A Fuzzy Multi-Criteria Decision-Making Approach for Power Generation Problem Analysis | Journal of Engineering Sciences

A Fuzzy Multi-Criteria Decision-Making Approach for Power Generation Problem Analysis

Author(s): Emovon I.

Affiliation(s): Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun, Delta State, Nigeria

*Corresponding Author’s Address: emovon.ikuobase@fupre.edu.ng

Issue: Volume 7, Issue 2 (2020)

Paper received: September 11, 2020
The final version of the paper received: December 9, 2020
Paper accepted online: December 16, 2020

Emovon I. (2020). A fuzzy multi-criteria decision-making approach for power generation problem analysis. Journal of Engineering Sciences, Vol. 7(2), pp. E26–E31, doi: 10.21272/jes.2020.7(2).e5

DOI: 10.21272/jes.2020.7(2).e5

Research Area:  MECHANICAL ENGINEERING: Computational Mechanics

Abstract. The abundance of different energy sources such as coal, natural gas, and crude oil are in the Africa region, yet one of the lowest electric energy per capita consumption. Different factors have been attributed to this abysmal energy failure in the literature, leading to her slow economic and industrial advancement. These factors include poor maintenance of power generation infrastructure and lack of policy continuity, among others. The purpose of this article is to prioritize these power generation problems for proper budgetary allocation by managers of electric power. The fuzzy VIKOR technique is presented for the evaluation and ranking of these power generation problems. The analysis showed that poor maintenance is the most critical challenge of bedeviling power generation in Nigeria. The Fuzzy VIKOR produces the same result as the classical VIKOR used previously in resolving the problem. The proposed technique addresses the challenge of uncertainty and subjectivity by applying linguistic variables in the decision-making process, which the classical VIKOR is incapable of handling.

Keywords: fuzzy logic, VIKOR technique, electric power, power generation.


  1. Ogbonnaya, C., Abeykoon, C., Damo, U. M., Turan, A. (2019). The current and emerging renewable energy technologies for power generation in Nigeria: A review. Thermal Science and Engineering Progress, Vol. 13, 100390.
  2. Emovon, I. and Nwaoha, T. C. (2018). Power generation problems ranking using a combination of AHP and MOORA techniques, Annals of the Faculty of Engineering Hunedoara-International Journal of Engineering, Vol. 16(2), pp. 13-18.
  3. Emovon, I., Samuel, O. D. (2017). An integrated Statistical Variance and VIKOR methods for prioritising power generation problems in Nigeria. Journal of Engineering and Technology, Vol. 8(1), pp. 92-101.
  4. Aliyu, A. S., Ramli, A. T., Saleh, M.A. (2013). Nigeria electricity crisis: Power generation capacity expansion and environmental ramifications. Energy, Vol. 61, pp. 354-367.
  5. Monyei, C. G., Adewumi, A. O., Obolo, M. O., Sajou, B. (2018). Nigeria’s energy poverty: Insights and implications for smart policies and framework towards a smart Nigeria electricity network. Renewable and Sustainable Energy Reviews, Vol. 81, pp. 1582-1601.
  6. Okoye, C. O., Taylan, O., Baker, D. K. (2016). Solar energy potentials in strategically located cities in Nigeria: Review, resource assessment and PV system design. Renewable and Sustainable Energy Reviews, Vol. 55, pp. 550-566.
  7. Ibitoye, F. I., Adenikinju, A., 2007. Future demand for electricity in Nigeria. Applied Energy, Vol. 84(5), pp.492-504.
  8. Olaoye, T., Ajilore, T., Akinluwade, K., Omole, F., Adetunji, A. (2016). Energy crisis in Nigeria: Need for renewable energy mix. American Journal of Electrical and Electronic Engineering, Vol. 4(1), pp. 1-8.
  9. Onohaebi, O. S., Lawal, Y. O. (2010). Poor maintenance culture; the bane to electric power generation in Nigeria. Journal of Economics and Engineering, pp. 28-33.
  10. Idigbe, K. I., Igbinovia, S. O. (2010). Assessing the sustainability of electric power in Nigeria: a case study of the IPPs. J Econ Eng, 70-7.
  11. Sule, A. H. (2010). Major factors affecting electricity generation, transmission and distribution in Nigeria. International Journal of Engineering and Mathematical Intelligence, Vol. 1(1), pp. 164-169.
  12. Adenikinju, A. F. (2003). Electric infrastructure failures in Nigeria: A survey-based analysis of the costs and adjustment responses. Energy Policy, Vol. 31(14), pp. 1519-1530.
  13. Kaya, T., Kahraman, C. (2010). Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy, Vol. 35(6), pp. 2517-2527.
  14. Opricovic, S., Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, Vol. 156(2), pp. 445-455.
  15. Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E., Omid, M. (2018). Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry. Computers and Operations Research, Vol. 89, pp. 337-347.
  16. Shemshadi, A., Shirazi, H., Toreihi, M., Tarokh, M. J. (2011). A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Systems with Applications, Vol. 38(10), pp. 12160-12167.
  17. Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Vol. 2(1), pp. 5-21.
  18. Carpitella, S., Certa, A., Izquierdo, J., La Fata, C. M. (2018). A combined multi-criteria approach to support FMECA analyses: A real-world case. Reliability Engineering and System Safety, Vol. 169, pp. 394-402.
  19. Vinodh, S., Vimal, K. E. K. (2012). Thirty criteria based leanness assessment using fuzzy logic approach. The International Journal of Advanced Manufacturing Technology, Vol. 60(9-12), pp. 1185-1195.
  20. Kore, N. B., Ravi, K., Patil S. B. (2017). A simplified description of FUZZYTOPSIS method for Multicriteria Decision Making. International Research Journal of Engineering and Technology, Vol. 4(5), pp. 1-4.
  21. Azizi, A., Aikhuele, D. O., Souleman, F. S. (2015). A Fuzzy TOPSIS model to rank automotive suppliers. Procedia Manufacturing, Vol. 2, pp. 159-164.
  22. Wang, T. C., Liang, J. L., Ho, C. Y. (2006). Multi-criteria decision analysis by using fuzzy VIKOR. IEEE 2006 International Conference on Service Systems and Service Management, Vol. 2, pp. 901-906.
  23. Yong, D. (2006). Plant location selection based on fuzzy TOPSIS. The International Journal of Advanced Manufacturing Technology, Vol. 28(7-8), pp. 839-844.

Full Text

© 2014-2021 Sumy State University.
"Journal of Engineering Sciences"
ISSN 2312-2498 (Print), ISSN 2414-9381 (Online).
All rights reserved.