Research article

Intelligent optimization algorithm for strategic planning in economics with multi-factors assessment: A MEREC-driven Heronian mean framework

  • Published: 12 May 2025
  • MSC : 03E72, 62C86, 68U35, 90B50

  • Strategic planning in economics solves interconnected challenges such as dynamic interdependencies, conflicting objectives, and uncertain outcomes. Many such complexities present challenges for traditional approaches in terms of the evaluation and outcomes, requiring an innovative decision approach. An intelligent optimization algorithm is introduced in this paper, which assists policymakers in strategic planning through a multi-factor comprehensive assessment. A robust mechanism for analyzing, prioritizing, and resolving conflicting objectives is proposed based on integrating economic, social, and risk-related parameters within a highly sophisticated circular interval-valued framework. Unlike the traditional model, the proposed methodology is applied to reveal more adaptive, dynamic, and interdependent factors that provide actionable strategies that are consistent with policy goals. The comparison analysis demonstrates the proposed algorithm's validation and efficacy, highlighting the reliability and adaptability and depicting that the algorithm can transform the process of making economic strategy. The proposed work provides theoretical innovation and practical applications such as macroeconomic policy, financial risk management, and sustainable infrastructure development. It is a compelling tool for policymakers and strategists seeking to maximize economic outcomes in uncertain and dynamic environments.

    Citation: Yuejia Dang. Intelligent optimization algorithm for strategic planning in economics with multi-factors assessment: A MEREC-driven Heronian mean framework[J]. AIMS Mathematics, 2025, 10(5): 10866-10897. doi: 10.3934/math.2025494

    Related Papers:

  • Strategic planning in economics solves interconnected challenges such as dynamic interdependencies, conflicting objectives, and uncertain outcomes. Many such complexities present challenges for traditional approaches in terms of the evaluation and outcomes, requiring an innovative decision approach. An intelligent optimization algorithm is introduced in this paper, which assists policymakers in strategic planning through a multi-factor comprehensive assessment. A robust mechanism for analyzing, prioritizing, and resolving conflicting objectives is proposed based on integrating economic, social, and risk-related parameters within a highly sophisticated circular interval-valued framework. Unlike the traditional model, the proposed methodology is applied to reveal more adaptive, dynamic, and interdependent factors that provide actionable strategies that are consistent with policy goals. The comparison analysis demonstrates the proposed algorithm's validation and efficacy, highlighting the reliability and adaptability and depicting that the algorithm can transform the process of making economic strategy. The proposed work provides theoretical innovation and practical applications such as macroeconomic policy, financial risk management, and sustainable infrastructure development. It is a compelling tool for policymakers and strategists seeking to maximize economic outcomes in uncertain and dynamic environments.



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    [1] G. A. Steiner, Strategic planning, New York: Free Press, 1979.
    [2] A. R. Arabeyyat, R. Aldweik, A comprehensive exploration of the jordan tourism board's 2023 strategic planning, news, and social media, Emir. J. Bus. Econ. Soc. Stud., 3 (2024), 32–41. https://doi.org/10.54878/79sfh467 doi: 10.54878/79sfh467
    [3] A. A. Akinsulire, C. Idemudia, A. C. Okwandu, O. Iwuanyanwu, Strategic planning and investment analysis for affordable housing: Enhancing viability and growth, Magna Sci. Adv. Res. Rev., 11 (2024), 119–131. https://doi.org/10.30574/msarr.2024.11.2.0114 doi: 10.30574/msarr.2024.11.2.0114
    [4] R. Kemp, Strategic planning in local government: A casebook, New York: Routledge, 1992. https://doi.org/10.4324/9781003571957
    [5] E. Raji, T. I. Ijomah, O. G. Eyieyien, Integrating technology, market strategies, and strategic management in agricultural economics for enhanced productivity, Int. J. Manag. Entrep. Res., 6 (2024), 2112–2124. https://doi.org/10.51594/ijmer.v6i7.1260 doi: 10.51594/ijmer.v6i7.1260
    [6] D. Frei, D. Ruloff, Handbook of foreign policy analysis: Methods for practical application in foreign policy planning, strategic planning and business risk assessment, Martinus Nijhoff, 1989.
    [7] M. J. Rahayu, A. H. Juwita, S. Bintariningtyas, E. F. Rini, T. Wahyuni., The effectiveness of the regional long-term development plan of Purworejo Regency: The evaluation of strategic planning, Plan. Malays., 22 (2024).https://doi.org/10.21837/pm.v22i30.1436
    [8] G. Zakhidov, Economic indicators: Tools for analyzing market trends and predicting future performance, Int. Multidiscip. J. Univ. Sci. Prospect., 2 (2024), 23–29.
    [9] H. Kalliomäki, P. Oinas, T. Salo, Innovation districts as strategic urban projects: The emergence of strategic spatial planning for urban innovation, Eur. Plan. Stud., 32 (2024), 78–96. https://doi.org/10.1080/09654313.2023.2216727 doi: 10.1080/09654313.2023.2216727
    [10] A. Bazina, Strategic planning and its impact on persistent profitability and productivity in African agriculture sector, Multi-Knowl. Electron. Compr. J. Educ. Sci. Publ., 2021.
    [11] L. A. Zadeh, Fuzzy sets, Inf. Control, 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X doi: 10.1016/S0019-9958(65)90241-X
    [12] K. T. Atanassov, Interval-valued intuitionistic fuzzy sets, Cham: Springer, 2020.https://doi.org/10.1007/978-3-030-32090-4
    [13] R. R. Yager, Pythagorean fuzzy subsets, In: 2013 Joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), 2013, 57–61.https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375
    [14] R. R. Yager, Generalized orthopair fuzzy sets, IEEE Trans. Fuzzy Syst., 25 (2017), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005 doi: 10.1109/TFUZZ.2016.2604005
    [15] R. Imran, K. Ullah, Z. Ali, M. Akram, A multi-criteria group decision-making approach for robot selection using interval-valued intuitionistic fuzzy information and Aczel-Alsina Bonferroni means, Spectr. Decis. Mak. Appl., 1 (2024), 1–32. https://doi.org/10.31181/sdmap1120241 doi: 10.31181/sdmap1120241
    [16] S. Biswas, A. Sanyal, D. Pamucar, Students' perceptions about the webinars: An intuitionistic fuzzy force field analysis, Spec. Oper. Res., 2 (2025), 113–133.
    [17] S. Kousar, A. Ansar, N. Kausar, G. Freen, Multi-criteria decision-making for smog mitigation: A comprehensive analysis of health, economic, and ecological impacts, Spectr. Decis. Mak. Appl., 2 (2025), 53–67. https://doi.org/10.31181/sdmap2120258 doi: 10.31181/sdmap2120258
    [18] K. T. Atanassov, Circular intuitionistic fuzzy sets, J. Intell. Fuzzy Syst., 39 (2020), 5981–5986. https://doi.org/10.3233/JIFS-189072 doi: 10.3233/JIFS-189072
    [19] S. Varelas, G. Tsoupros, Key performance indicators and data envelopment analysis in Greek tourism: A strategic planning tool for destinations and DMMOs, Sustainability, 16 (2024), 3453. https://doi.org/10.3390/su16083453 doi: 10.3390/su16083453
    [20] M. S. Alam, J. B. Deb, A. A. Amin, S. Chowdhury, An artificial neural network for predicting air traffic demand based on socio-economic parameters, Decis. Anal. J., 10 (2024), 100382. https://doi.org/10.1016/j.dajour.2023.100382 doi: 10.1016/j.dajour.2023.100382
    [21] L. Vandersmissen, B. George, J. Voets, Strategic planning and performance perceptions of managers and citizens: Analysing multiple mediations, Public Manag. Rev., 26 (2024), 514–538. https://doi.org/10.1080/14719037.2022.2103172 doi: 10.1080/14719037.2022.2103172
    [22] H. E. Adama, O. A. Popoola, C. D. Okeke, A. E. Akinoso, Economic theory and practical impacts of digital transformation in supply chain optimization, Int. J. Adv. Econ., 6 (2024), 95–107. https://doi.org/10.51594/ijae.v6i4.1072 doi: 10.51594/ijae.v6i4.1072
    [23] M. Tiits, E. Karo, T. Kalvet, Small countries facing the technological revolution: Fostering synergies between economic complexity and foresight research, Compet. Rev., 35 (2025), 53–75. https://doi.org/10.1108/CR-03-2023-0051 doi: 10.1108/CR-03-2023-0051
    [24] R. Imran, K. Ullah, Z. Ali, M. Akram, An approach to multi-attribute decision-making based on single-valued neutrosophic hesitant fuzzy aczel-alsina aggregation operator, Neutrosophic Syst. Appl., 22 (2024), 7. https://doi.org/10.61356/j.nswa.2024.22387 doi: 10.61356/j.nswa.2024.22387
    [25] E. Boltürk, C. Kahraman, Interval-valued and circular intuitionistic fuzzy present worth analyses, Informatica, 33 (2022), 693–711. https://doi.org/10.15388/22-INFOR478 doi: 10.15388/22-INFOR478
    [26] A. Hussain, K. Ullah, H. Garg, T. Mahmood, A novel multi-attribute decision-making approach based on T-spherical fuzzy Aczel Alsina Heronian mean operators, Granul. Comput., 9 (2024), 21. https://doi.org/10.1007/s41066-023-00442-6 doi: 10.1007/s41066-023-00442-6
    [27] M. Keshavarz-Ghorabaee, M. Amiri, E. K. Zavadskas, Z. Turskis, J. Antucheviciene, Determination of objective weights using a new method based on the removal effects of criteria (MEREC), Symmetry, 13 (2021), 525. https://doi.org/10.3390/sym13040525 doi: 10.3390/sym13040525
    [28] M. A. Ali, M. Kamraju, D. B. Sonaji, Economic policies for sustainable development: Balancing growth, social equity, and environmental protection, ASEAN J. Econ. Econ. Educ., 3 (2024), 23–28.
    [29] B. Ma, A. Wang, Exploring the role of renewable energy in green job creation and sustainable economic development: An empirical approach, Energy Strategy Rev., 58 (2025), 101642. https://doi.org/10.1016/j.esr.2025.101642 doi: 10.1016/j.esr.2025.101642
    [30] S. A. Khan, A. Q. Patoli, H. Ahmed, I. Ahmed, Innovations in green technologies: analyzing their contribution to job creation and sustainable economic transitions, Rev. Appl. Manag. Soc. Sci., 8 (2025), 263–277. https://doi.org/10.47067/ramss.v8i1.456 doi: 10.47067/ramss.v8i1.456
    [31] R. Wang, M. Qamruzzaman, S. Karim, Unveiling the power of education, political stability and ICT in shaping technological innovation in BRI nations, Heliyon, 10 (2024), e30142. https://doi.org/10.1016/j.heliyon.2024.e30142 doi: 10.1016/j.heliyon.2024.e30142
    [32] W. Wang, Y. Feng, Group decision making based on generalized intuitionistic fuzzy yager weighted heronian mean aggregation operator, Int. J. Fuzzy Syst., 26 (2024), 1364–1382. https://doi.org/10.1007/s40815-023-01672-1 doi: 10.1007/s40815-023-01672-1
    [33] M. Saqlain, R. Imran, S. Hassan, TOPSIS technique of MCDM under cubic intuitionistic fuzzy soft set environment, Sci. Inquiry Rev., 7 (2023), 33–52. https://doi.org/10.32350/sir.71.03 doi: 10.32350/sir.71.03
    [34] M. Saqlain, M. Riaz, R. Imran, F. Jarad, Distance and similarity measures of intuitionistic fuzzy hypersoft sets with application: Evaluation of air pollution in cities based on air quality index, AIMS Mathematics, 8 (2023), 6880–6899. https://doi.org/10.3934/math.2023348 doi: 10.3934/math.2023348
    [35] R. Imran, K. Ullah, Z. Ali, M. Akram, T. Senapati, The theory of prioritized Muirhead mean operators under the presence of complex single-valued neutrosophic values, Decis. Anal. J., 7 (2023), 100214. https://doi.org/10.1016/j.dajour.2023.100214 doi: 10.1016/j.dajour.2023.100214
    [36] H. Wang, W. Zhao, J. Zheng, Improved q-rung orthopair fuzzy WASPAS method based on softmax function and frank operations for investment decision of community group-buying platform, J. Soft Comput. Decis. Anal., 2 (2024), 188–212. https://doi.org/10.31181/jscda21202442 doi: 10.31181/jscda21202442
    [37] K. Kara, H. Özyürek, G. C. Yalçın, N. Burgaz, Enhancing financial performance evaluation: The MEREC-RBNAR hybrid method for sustainability-indexed companies, J. Soft Comput. Decis. Anal., 2 (2024), 236–257. https://doi.org/10.31181/jscda21202444 doi: 10.31181/jscda21202444
    [38] T. Xu, H. Wang, L. Feng, Y. Zhu, Risk factors assessment of smart supply chain in intelligent manufacturing services using DEMATEL method with linguistic q-ROF information, J. Oper. Intell., 2 (2024), 129–152. https://doi.org/10.31181/jopi21202417 doi: 10.31181/jopi21202417
    [39] Y. Wang, X. Han, W. Wang, A fermatean fuzzy ORESTE method for evaluating the resilience of the food supply chain, J. Oper. Intell., 2 (2024), 78–94. https://doi.org/10.31181/jopi2120249 doi: 10.31181/jopi2120249
    [40] J. Ge, S. Zhang, Adaptive inventory control based on fuzzy neural network under uncertain environment, Complexity, 2020 (2020), 6190936. https://doi.org/10.1155/2020/6190936 doi: 10.1155/2020/6190936
    [41] R. Imran, K. Ullah, Circular intuitionistic fuzzy edas approach: A new paradigm for decision-making in the automotive industry sector, Spectr. Eng. Manag. Sci., 3 (2025), 76–92. https://doi.org/10.31181/sems31202537i doi: 10.31181/sems31202537i
    [42] M. Sarwar, H. Humaira T. Li, Fuzzy fixed point results and applications to ordinary fuzzy differential equations in complex valued metric spaces, Hacet. J. Math. Stat., 49 (2018), 1712–1728. https://doi.org/10.15672/HJMS.2018.633 doi: 10.15672/HJMS.2018.633
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