigonometric (ST) aggregation operators (AOs) for BF sets. Jana et al (2019) introduced BF Dombi AOs (DAOs), and Wei et al (2018) introduced BF Hamacher AOs (HAOs).…”

Cubic Intuitionistic Fuzzy Topology with Application to Uncertain Supply Chain Management

“…Bairagi [59] developed a novel MCDM model for warehouse location selection in supply chain management. Recently, some applications of fuzzy modeling have been developed, such as uncertain supply chains [60], medical tourism supply chains [61], sustainable plastic recycling processes [62], and pattern recognition [63].…”