Fore Sighting and Estimating the Risk of Investing in the Construction of Power Plants Using AHP

Shayan Hosseini¹, Gevork Gharehpetian², Fereshteh Farzianpour^3*

¹Electrical Engineering School, Amirkabir University of Technology, Tehran, Iran.
²Iran Grid Secure Operation Research, Amirkabir University of Technology, Tehran, Iran.
³Department of Health Management and Health Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

DOI: 10.4236/jssm.2015.84053                

Abstract

Today, with advances that have occurred in electricity industry and technology of manufacturing all kinds of power plants whether renewable or perishable, making decision to choose the type and kind of an ideal plant is very important and strategic. For any weakness in determining short, medium and long term parameters affecting deciding whether technical, economical, environmental, social, political , and so on may cause irreparable damage. Also timing and fore sighting factors should be taken into account in decision-making equations. Selecting the type suitable for use in power plants connected to the network or independent sector is the main part of task. Therefore, because there are many variables and factors in the text and the margin of such a task, bed and plant kind selection is very difficult and time consuming. This choice is ultimately influenced by many technical and non-technical measures that are each divided into further subcategories. Due to repetition of this operation in the discussion of issues, finding an efficient way in this area would be very useful. In this paper, a hierarchical decision-making procedure for the selection of the ideal power for productivity and satisfaction in the operation of taking is introduced. That can be generalized to other types of construction and operation concepts in technologies of power plants.

Keywords

Fore Sighting, Risk Estimation, Construction of Power Plants, AHP

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Kahraman, C. (2015) Fuzzy Multi-Criteria Decision-Making: Theory and Applications with Recent Developments. Semay Danesh Press, Tehran, 86-103
[2]	AKKOÇ, S. and Vatansever, K. (2013) Fuzzy Performance Evaluation with AHP and TOPSIS Methods: Evidence from Turkish Banking Sector after the Global Financial Crisis. Eurasian Journal of Business and Economics, 6, 53-74.
[3]	Avazpour, R., Ebrahimi, E. and Fathi, M.R. (2013) A 360 Degree Feedback Model for Performance Appraisal Based on Fuzzy AHP and TOPSIS. International Journal of Economy, Management and Social Sciences, 2, 969-976.
[4]	Deng, H. (1999) Multi-Criteria Analysis with Fuzzy Pair-Wise Comparison. International Journal of Approximate Reasoning, 21, 215-231.
[5]	Fang, C.H., Chang, S.T. and Chen, G.L. (2010) Competency Development among Taiwanese Healthcare Middle Manager: A Test of the AHP Approach. African Journal of Business Management, 4, 2845-2855.
[6]	Kirschen, D.S. and Strbac, G. (2004) Fundamentals of Power System Economics. c, 101-286. http://dx.doi.org/10.1002/0470020598
[7]	Melón, M.G., Beltran, P.A. and Cruz, M.C.G. (2008) An AHP Based Evaluation Procedure for Innovative Educational Projects: A Faceto-Face vs. Computer-Mediated Case Study. Omega, 36, 754-765. http://dx.doi.org/10.1016/j.omega.2006.01.005
[8]	Shafii, M., Hosseini, S.M., Arab, M., Asgharizadeh, E. and Farzianpour, F. (2016) Performance Analysis of Hospital Managers Using Fuzzy AHP and Fuzzy TOPSIS: Iranian Experience. Global Journal of Health Science, 8, 1916-9744.
[9]	Karsak, E.E. (2002) Distance-Based Fuzzy MCDM Approach for Evaluating Flexible Manufacturing System Alternatives. International Journal of Production Research, 40, 3167-3181. http://dx.doi.org/10.1080/00207540210146062
[10]	Kuo, M.S., Tzeng, G.H. and Huang, W.C. (2007) Group Decision Making Based on Concepts of Ideal and Anti-Ideal Points in Fuzzy Environment. Mathematical and Computer Modeling, 45, 324-339. http://dx.doi.org/10.1016/j.mcm.2006.05.006
[11]	Torfi, F., Farahani, R.Z. and Rezapour, S. (2010) Fuzzy AHP to Determine the Relative Weights of Evaluation Criteria and Fuzzy TOPSIS to Rank the Alternatives. Applied Soft Computing, 10, 520-528. http://dx.doi.org/10.1016/j.asoc.2009.08.021
[12]	Tsai, H.Y., Chang, C.W. and Lin, H.L. (2010) Fuzzy Hierarchy Sensitive with Delphi Method to Evaluate Hospital Organization Performance. Expert Systems with Applications, 37, 5533-5541. http://dx.doi.org/10.1016/j.eswa.2010.02.099
[13]	Van Laarhoven, P.J.M. and Pedrcyz, W. (1983) A Fuzzy Extension of Saaty’s Priority Theory. Fuzzy Sets and Systems, 11, 199-227. http://dx.doi.org/10.1016/S0165-0114(83)80082-7
[14]	Yang, T. and Hung, C.C. (2007) Multiple-Attribute Decision Making Methods for Plant Layout Design Problem. Robotics and Computer-Integrated Manufacturing, 23, 126-137. http://dx.doi.org/10.1016/j.rcim.2005.12.002
[15]	Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
[16]	Ayag, Z. and Ozdemir, R.G. (2006) A fuzzy AHP Approach to Evaluating Machine Tool Alternatives. Journal of Intelligent Manufacturing, 17, 179-190. http://dx.doi.org/10.1007/s10845-005-6635-1
[17]	Basligil, H. (2005) The Fuzzy Analytic Hierarchy Process for Software Selection Problems. Journal of Engineering and Natural Sciences, 2, 24-33.
[18]	Bojadziev, G. and Bojadziev, M. (1998) Fuzzy Sets Fuzzy Logic Applications. World Scientific Publishing, Singapore.
[19]	Bozdag, C.E., Kahraman, C. and Ruan, D. (2003) Fuzzy Group Decision Making for Selection among Computer Integrated Manufacturing Systems. Computer in Industry, 51, 13-29. http://dx.doi.org/10.1016/S0166-3615(03)00029-0
[20]	Chang, Y.H., Cheng, C.H. and Wang, T.C. (2003) Performance Evaluation of International Airports in the Region of East Asia. Proceedings of Eastern Asia Society for Transportation Studies, 4, 213-230.
[21]	Chan, F.T.S. and Kumar, N. (2007) Global Supplier Development Considering Risk Factors Using Fuzzy Extended AHP-Based Approach. Omega International Journal of Management Science, 35, 417-431. http://dx.doi.org/10.1016/j.omega.2005.08.004
[22]	Chen, C.T., Lin, C.T. and Huang, S.F. (2006) A Fuzzy Approach for Supplier Evaluation and Selection in Supply Chain Management. International Journal of Production Economics, 102, 289-301. http://dx.doi.org/10.1016/j.ijpe.2005.03.009
[23]	Chou, T.Y. and Liang, G.S. (2001) Application of a Fuzzy Multi-Criteria Decision Making Model for Shipping Company Performance Evaluation. Maritime Policy and Management, 28, 375-392. http://dx.doi.org/10.1080/03088830110049951
[24]	Ertugrul, I. and Tus, A. (2007) Interactive Fuzzy Linear Programming and an Application Sample at a Textile Firm. Fuzzy Optimization and Decision Making, 6, 29-49. http://dx.doi.org/10.1007/s10700-006-0023-y
[25]	Ertugrul, I. and Karakasoglu, N. (2009) Performance Evaluation of Turkish Cement Firms with Fuzzy Analytic Hierarchy Process and TOPSIS Methods. Expert Systems with Applications, 36, 702-715. http://dx.doi.org/10.1016/j.eswa.2007.10.014
[26]	Hakimi, P.N. and Hozhabr K.K. (2008) Efficiency Comparative Analysis of Big Industries Sector in Iran Provinces: By Using Stochastic Frontier Function. Journal of Knowledge and Development, 16, 115-140.
[27]	Zare Naghadehi, M., Mikaeil, R. and Ataei, M. (2009) The Application of Fuzzy Analytic Hierarchy Process (FAHP) Approach to Selection of Optimum Underground Mining Method for Jajarm Bauxite Mine, Iran. Expert Systems with Applications, 36, 8218-8226. www.elsevier.com/locate/eswa http://dx.doi.org/10.1016/j.eswa.2008.10.006
[28]	Al-Eraqi, A.S., Mustafa, A. and Tajudin, K.A. (2010) An Extended DEA Windows Analysis: Middle East and East African Seaports. Journal of Economic Studies, 37, 115-139.
[29]	Wu, D., Liang, L., Huang, Z. and Li, S.X. (2005) Aggregated Ratio Analysis in DEA. International Journal of Information Technology & Decision Making, 4, 369-384. http://dx.doi.org/10.1142/S0219622005001593
[30]	Xu, Z. (2008) On Multi-Period Multiattribute Decision Making. Knowledge-Based Systems, 21, 164-171. http://dx.doi.org/10.1016/j.knosys.2007.05.007