Power and Energy Transformation: Multi-Criteria Decision-Making Utilizing Complex q-Rung Picture Fuzzy Generalized Power Prioritized Yager Operators

Authors

DOI:

https://doi.org/10.31181/sor21202525

Keywords:

Power and Energy, Complex q-rung Picture Fuzzy Sets, Yager's Operations, MCDM, Fuzzy logic

Abstract

Existing studies in decision-making often face limitations in adequately handling complex and uncertain environments. This study addresses these shortcomings by introducing novel operations and aggregation techniques tailored for complex q-rung picture fuzzy numbers (Cq-RPFNs). Building upon the drawbacks of conventional approaches, we extend Yeager's operations to Cq-RPFNs, enabling comprehensive operations such as addition, multiplication, scalar product, and scalar power. Additionally, two aggregation operators, the Complex q-rung Picture Fuzzy Generalized Power Prioritized Yager Weighted Average, and the Complex q-rung Picture Fuzzy Generalized Power Prioritized Yager Weighted Geometric are developed to overcome the limitations of existing aggregation methods. Rigorous analysis of these operators for properties such as idempotency and monotonicity ensures their robustness in decision-making processes. Through a comprehensive multi-criteria decision-making method utilizing these operators, decision-makers are empowered to navigate complex decision landscapes effectively. Comparative evaluations against existing alternatives highlight the superiority and effectiveness of the proposed approaches. A case study focusing on India's Power and Energy Sector in 2024 demonstrates practical application, supported by numerical examples that validate the methodologies' efficacy. This study addresses the drawbacks of existing approaches and provides valuable insights and tools for decision-makers operating in complex and uncertain domains.

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References

Bharatee, A., Ray, P., Subudhi, B.,&Ghosh, A. (2022). Power management strategies in a hybrid energy storage system integrated ac/dc microgrid: A review. Energies, 15(19), 7176. https://doi.org/10.3390/en15197176

Ghasemi, N., Ghanbari, M., & Ebrahimi, R. (2023). Intelligent and optimal energy management strategy to control the micro-grid voltage and frequency by considering the load dynamics and transient stability. International Journal of Electrical Power and Energy Systems, 145, 108618. https://doi.org/10.1016/j.ijepes.2022.108618

Bao, Y., Li, R., Yang, X., Saini, G., Krishna, P., & Wang, G. (2023). Optimal planning and multicriteria decision-making for effective design and performance of hybrid microgrid integrated with energy management strategy. Sustainable Energy Technologies and Assessments, 56, 103074. https://doi.org/10.1016/j.seta.2023.103074

Karunathilake, E., Le, A., Heo, S., Chung, Y., & Mansoor, S. (2023). The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture, 13(8), 1593. https://doi.org/10.3390/agriculture13081593

Liu, J., Ma, L., & Wang, Q. (2023). Energy management method of integrated energy system based on collaborative optimization of distributed flexible resources. Energy, 264, 125981. https://doi.org/10.1016/j.energy.2022.125981

Kandpal, V., Jaswal, A., Santibanez Gonzalez, E., & Agarwal, N. (2024). Environmental impact assessment and sustainable energy transition. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-52943-6 9

Yu, S., You, L., & Zhou, S. (2023). A review of optimization modeling and solution methods in renewable energy systems. Frontiers of Engineering Management, 10(4), 640–671. https://doi.org/10.1007/s42524-023-0271-3

Chen, X., Chen, C., Tian, G., Yang, Y., Yong, X., & Zhao, Y. (2023). Comprehensive evaluation research of hybrid energy systems driven by renewable energy based on fuzzy multi-criteria decision-making. Available at SSRN, 4579594. https://doi.org/10.3389/fenrg.2023.1294391

Flickr. (n.d.). https://www.flickr.com/photos/192315865%40N02/50997746456/

Led street light [[accessed Feb 6, 2024]]. (n.d.). https://cn.kompass.com/p/huaxia-led-videodisplay-screen-and-led-lighting/cn228934/led-street-light-led-wall-washer-light-led-pixellight-strips/cd2c66f7-a1bd-4b0f-9211-6a8cfad71b79/

Wave swell energy ltd [[accessed Feb 8, 2024]]. (n.d.). https://tethys.pnnl.gov/organization/wave-swell-energy-ltd

Model, W. E. M. 3. (n.d.). Wave energy machine 3d model [[accessed Feb 8, 2024]]. https://free3d.com/3d-model/wave-energy-machine-9583.html

Grid: Integrating large-scale battery energy into the electricity network, P. (n.d.). Power grid: Integrating large-scale battery energy into the electricity network [[accessed Feb 8, 2024]]. https://roboticsandautomationnews.com/2018/07/17/power- grid- integrating-large- scalebattery-energy-into-the-electricity-network/18337/

Tech, T. R. O. I. I. S. G. (n.d.). The role of IoT in smart grid tech [[accessed Feb 9, 2024]]. https://www.intuz.com/blog/the-role-of-iot-in-smart-grid-tech

Energy. (n.d.). 8 ways of generating electricity at home [[accessed Feb 9, 2024]]. https://diynewtech.com/8-ways-of-generating-electricity-at-home/

Leadership, M. O. D. M.-T. I. C. (n.d.). Micro offsets driving macro-transformation in climate leadership [[accessed Feb 9, 2024]]. https://enkingint.org/micro-offsets-driving-macrotransformation-in-climate-leadership/

M., F. B., Petchimuthu, S., Kamacı, H., & Senapati, T. (2024). Evaluation of artificial intelligence based solid waste segregation technologies through multi-criteria decision-making and complex q-rung picture fuzzy frank aggregation operators. Engineering Applications of Artificial Intelligence, 133, 108154. https://doi.org/10.1016/j.engappai.2024.108154

Senapati, S. P. B. P. F. B. M. T. (2024). Exploring pharmacological therapies through complex qrung picture fuzzy aczel–alsina prioritized ordered operators in adverse drug reaction analysis. Engineering Applications of Artificial Intelligence, 133, 107996. https://doi.org/10.1016/j.engappai.2024.107996

Subramanian Petchimuthua. Fathima Banu M. S. Thiruvazhimarba Pillaib, T. S. (n.d.). Advancing greenhouse gas emission reduction strategies: Integrating multi-criteria decision-making with complex q-rung picture fuzzy Sugeno-Weber operators [communicated].

Pamucar, S. P. F. B. M. M. R. D. (n.d.). Water resource management optimization model for decision analytics using complex q-rung picture fuzzy information and yager power operators [communicated].

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1007/978-1-4899-1633-4 2

Atanassov, K. T. (1989). More on intuitionistic fuzzy sets. Fuzzy sets and systems, 33(1), 37–45. https://doi.org/10.1016/0165-0114(89)90215-7

Yager, R. (1994). Aggregation operators and fuzzy systems modeling. Fuzzy Set Syst, 67(2), 129–145. https://doi.org/10.1016/0165-0114(94)90082-5

Yager, R. R. (2016). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005

Cuong, B. C., & Kreinovich, V. (2013). Picture fuzzy sets new concepts for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013), 1–6. https://doi.org/10.1109/WICT.2013.7113099

Cuong, B. C. (2019). Pythagorean picture fuzzy sets, part 1-basic notions. Journal of Computer Science and Cybernetics, 35(4), 293–304. https://doi.org/10.15625/1813-9663/35/4/13898

Liu, Z., Sun, Y., Xing, C., Liu, J., He, Y., Zhou, Y., & Zhang, G. (2022). Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives. Energy and AI, 100195. https://doi.org/10.1016/j.egyai.2022.100195

Ramot, D., Milo, R., Friedman, M., & Kandel, A. (2002). Complex fuzzy sets. IEEE transactions on fuzzy systems, 10(2), 171–186. https://doi.org/10.1109/91.995119

Alkouri, A. M. J. S., & Salleh, A. R. (2012). Complex intuitionistic fuzzy sets. AIP conference proceedings, 1482, 464–470. https://doi.org/10.1063/1.4757515

Ullah, K., Mahmood, T., Ali, Z., & Jan, N. (2020). On some distance measures of complex Pythagorean fuzzy sets and their applications in pattern recognition. Complex Intelligent Systems, 6, 15–27. https://doi.org/10.1007/s40747-019-0103-6

Liu, P., Ali, Z., Mahmood, T., & Hassan, N. (2020). Group decision-making using complex q-rung orthopair fuzzy Bonferroni mean. International Journal of Computational Intelligence Systems, 13(1), 822. https://doi.org/10.2991/ijcis.d.200514.001

Akram, M., Bashir, A., & Garg, H. (2020). Decision-making model under complex picture fuzzy hamacher aggregation operators. Computational and Applied Mathematics, 39, 1–38. https://doi.org/10.1007/s40314-020-01251-2

Akram, M., Bashir, A., & Edalatpanah, S. (2021). A hybrid decision-making analysis under complex q-rung picture fuzzy einstein averaging operators. Computational and Applied Mathematics, 40, 1–35. https://doi.org/10.1007/s40314-021-01651-y

Dagar, A., Mittal, A., Singh, A., Giri, S., & Rathee, S. (2024). Electrical power plant decision making by utilizing ifs weighted averaging operator. 2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), 1–4. https://doi.org/10.1109/IITCEE59897.2024.10467655

Mao, Q., Fan, J., Lv, J., Gao, Y., Chen, J., & Guo, M. (2024). A decision framework of offshore photovoltaic power station site selection based on Pythagorean fuzzy ELECTRE-III method. Journal of Renewable and Sustainable Energy, 16(2). https://doi.org/10.1063/5.0191823

Unver, M. (2024). Improved cosine similarity measures and extended topsis for q-rung orthopair fuzzy sets: Applications in green technology selection. 10.32388/EOGFR4.3

Mishra, A., Alrasheedi, M., Lakshmi, J., & Rani, P. (2024). Multi-criteria decision analysis model using the q-rung orthopair fuzzy similarity measures and the copras method for electric vehicle charging station site selection. Granular Computing, 9(1), 1–20. https://doi.org/10.1007/s41066-023-00447-1

Senapati, T., & Chen, G. (2022). Picture fuzzy WASPAS technique and its application in multicriteria decision-making. Soft Computing, 26, 4413–4421. https://doi.org/10.1007/s00500-022-06835-0

Kahraman, C. (2024). Proportional picture fuzzy sets and their AHP extension: Application to waste disposal site selection. Expert Systems with Applications, 238, 122354. https://doi.org/10.1016/j.eswa.2023.122354

Hussain, A., Ullah, K., Senapati, T., & Moslem, S. (2023). Complex spherical fuzzy Aczel-Alsina aggregation operators and their application in assessment of electric cars. Heliyon, 9, e18100. https://www.cell.com/heliyon/pdf/S2405-8440(23)05308-2.pdf

Khan, M., Jan, S., Jan, R., Senapati, T., & Moslem, S. (2023). Complex interval-valued intuitionistic fuzzy decision support system with application to covid-19 healthcare facilities. Complex & Intelligent Systems, 1–30. https://doi.org/10.1007/s40747-023-01090-8

Kumar, S.-M., & Chen, S. (2024). Multiattribute decision making based on q-rung orthopair fuzzy yager prioritized weighted arithmetic aggregation operator of q-rung orthopair fuzzy numbers. Information Sciences, 657, 119984. https://doi.org/10.1016/j.ins.2023.119984

Liu, P., Shahzadi, G., & Akram, M. (2020). Specific types of q-rung picture fuzzy yager aggregation operators for decision-making. International Journal of Computational Intelligence Systems, 13(1), 1072–1091. https://doi.org/10.2991/ijcis.d.200717.001

Javeed, S., Javed, M., Shafique, I., Shoaib, M., Khan, M., Cui, L., Askar, S., & Alshamrani, A. (2024). Complex q-rung orthopair fuzzy yager aggregation operators and their application to evaluate the best medical manufacturer. Applied Soft Computing, 157, 111532. https://doi.org/10.1016/j.asoc.2024.111532

Wang, W., & Feng, Y. (2024). Group decision making based on generalized intuitionistic fuzzy yager weighted heronian mean aggregation operator. Int. J. Fuzzy Syst. https://doi.org/10.1007/s40815-023-01672-1

Yager, R. R. (2001). The power average operator. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 31(6), 724–731. https://doi.org/10.1109/3468.983429

Jana, C., Garg, H., & Pal, M. (2023). Multi-attribute decision making for power Dombi operators under Pythagorean fuzzy information with MABAC method. Journal of Ambient Intelligence and Humanized Computing, 14(8), 10761–10778. https://doi.org/10.1007/s12652-022-04348-0

Ma, L., Jabeen, K., Karamti,W., Ullah, K., Khan, Q., Garg, H., & Yin, S. (2024). Aczel-Alsina power bonferroni aggregation operators for picture fuzzy information and decision analysis. Complex & Intelligent Systems, 1–24. https://doi.org/10.1007/s40747-023-01287-x

Dhankhar, C., & Kumar, K. (2023). Multi-attribute decision making based on the q-rung orthopair fuzzy yager power weighted geometric aggregation operator of q-rung orthopair fuzzy values. Granular Computing, 8(5), 1013–1025. https://doi.org/10.1007/s41066-023-00367-0

Liu, P., Ali, Z., & Mahmood, T. (2024). Schweizer-sklar power aggregation operators based on complex intuitionistic fuzzy information and their application in decision-making. Complex & Intelligent Systems, 1–18. https://doi.org/10.1007/s40747-023-01331-w

Yager, R. (2008). Prioritized aggregation operators. International Journal of Approximate Reasoning, 48(1), 263–274. https://doi.org/10.1016/j.ijar.2007.08.009

Kumar, K., & Chen, S. (2024). Multiattribute decision-making based on q-rung orthopair fuzzy yager prioritized weighted arithmetic aggregation operator of q-rung orthopair fuzzy numbers. Information Sciences, 657, 119984. https://doi.org/10.1016/j.ins.2023.119984

Neelam, R., Bhardwaj, & Arora, R. e. a. (2024). Linguistic q-rung orthopair fuzzy yager prioritized weighted geometric aggregation operator of linguistic q-rung orthopair fuzzy numbers and its application to multiattribute group decision-making. Granul. Comput., 9, 38. https://doi.org/10.1007/s41066-024-00460-y

Garg, H., & Rani, D. (2023). New prioritized aggregation operators with priority degrees among priority orders for complex intuitionistic fuzzy information. Journal of Ambient Intelligence and Humanized Computing, 14(3), 1373–1399. https://doi.org/10.1007/s12652-021-03164-2

Li, L., Zhang, R.,Wang, J., Shang, X., & Bai, K. (2018). A novel approach to multi-attribute group decision-making with q-rung picture linguistic information. Symmetry, 10(5), 172. https://doi.org/10.3390/sym10050172

Britannica, T. E. o. E. (2024). Hermann minkowski [Encyclopedia Britannica]. https://www.britannica.com/biography/Hermann-Minkowski

Of New, M., & Renewable Energy (MNRE), G. o. I. (n.d.). Renewable energy in india [[accessed

May 25, 2024]]. https://mnre.gov.in

Stock, R.,&Sovacool, B. (2024). Blinded by sunspots: Revealing the multidimensional and intersectional inequities of solar energy in india. Global Environmental Change, 84, 102796. https://doi.org/10.1016/j.gloenvcha.2023.102796

Husain, A., Hasan, M., Khan, Z., & Asjad, M. (2024). A robust decision-making approach for the selection of an optimal renewable energy source in india. Energy Conversion and Management, 301, 117989. https://doi.org/10.1016/j.enconman.2023.117989

Kumar, M., Tiwari, R., Kumar, K., Rautela, K., & Safi, S. (2024). Quantitative analysis of hydropower potential in the upper beas basin using geographical information system and mike 11 nedbor afrstromnings model (nam). Ecohydrology, e2618. https://doi.org/10.1002/eco.2618

Mohan, I., Panda, A., Volli, V., & Kumar, S. (2024). An insight on upgrading of biomass pyrolysis products and utilization: Current status and future prospect of biomass in india. Biomass Conversion and Biorefinery, 14(5), 6185–6203. https://doi.org/10.1007/s13399-022-02833-2

DGiri, P., Paul, S., & Debnath, B. (2024). A fuzzy graph theory and matrix approach (fuzzy gtma) to select the best renewable energy alternative in india. Applied Energy, 358, 122582. https://doi.org/10.1016/j.apenergy.2023.122582

Shift, C. M. (2024). Ev battery recycling in india: Opportunities and challenges [[accessed May 25, 2024]]. https://cleanmobilityshift.com/ecosystem/ev-battery-recycling-in-india-opportunities-and-challenges/

(IRENA), I. R. E. A. (n.d.[a]). Renewable power generation costs in 2019 [[accessed May 25, 2024]]. https://www.irena.org/publications/2020/Jun/Renewable-Power-Costs-in-2019

On Climate Change (IPCC), I. P. (n.d.). Climate change 2014: Mitigation of climate change [[accessed May 25, 2024]]. https://www.ipcc.ch/report/ar5/wg3/

(IRENA), I. R. E. A. (n.d.[b]). Renewable energy market analysis: Southeast asia [[accessed May 25, 2024]]. https://www.irena.org/publications/2018/Jan/Renewable-Energy-Market-Analysis-Southeast-Asia

(IEA), I. E. A. (n.d.). Energy efficiency 2019: Analysis and outlooks to 2040 [[accessed May 25, 2024]]. https://www.iea.org/reports/energy-efficiency-2019

Garg, H., Ali, Z., & Mahmood, T. (2021). Generalized dice similarity measures for complex qrung orthopair fuzzy sets and its application. Complex and Intelligent Systems, 7, 667–686. https://doi.org/10.1007/s40747-020-00203-x

Du, Y., Du, X., Li, Y., Cui, J.-x.,&Hou, F. (2022). Complex q-rung orthopair fuzzy frank aggregation operators and their application to multi-attribute decision making. Soft Comput., 26(22), 11973–12008. https://doi.org/10.1007/s00500-022-07465-2

Senapati, T. (2022). Approaches to multi-attribute decision making based on picture fuzzy aczelalsina average aggregation operators. Computational and Applied Mathematics, 41, 40. https://doi.org/10.1007/s40314-021-01742-w

Du, W. (2020). More on dombi operations and dombi aggregation operators for q-rung orthopair fuzzy values. Journal of Intelligent & Fuzzy Systems, 39(3), 3715–3735. https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs192052

Garg, H., & Chen, S.-M. (2020). Multiattribute group decision making based on neutrality aggregation operators of q-rung orthopair fuzzy sets. Information Sciences, 517, 427–447. https://doi.org/10.1016/j.ins.2019.11.035

Published

2025-01-27

How to Cite

Petchimuthu, S., Banu M, F., Mahendiran, C., & Premala, T. (2025). Power and Energy Transformation: Multi-Criteria Decision-Making Utilizing Complex q-Rung Picture Fuzzy Generalized Power Prioritized Yager Operators. Spectrum of Operational Research, 2(1), 219-258. https://doi.org/10.31181/sor21202525