Simulator selection based on complex probabilistic hesitant fuzzy soft structure using multi-parameters group decision-making
Simulation software replicates the behavior of real electrical equipment using mathematical models. This is efficient not only in regard to time savings but also in terms of investment. It, at large scale for instance airplane pilots, chemical or nuclear plant operators, etc., provides valuable expe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-05-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.2023907?viewType=HTML |
| _version_ | 1797810653184917504 |
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| author | Shahzaib Ashraf Harish Garg Muneeba Kousar Sameh Askar Shahid Abbas |
| author_facet | Shahzaib Ashraf Harish Garg Muneeba Kousar Sameh Askar Shahid Abbas |
| author_sort | Shahzaib Ashraf |
| collection | DOAJ |
| description | Simulation software replicates the behavior of real electrical equipment using mathematical models. This is efficient not only in regard to time savings but also in terms of investment. It, at large scale for instance airplane pilots, chemical or nuclear plant operators, etc., provides valuable experiential learning without the risk of a catastrophic outcome. But the selection of a circuit simulator with effective simulation accuracy poses significant challenges for today's decision-makers because of uncertainty and ambiguity. Thus, better judgments with increased productivity and accuracy are crucial. For this, we developed a complex probabilistic hesitant fuzzy soft set (CPHFSS) to capture ambiguity and uncertain information with higher accuracy in application scenarios. In this manuscript, the novel concept of CPHFSS is explored and its fundamental laws are discussed. Additionally, we investigated several algebraic aspects of CPHFSS, including union, intersections, soft max-AND, and soft min-OR operators, and we provided numerical examples to illustrate these key qualities. The three decision-making strategies are also constructed using the investigated idea of CPHFSS. Furthermore, numerical examples related to bridges and circuit simulation are provided in order to assess the validity and efficacy of the proposed methodologies. The graphical expressions of the acquired results are also explored. Finally, we conclude the whole work. |
| first_indexed | 2024-03-13T07:11:01Z |
| format | Article |
| id | doaj.art-92b7ca376bc64e388c5fbc15f8f0570f |
| institution | Directory Open Access Journal |
| issn | 2473-6988 |
| language | English |
| last_indexed | 2024-03-13T07:11:01Z |
| publishDate | 2023-05-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj.art-92b7ca376bc64e388c5fbc15f8f0570f2023-06-06T01:24:22ZengAIMS PressAIMS Mathematics2473-69882023-05-0188177651780210.3934/math.2023907Simulator selection based on complex probabilistic hesitant fuzzy soft structure using multi-parameters group decision-makingShahzaib Ashraf 0Harish Garg1Muneeba Kousar2Sameh Askar 3Shahid Abbas41. Institute of Mathematics, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan2. School of Mathematics, Thapar Institute of Engineering & Technology (Deemed University), Patiala 147004, Punjab, India 3. Department of Mathematics, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India 4. Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan 5. College of Technical Engineering, The Islamic University, Najaf, Iraq1. Institute of Mathematics, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan6. Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia7. Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, AustraliaSimulation software replicates the behavior of real electrical equipment using mathematical models. This is efficient not only in regard to time savings but also in terms of investment. It, at large scale for instance airplane pilots, chemical or nuclear plant operators, etc., provides valuable experiential learning without the risk of a catastrophic outcome. But the selection of a circuit simulator with effective simulation accuracy poses significant challenges for today's decision-makers because of uncertainty and ambiguity. Thus, better judgments with increased productivity and accuracy are crucial. For this, we developed a complex probabilistic hesitant fuzzy soft set (CPHFSS) to capture ambiguity and uncertain information with higher accuracy in application scenarios. In this manuscript, the novel concept of CPHFSS is explored and its fundamental laws are discussed. Additionally, we investigated several algebraic aspects of CPHFSS, including union, intersections, soft max-AND, and soft min-OR operators, and we provided numerical examples to illustrate these key qualities. The three decision-making strategies are also constructed using the investigated idea of CPHFSS. Furthermore, numerical examples related to bridges and circuit simulation are provided in order to assess the validity and efficacy of the proposed methodologies. The graphical expressions of the acquired results are also explored. Finally, we conclude the whole work.https://www.aimspress.com/article/doi/10.3934/math.2023907?viewType=HTMLcomplex probabilistic hesitant fuzzy soft setsoft max-andsoft min-or operatorspropertiesalgorithmsdecision-making |
| spellingShingle | Shahzaib Ashraf Harish Garg Muneeba Kousar Sameh Askar Shahid Abbas Simulator selection based on complex probabilistic hesitant fuzzy soft structure using multi-parameters group decision-making AIMS Mathematics complex probabilistic hesitant fuzzy soft set soft max-and soft min-or operators properties algorithms decision-making |
| title | Simulator selection based on complex probabilistic hesitant fuzzy soft structure using multi-parameters group decision-making |
| title_full | Simulator selection based on complex probabilistic hesitant fuzzy soft structure using multi-parameters group decision-making |
| title_fullStr | Simulator selection based on complex probabilistic hesitant fuzzy soft structure using multi-parameters group decision-making |
| title_full_unstemmed | Simulator selection based on complex probabilistic hesitant fuzzy soft structure using multi-parameters group decision-making |
| title_short | Simulator selection based on complex probabilistic hesitant fuzzy soft structure using multi-parameters group decision-making |
| title_sort | simulator selection based on complex probabilistic hesitant fuzzy soft structure using multi parameters group decision making |
| topic | complex probabilistic hesitant fuzzy soft set soft max-and soft min-or operators properties algorithms decision-making |
| url | https://www.aimspress.com/article/doi/10.3934/math.2023907?viewType=HTML |
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