Measuring the Effects of Different Factors on Activity Times in Wind Power Projects under Fuzzy Set Theory

Year 2025, Volume: 38 Issue: 3, 1374 - 1386, 01.09.2025

Pınar Kaya Samut

https://doi.org/10.35378/gujs.1599844

Abstract

In this study, it is aimed to determine the project completion time in a more realistic way by analyzing the effects of uncertainties experienced in wind power projects. For this purpose, a method was applied to calculate the project completion time under fuzzy set theory by including the risk factors in the relevant activity durations of an international company's wind power project. CPM and F-PERT methods were also applied to show the robustness of the applied method, and these three methods' results were benchmarked against the actual time of the project. The result of the study showed that a more realistic estimate of the project completion time can be determined with the applied method, in which all the factors affecting the duration of the activities are included, experts’ opinions are taken into consideration, and historical data is used. In this study, in which the risk factors for wind power projects are examined in detail and suggestions for improvement are put forward, a customized roadmap for the management of all types of projects, particularly wind power projects, is intended to serve decision makers.

Keywords

Fuzzy set theory , Project management , Renewable energy , Wind power

References

• [1] Haghighi, M.H., Mousavi, S.M., Antuchevıčıenė, J., and Mohagheghi, V., “A new analytical methodology to handle time-cost trade-off problem with considering quality loss cost under interval-valued fuzzy uncertainty”, Technological and Economic Development of Economy, 25(2): 277-299, (2019). DOI: https://doi.org/10.3846/tede.2019.8422.
• [2] Kelley, J.E., “Critical path planning and scheduling – Mathematical basis”, Operations Research, 9(3): 296-320, (1961). DOI: https://doi.org/10.1287/opre.9.3.296.
• [3] Zammori, F.A., Braglia, M., and Frosolini, M., “A fuzzy multi-criteria approach for critical path definition”, International Journal of Project Management, 27(3): 278–291, (2009). DOI: https://doi.org/10.1016/j.ijproman.2008.03.006.
• [4] Beaula, T., and Vijaya, V., “A new method to find critical path from multiple paths in project networks”, International Journal of Fuzzy Mathematical Archive, 9(2): 235-243, (2015). DOI: http://dx.doi.org/10.22457/ijfma.v16n1a9.
• [5] Malcolm, D.G., Roseboom, J.H., Clark, C.E., and Fazar, W., “Application of a technique for research and development project evaluation”, Operations Research, 7(5): 646–669, (1959). DOI: https://doi.org/10.1287/opre.7.5.646.
• [6] Farnum, N.R., and Stanton, L.W., “Some results concerning the estimation of beta distribution parameters in PERT”, Journal of the Operational Research Society, 38(3): 287-290, (1987). DOI: https://doi.org/10.1057/jors.1987.45.
• [7] Ammar, M.A., and Abd-ElKhalek, S.I. “Criticality measurement in fuzzy project scheduling”, International Journal of Construction Management, 22(2): 252-261, (2019). DOI: https://doi.org/10.1080/15623599.2019.1619226.
• [8] Chanas, S., Dubois, D., and Zielinski, P., “On the sure criticality of asks in activity networks with imprecise durations”, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 32(4): 393–407, (2002). DOI: https://doi.org/10.1109/TSMCB.2002.1018760.
• [9] Habibi, F., Birgani, O.T, Koppelaar, H., and Radenović, S., “Using fuzzy logic to improve the project time and cost estimation based on Project Evaluation and Review Technique (PERT)”, Journal of Project Management, 3(4): 183-196, (2018). DOI: https://doi.org/10.5267/j.jpm.2018.4.002.
• [10] Dubois, D., Fargier, H., and Fortemps, P., “Fuzzy scheduling: modelling flexible constraints vs. coping with incomplete knowledge”, European Journal of Operational Research, 147(2): 231-252, (2003). DOI: https://doi.org/10.1016/S0377-2217(02)00558-1.
• [11] Morovatdar, R., Aghaie, A., Roghanian, E., and Asl-Haddad, A., “An algorithm to obtain possibly critical paths in imprecise project networks”, Iranian Journal of Operations Research, 4(1): 39–54, (2013). Access: https://www.researchgate.net/publication/333557147_An_Algorithm_to_Obtain_Possibly_Critical_Paths_in_Imprecise_Project_Networks.
• [12] Ock, J-H., and Han S-H., “Measuring risk-associated activity's duration: A fuzzy set theory application”, KSCE Journal of Civil Engineering, 14(5): 663-671, (2010). DOI: https://doi.org/10.1007/s12205-010-1003-x.
• [13] Nguyen, V.T., Hai, N.H., and Lan, N.T.K., “Spherical fuzzy multicriteria decision-making model for wind turbine selection in a renewable energy Project”, Energies, 15(3): 1-12, (2022). DOI: https://doi.org/10.3390/en15030713.
• [14] Guo, S-J., Chen, J-H., and Chiu, C-H., “Fuzzy duration forecast model for wind turbine construction project sıbject to the impact of wind uncertainty”, Automation Construction, 81: 401-410, (2017). DOI: https://doi.org/10.1016/j.autcon.2017.03.009.
• [15] Marimuthu, C., and Kirubakaran, V., “Carbon pay back period for solar and wind energy project installed in India: A critical review”, Renewable and Sustainable Energy Reviews, 23: 80-90, (2013). DOI: https://doi.org/10.1016/j.rser.2013.02.045.
• [16] Öztürk, S., Fthenakis, V., and Faulstich, S., “Failure modes, effects and criticality analysis for wind turbines considering climatic regions, and comparing geared and direct drive wind turbines”, Energies, 11(9): 1-18, (2018). DOI: https://doi.org/10.3390/en11092317.
• [17] Republic of Türkiye Ministry of energy and natural resources, Renewable energy sources. https://enerji.gov.tr/eigm-yenilenebilir-enerji-kaynaklar-ruzgar, Access date: 05.09.2022.
• [18] Turkish Wind Energy Association, https://tureb.com.tr//lib/uploads/2d7a823a65b9af8u.pdf , Access date: 05.09.2022.
• [19] Anadolu Agency, https://www.aa.com.tr/tr/ekonomi/ruzgar-yukselen-kapasitesiyle-yenilenebilir-enerjidepayini-artiriyor/2272927), Access date: 14.06.2021.
• [20] Anadolu Agency, https://www.aa.com.tr/tr/cevre/kuresel-ruzgar-enerjisi-kapasitesi-5-yilda-556-gigavatartacak/2559265#:~:text=AA%20muhabirinin%2C%20K%C3%BCresel, Access date: 09.04.2022.
• [21] Kang, H-Y., Lee, A.H.I., and Huang, T-T., “Project management for a wind turbine construction by applying fuzzy multiple objective linear programming models”, Energies, 9(12): 1-15, (2016). DOI: https://doi.org/10.3390/en9121060.
• [22] Lee, A.H.I., Kang, H-Y., and Huang, T.T., “Project management model for constructing a renewable plant”, Procedia Engineering, 174: 145-154, (2017). DOI: https://doi.org/10.1016/j.proeng.2017.01.186.
• [23] Taghipour, M., Shamami, N., Lotfi, A., and Maryan, P., “Evaluating project planning and control system in multi-project organizations under fuzzy data approach considering resource constraints (case study: wind tunnel construction project)”, Management, 3(1): 29-46, (2020). DOI: https://doi.org/10.31058/j.mana.2020.31003.
• [24] Mohammed, E., Seresht, N.G., Hague, S., Chehouri, A., and AbouRizk, S., “Domain-specific risk assessment using integrated simulation: a case study of an onshore wind Project”, Canadian Journal of Civil Engineering, 49(5): 770-782, (2022). DOI: https://doi.org/10.1139/cjce-2021-0099.
• [25] Ock, J.H., “Activity duration quantification under uncertainty: fuzzy set theory application”, Cost Engineering, 38(1): 26-29, (1996). DOI: https://www.proquest.com/openview/6f810ac7d39d14f8a5e8f34b298804d6/ 1?pq-origsite=gscholar&cbl=49080.
• [26] Zadeh, L.A., “Toward a generalized theory of uncertainty (GTU) – an outline”, Information Sciences, 172(1-2): 1-40, (2005). DOI: https://doi.org/10.1016/j.ins.2005.01.017.
• [27] Chen, C-T., and Huang, S-F., “Applying fuzzy method for measuring criticality in project network”, Information Sciences, 177(12): 2448-2458, (2007). DOI: https://doi.org/10.1016/j.ins.2007.01.035.
• [28] Dubios, D., and Prade, H., Théorie des Possibilités. Applications à la Représentation des Connaissances en Informatique, 2nd edition, Masson, Paris, (1988).
• [29] Paek, J.H., Lee, Y.W., and Ock, J.H., “Pricing construction risk: Fuzzy set application”, Journal of Construction Engineering and Management, 119(4): 743-756, (1993). DOI: https://ascelibrary.org/doi/10.1061/%28ASCE%290733-9364%281993%29119%3A4%28743%29.
• [30] Bogardi, I., and Bardossy, A., “Regional management of an aquifer for mining under fuzzy environmental objectives”, Water Resources Research, 19(6): 1394-1402, (1983). DOI: https://doi.org/10.1029/WR019i006p01394.
• [31] Li, R.J., and Lee, E.S., “Ranking fuzzy numbers---a comparison”, Proc. of NAFIPS, West Lafayette, Indiana, 169-204, (1987).
• [32] McCahon, C.S., and Lee, E.S., “Project network analysis with fuzzy activity times”, Computers and Mathematics with Applications, 15(10): 829-838, (1988). DOI: https://doi.org/10.1016/0898-1221(88)90120-4.
• [33] Eroglu, O., Aktas Potur, E., Kabak, M., Gencer, C., “A Literature Review: Wind Energy Within The Scope of MCDM Methods”, Gazi University Journal of Science, 36(4): 1578-1599 (2023). DOI: 10.35378/gujs.1090337.