Research article

Innovative q-rung orthopair fuzzy prioritized aggregation operators based on priority degrees with application to sustainable energy planning: A case study of Gwadar

  • Received: 30 July 2021 Accepted: 26 August 2021 Published: 07 September 2021
  • MSC : 03E72, 94D05, 90B50

  • Clean energy potential can be used on a large scale in order to achieve cost competitiveness and market effectiveness. This paper offers sufficient information to choose renewable technology for improving the living conditions of the local community while meeting energy requirements by employing the notion of q-rung orthopair fuzzy numbers (q-ROFNs). In real-world situations, a q-ROFN is exceptionally useful for representing ambiguous/vague data. A multi-criteria decision-making (MCDM) is proposed in which the parameters have a prioritization relationship and the idea of a priority degree is employed. The aggregation operators (AOs) are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, some prioritized operators with q-ROFNs are proposed named as "q-rung orthopair fuzzy prioritized averaging (q-ROFPAd) operator with priority degrees and q-rung orthopair fuzzy prioritized geometric (q-ROFPGd) operator with priority degrees". The results of the proposed approach are compared with several other related studies. The comparative analysis results indicate that the proposed approach is valid and accurate which provides feasible results. The characteristics of the existing method are often compared to other current methods, emphasizing the superiority of the presented work over currently used operators. Additionally, the effect of priority degrees is analyzed for information fusion and feasible ranking of objects.

    Citation: Muhammad Riaz, Hafiz Muhammad Athar Farid, Hafiz Muhammad Shakeel, Muhammad Aslam, Sara Hassan Mohamed. Innovative q-rung orthopair fuzzy prioritized aggregation operators based on priority degrees with application to sustainable energy planning: A case study of Gwadar[J]. AIMS Mathematics, 2021, 6(11): 12795-12831. doi: 10.3934/math.2021739

    Related Papers:

  • Clean energy potential can be used on a large scale in order to achieve cost competitiveness and market effectiveness. This paper offers sufficient information to choose renewable technology for improving the living conditions of the local community while meeting energy requirements by employing the notion of q-rung orthopair fuzzy numbers (q-ROFNs). In real-world situations, a q-ROFN is exceptionally useful for representing ambiguous/vague data. A multi-criteria decision-making (MCDM) is proposed in which the parameters have a prioritization relationship and the idea of a priority degree is employed. The aggregation operators (AOs) are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, some prioritized operators with q-ROFNs are proposed named as "q-rung orthopair fuzzy prioritized averaging (q-ROFPAd) operator with priority degrees and q-rung orthopair fuzzy prioritized geometric (q-ROFPGd) operator with priority degrees". The results of the proposed approach are compared with several other related studies. The comparative analysis results indicate that the proposed approach is valid and accurate which provides feasible results. The characteristics of the existing method are often compared to other current methods, emphasizing the superiority of the presented work over currently used operators. Additionally, the effect of priority degrees is analyzed for information fusion and feasible ranking of objects.



    加载中


    [1] L. A. Zadeh, Fuzzy sets, Inf. Control, 8 (1965), 338–353.
    [2] K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy. Set. Syst., 20 (1986), 87–96.
    [3] R. R. Yager, A. M. Abbasov, Pythagorean membership grades, complex numbers, and decision making, Int. J. Intell. Syst., 28 (2013), 436–452. doi: 10.1002/int.21584
    [4] R. R. Yager, Pythagorean fuzzy subsets, IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint, Edmonton, Canada, IEEE, 2013, 57–61.
    [5] R. R. Yager, Pythagorean membership grades in multi criteria decision-making, IEEE Trans. Fuzzy Syst., 22 (2014), 958–965. doi: 10.1109/TFUZZ.2013.2278989
    [6] R. R. Yager, Generalized orthopair fuzzy sets., IEEE Trans. Fuzzy Syst, 25 (2017), 1222–1230. doi: 10.1109/TFUZZ.2016.2604005
    [7] M. I. Ali, Another view on q-rung orthopair fuzzy sets, Int. J. Intell. Syst., 33 (2018), 2139–2153. doi: 10.1002/int.22007
    [8] Z. S. Xu, Intuitionistic fuzzy aggregation operators, IEEE Trans. Fuzzy Syst., 15 (2007), 1179–1187. doi: 10.1109/TFUZZ.2006.890678
    [9] Z. S. Xu, R. R. Yager, Some geometric aggregation operators based on intuitionistic fuzzy sets, Int. J. Gen. Syst., 35 (2006), 417–433. doi: 10.1080/03081070600574353
    [10] M. Riaz, M. R. Hashmi, Linear diophantine fuzzy set and its applications towards multi-attribute decision making problems, J. Intell. Fuzzy Syst., 37 (2019), 5417–5439. doi: 10.3233/JIFS-190550
    [11] M. Riaz, H. M. A. Farid, M. Aslam, D. Pamucar, D. Bozanic, Novel approach for third-party reverse logistic provider selection process under linear diophantine fuzzy prioritized aggregation operators, Symmetry, 13 (2021), 1152. doi: 10.3390/sym13071152
    [12] A. Iampan, G. S. Garcia, M. Riaz, H. M. A. Farid, R. Chinram, Linear diophantine fuzzy einstein aggregation operators for multi-criteria decision-making problems, J. Math., 2021 (2021), 5548033.
    [13] P. Liu, J. Liu, Some q-rung orthopai fuzzy bonferroni mean operators and their application to multi-attribute group decision making, Int. J. Intell. Syst., 33 (2018), 315–347. doi: 10.1002/int.21933
    [14] M. J. Khan, J. C. R. Alcantud, P. Kumam, W. Kumam, A. N. A. Kenani, An axiomatically supported divergence measures for q-rung orthopair fuzzy sets, Int. J. Intell. Syst., 2018, https://doi.org/10.1002/int.22545.
    [15] M. Riaz, A. Razzaq, H. Kalsoom, D. Pamucar, H. M. A. Farid, Y. M. Chu, q-Rung orthopair fuzzy geometric aggregation operators based on generalized and group-generalized parameters with application to water loss management, Symmetry, 12 (2020), 1236. doi: 10.3390/sym12081236
    [16] H. M. A. Farid, M. Riaz, Some generalized q-rung orthopair fuzzy Einstein interactive geometric aggregation operators with improved operational laws, Int. J. Intell. Systs., 2021, https://doi.org/10.1002/int.22587.
    [17] Z. Liu, S. Wang, P. Liu, Multiple attribute group decision making based on q-rung orthopair fuzzy Heronianmean operators, Int. J. Intell. Syst., 33 (2018), 2341–2363. doi: 10.1002/int.22032
    [18] M. Sitara, M. Akram, M. Riaz, Decision-making analysis based on q-rung picture fuzzy graph structures, J. Appl. Math. Comput., 2021, https:doi.org/10.1007/s12190-020-01471-z.
    [19] M. Akram, A. Khan, J. C. R. Alcantud, G. Santos-Garcia, A hybrid decision-making framework under complex spherical fuzzy prioritized weighted aggregation operators, Expert Syst., 2021, https://doi.org/10.1111/exsy.12712.
    [20] M. Akram, N. Yaqoob, G. Ali, W. Chammam, Extensions of Dombi aggregation operators for decision making under $m$-polar fuzzy information, J. Math., 6 (2020), 1–20.
    [21] L. Wang, H. Garg, N. Li, Pythagorean fuzzy interactive Hamacher power aggregation operators for assessment of express service quality with entropy weight, Soft Comput., 25 (2021), 973–993. doi: 10.1007/s00500-020-05193-z
    [22] A. Saha, P. Majumder, D. Dutta, B. K. Debnath, Multi attribute decision making using q-rung orthopair fuzzy weighted fairly aggregation operators, J. Ambient Intell. Humaniz. Comput. 12 (2021), 8149–8171.
    [23] S. Ashraf, S. Abdullah, T. Mahmood, M. Aslam, Cleaner production evaluation in gold mines using novel distance measure method with cubic picture fuzzy numbers, Int. J. Fuzzy Syst., 21 (2019), 2448–2461. doi: 10.1007/s40815-019-00681-3
    [24] S. Chakraborty, V. Kumar, Development of an intelligent decision model for non-traditional machining processes, Decis. Mak. Appl. Manag. Eng., 4 (2021), 194–214. doi: 10.31181/dmame2104194c
    [25] M. Riaz, N. Cagman, N. Wali, A. Mushtaq, Certain properties of soft multi-set topology with applications in multi-criteria decision making, Decis. Mak. Appl. Manag. Eng., 3 (2020), 70–96. doi: 10.31181/dmame2003070r
    [26] Z. Ali, T. Mahmood, K. Ullah, Q. Khan, Einstein geometric aggregation operators using a novel complex interval-valued pythagorean fuzzy setting with application in green supplier chain management, Rep. Mech. Eng., 2 (2021), 105–134. doi: 10.31181/rme2001020105t
    [27] A. Alosta, O. Elmansuri, I. Badi, Resolving a location selection problem by means of an integrated AHP-RAFSI approach, Rep. Mech. Eng., 2 (2021), 135–142. doi: 10.31181/rme200102135a
    [28] M. Riaz, H. Garg, H. M. A. Farid, R. Chinram, Multi-criteria decision making based on bipolar picture fuzzy operators and new distance measures, Comput. Model. Eng. Sci., 127 (2021), 771–800.
    [29] J. C. R. Alcantud, Softarisons: Theory and practice, Neural Comput. Appl. 2021, https://doi.org/10.1007/s00521-021-06272-4.
    [30] J. C. R. Alcantud, An operational characterization of soft topologies by crisp topologies, Mathematics, 9 (2021), 1656. doi: 10.3390/math9141656
    [31] R. R. Yager, Prioritized aggregation operators, Int. J. Approx. Reason, 48 (2008), 263–274. doi: 10.1016/j.ijar.2007.08.009
    [32] B. Li, Z. Xu, Prioritized aggregation operators based on the priority degrees in multicriteria decision-making, Int. J. Intell. Syst., 34 (2019), 1985–2018. doi: 10.1002/int.22123
    [33] M. Riaz, D. Pamucar, H. M. A. Farid, M. R. Hashmi, q-Rung orthopair fuzzy prioritized aggregation operators and their application towards green supplier chain management, Symmetry, 12 (2020), 976. doi: 10.3390/sym12060976
    [34] P. Liu, P. Wang, Some q-rung orthopair fuzzy aggregation operator and their application to multi-attribute decision making, Int. J. Intell. Syst., 33 (2018), 259–280. doi: 10.1002/int.21927
    [35] B. Lin, M. Y. Raza, Coal and economic development in Pakistan: A necessity of energy source, Energy, 207 (2020), 118244. doi: 10.1016/j.energy.2020.118244
    [36] E. Sanchez-Triana, S. Enriquez, J. Afzal, A. Nakagawa, A. Shuja Khan, Cleaning Pakistan's air: Policy options to address the cost of outdoor air pollution 2014, https:doi.org/10.1596/978-1-4648-0235-5.
    [37] www.finance.gov.pk/survey/chapter20/PES-2019-20.pdf.
    [38] A. Rehman, H. Ma, I. Ozturk, M. Ahmad, A. Rauf, M. Irfan, Another outlook to sector-level energy consumption in Pakistan from dominant energy sources and correlation with economic growth, Environ. Sci. Pollut. Res., 2020, https:doi.org/10.1007/s11356-020-09245-7.
    [39] F. Shaikh, Q. Ji, Y. Fan, The diagnosis of an electricity crisis and alternative energy development in Pakistan, Renew. Sustain. Energy Rev., 52 (2015), 1172–1185. doi: 10.1016/j.rser.2015.08.009
    [40] M. Jabeen, Solar technology and Gwadar port city of Pakistan, Socio-Econ. Eval., 29 (2017), 525–532.
    [41] A. Insaf, Z. Uddin, M. S. Baig, An initial assessment of tidal energy resources using GIS for Miani Hor, Baluchistan province, Pakistan, Arab. J. Geosci., 13 (2020), 196. doi: 10.1007/s12517-020-5191-5
    [42] Y. Perignon, Assessing accuracy in the estimation of spectral content in wave energy resource on the French Atlantic test site SEMREV, Renew. Energy, 114 (2017), 145–153. doi: 10.1016/j.renene.2017.02.086
    [43] A. Insaf, M. S. Baig, A. Kausar, T. Rauf, M. Shahzad, S. A. Khan, Estimation of various tidal parameters and possibility for harnessing tidal energy along the southeast coastal area, Sci. Int., 28 (2016), 179–185.
    [44] J. P. J. O'Carroll, R. M. Kennedy, G. Savidge, Identifying relevant scales of variability for monitoring epifaunal reef communities at a tidal energy extraction site, Ecol. Indic., 73 (2017), 388–397. doi: 10.1016/j.ecolind.2016.10.005
    [45] S. H. Shami, J. Ahmad, R. Zafar, M. Haris, S. Bashir, Evaluating wind energy potential in Pakistan's three provinces, with proposal for integration into national power grid, Renew. Sustain. Energy Rev., 53 (2016), 408–421. doi: 10.1016/j.rser.2015.08.052
    [46] I. Ullah, Q. uz, Z. Chaudhry, A. J. Chipperfield, An evaluation of wind energy potential at Kati Bandar, Pakistan, Renew. Sustain. Energy Rev., 14 (2010), 856–861. doi: 10.1016/j.rser.2009.10.014
    [47] K. S. Khan, M. Tariq, Wind resource assessment using SODAR and meteorological mast: A case study of Pakistan, Renew. Sustain. Energy Rev., 81 (2018), 2443–2449. doi: 10.1016/j.rser.2017.06.050
    [48] www.finance.gov.pk/survey-1718.html.
    [49] A. Ashfaq, A. Ianakiev, Features of fully integrated renewable energy atlas for Pakistan; wind, solar and cooling, Renew. Sustain. Energy Rev., 97 (2018), 14–27. doi: 10.1016/j.rser.2018.08.011
    [50] F. Shaikh, Q. Ji, Y. Fan, The diagnosis of an electricity crisis and alternative energy development in Pakistan, Renew. Sustain. Energy Rev., 52 (2015), 1172–1185. doi: 10.1016/j.rser.2015.08.009
    [51] S. R. Shakeel, J. Takala, W. Shakeel, Renewable energy sources in power generation in Pakistan, Renew. Sustain. Energy Rev., 64 (2016), 421–434. doi: 10.1016/j.rser.2016.06.016
    [52] S. Stokler, C. Schillings, B. Kraas, Solar resource assessment study for Pakistan, Renew. Sustain. Energy Rev., 58 (2016), 1184–1188. doi: 10.1016/j.rser.2015.12.298
    [53] M. Sultan, J. Wu, F. E. Aleem, M. Imran, Cost and energy analysis of a grid-tie solar system synchronized with utility and fossil fuel generation with major Issues for the attenuation of solar power in Pakistan, Solar Energy, 174 (2018), 967–975. doi: 10.1016/j.solener.2018.09.052
    [54] S. Z. Farooqui, Prospects of renewables penetration in the energy mix of Pakistan, Renew. Sustain. Energy Rev., 29 (2014), 693–700. doi: 10.1016/j.rser.2013.08.083
    [55] H. A. Khan, S. Pervaiz, Technological review on solar PV in Pakistan: Scope, practices and recommendations for optimized system design, Renew. Sustain. Energy Rev., 23 (2013), 147–154. doi: 10.1016/j.rser.2013.02.031
    [56] H. A. Sher, A. F. Murtaza, K. E. Addoweesh, M. Chiaberge, Pakistan's progress in solar PV based energy generation, Renew. Sustain. Energy Rev., 47 (2015), 213–217. doi: 10.1016/j.rser.2015.03.017
    [57] S. Malik, M. Qasim, H. Saeed, Y. Chang, F. Taghizadeh-Hesary, Energy security in Pakistan: Perspectives and policy implications from a quantitative analysis, Energy Policy, 144 (2020), 111552. doi: 10.1016/j.enpol.2020.111552
    [58] S. L. Satti, M. S. Hassan, H. Mahmood, M. Shahbaz, Coal consumption: An alternate energy resource to fuel economic growth in Pakistan, Econ. Model., 36 (2014), 282–287. doi: 10.1016/j.econmod.2013.09.046
    [59] Q. U. A. Ali, U. Khayyam, U. Nazar, Energy production and $CO_2$ emissions: The case of coal fired power plants under China Pakistan economic corridor, J. Clean. Prod., 281 (2021), 124974. doi: 10.1016/j.jclepro.2020.124974
    [60] A. W. Bhutto, S. Karim, Coal gasification for sustainable development of the energy sector in Pakistan, Energy Sustain. Dev., 9 (2005), 60–67. doi: 10.1016/S0973-0826(08)60500-1
    [61] B. Lin, M. Y. Raza, Coal and economic development in Pakistan: A necessity of energy source, Energy, 207 (2020), 118244. doi: 10.1016/j.energy.2020.118244
    [62] M. Riaz, H. M. A. Farid, H. Kalsoom, D. Pamucar, Y. M. Chu, A robust q-rung orthopair fuzzy Einstein prioritized aggregation operators with application towards MCGDM, Symmetry, 12 (2020), 1058. doi: 10.3390/sym12061058
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2114) PDF downloads(85) Cited by(16)

Article outline

Figures and Tables

Figures(2)  /  Tables(11)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog