Buradasınız

Anasayfa » Content

A New Assessment Method of new Energy in Regional Sustainable Development based on Hesitant Fuzzy Information

Journal Name:

  • Journal of Industrial Engineering and Management

Publication Year:

  • 2014
DOI: 
http://dx.doi.org/10.3926/jiem.1299

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
Purpose: The new energy has been an important driving force in region sustainable development. It is a critical issue to evaluate the role of new energy in region sustainable development. Design/methodology/approach: To deal with this issue, this paper proposes a new score function, in which, both mean and variance are considered. Then it introduces the basic operators, such as hesitant fuzzy weighted averaging operator and hesitant fuzzy weighted geometric operator to get the comprehensive assessment provided by the decision maker on each attribute. Findings: Due to the drawbacks of existing methods with hesitant fuzzy information, this paper puts forward a method and the procedure to solve the MADM (multiple attribute decision making) problem. And an illustrative example is demonstrated to verify the reliability of the proposed method. Research limitations/implications: The method can be used to evaluate the new energy in regional sustainable development, but it cannot solve the problems with many experts. Practical implications: Based on the new framework, a case study is carried out to verify its applicability and validity. The research can fill the gaps for the assessment framework of new energy in regional sustainable development. This paper is of practical value in real life, which is the application of some techniques. Originality/value: This paper describes in detail in evaluating the role of new energy in region sustainable development. And a new score function is proposed with hesitant fuzzy information, that is, the idea of variance is introduced to form a new score function to measure the deviation of hesitant fuzzy elements. Meanwhile, the basic operator, such as hesitant fuzzy weighted averaging operator and hesitant fuzzy weighted geometric operator are introduced to integrate the hesitant fuzzy information.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-new-assessment-method-new-energy-regional-sustainable-development-based-hesitant-fuzzy-information.pdf
  • 5
1334
1346
  • English

REFERENCES

References: 

Afgan, N.H. & Carvalho, M.G. (2008) . Sustainability assessment of a hybrid energy system.
Energy Policy, 36, 2903-2910. http://dx.doi.org/10.1016/j.enpol.2008.03.040
Bilgen, S., Keles, S., Kaygusuz, A., Sari, A., & Kaygusuz, K. (2008) . Global warming and
renewable energy sources for sustainable development: A case study in Turkey. Renewable
and Sustainable Energy Reviews, 12, 372-396. http://dx.doi.org/10.1016/j.rser.2006.07.016
Carrera, D.G. & Mack, A. (2010). Sustainability assessment of energy technologies via social
indicators: Results of a survey among European energy experts. Energy Policy, 38,
1030-1039. http://dx.doi.org/10.1016/j.enpol.2009.10.055
Dubois, D., & Prade, H. (1985). A review of fuzzy sets aggregation connectives. Information
Science, 36, 85-121. http://dx.doi.org/10.1016/0020-0255(85)90027-1
Evans, A., Strezon, V., & Evans, T.J. (2009) . Assessment of sustainability indicators for
renewable energy technologies. Renewable and Sustainable Energy Reviews, 13, 1082-1088.
http://dx.doi.org/10.1016/j.rser.2008.03.008
Hawila, D., Mondal, M., Kennedy, S., & Mezher, T. (2014) . Renewable energy readiness
assessment for North African Countries. Renewable and Sustainable Energy Reviews, 33,
128-140. http://dx.doi.org/10.1016/j.rser.2014.01.066
Kemmler, A., & Spreng, D. (2007). Energy indicators for tracking sustainability in developing
countries. Energy Policy, 35, 2466-2480. http://dx.doi.org/10.1016/j.enpol.2006.09.006
Krupa, J., & Burch, S. (2011) . A new energy future for South Africa: The political ecology of
South African renewable energy. Energy Policy, 39, 6254-6261.
http://dx.doi.org/10.1016/j.enpol.2011.07.024
Omer, A.M. (2008) . Energy, environment and sustainable development. Renewable and
Sustainable Energy Reviews, 12, 2265-2300. http://dx.doi.org/10.1016/j.rser.2007.05.001
Pang, X., Mortberg, U., & Brown, N. (2014). Energy models from a strategic environmental
assessment perspective in an EU context-What is missing concerning renewables? Renewable
and Sustainable Energy Reviews, 33, 353-362. http://dx.doi.org/10.1016/j.rser.2014.02.005
Sahir, M.H., & Qureshi, A.H. (2008) . Assessment of new and renewable energy resources
potential and identification of barriers to their significant utilization in Pakistan. Renewable
and Sustainable Energy Reviews, 12, 290-298. http://dx.doi.org/10.1016/j.rser.2006.07.002
Singh, R.K., Murty, H.R., Gupta, S.K., & Dikshit, A.K. (2009). An overview of sustainability
assessment methodologies. Ecological Indicators, 9, 189-212.
http://dx.doi.org/10.1016/j.ecolind.2008.05.011
Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6),
225-235. http://dx.doi.org/10.1002/int.20418
Torra, V., & Narukawa, Y. (2009). On hesitant fuzzy sets and decision. 2009 IEEE International
Conference on Fuzzy Systems, 3, 1378-1382. http://dx.doi.org/10.1109/FUZZY.2009.5276884
Valochi, M., Juliano, J., & Schurr, A. (2014) . Switching Perspectives: Creating new business
model for a changing world of energy. Smart Grid Applications and Developments Green
Energy and Technology, 165-182. http://dx.doi.org/10.1007/978-1-4471-6281-0_9
Wei, G.W. (2012). Hesitant fuzzy prioritized operators and their application to multi attribute
decision making. Knowledge-Based Systems, 31, 176-182.
http://dx.doi.org/10.1016/j.knosys.2012.03.011
Xia, M.M., & Xu, Z.S. (2011). Hesitant fuzzy information aggregation in decision making.
International Journal of Approximate Reasoning, 52(3), 395-407.
http://dx.doi.org/10.1016/j.ijar.2010.09.002
Xu, Z.S., & Xia, M.M. (2011). Distance and similarity measures for hesitant fuzzy sets.
Information Sciences, 181, 2128-2138. http://dx.doi.org/10.1016/j.ins.2011.01.028
Xu, Z.S., & Zhang. X.L. (2013). Hesitant fuzzy multi-attribute decision making based on
TOPSIS with incomplete weight information. Knowledge-Based Systems, 52, 53-64.
http://dx.doi.org/10.1016/j.knosys.2013.05.011
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338-356.
http://dx.doi.org/10.1016/S0019-9958(65)90241-X
Zimmermann, H.J. (1985). Fuzzy Set Theory and Its Applications. Kluwer Academic Publishers,
Dordrecht. http://dx.doi.org/10.1007/978-94-015-7153-1

Thank you for copying data from http://www.arastirmax.com