1. Abubakar, M., and B. Umar, 2006. Comparison of energy use patterns in Maiduguri and yoke flour mills Nigeria. The CIGR Journal of Scientific Research and Development, Agricultural Engineering International 16. Available at: http://cigrjournal.org/index.php/Ejounral/article/view/671. Accessed to May 2006.
2. Bagheri Neshani, A., A. A. Zeraei, and M. Bahadorifar. 2013. Impact assessment of economic, social and ecological production of bioethanol from SC and maize in rural areas. Second National Conference on Renewable Energy and Clean, Tehran. 10 Pages. (In Farsi).
3. Balocco, C., and D. Verdesca. 2007. Shannon entropy for energy technologies ex-ante evaluation. International Journal of Environmental Technology and Management 7(1/2): 197-217. Available at: http://dx.doi.org/10.1504/IJETM.2007.013245. Accessed may 2007.
4. Brasil. 2012. Ministe´ rio de Minas e Energia. Balanc¸o energe´tico nacional. Brası´lia. Available at: https://ben.epe.gov.br/downloads/S%c3%adntese%20do%20Relat%c3%b3rio%20Final_2012_Web.pdf.
5. Chen, S. J., and C. L. Hwang. 1992. Fuzzy Multiple Attribute Decision Making: Methods and Applications. Springer Verlag, New York.
6. Heragu, S. 1997. Facilities Design. PWS Publishing, Boston. Massachusetts.
7. Hwang, C. L., and Yoon, K. 1981. Multiple Attribute Decision Making – Method and Applications, A State of the Art Survey. Springer Verlag, New York.
8. Kabassi, K., and M. Virvou. 2004. Personalised adult e-training on computer use based on multiple attribute decision making. Interacting with Computers 16, 115–132. Available at: http://www.sciencedirect.com/science/article/pii/S0953543803001127. Accessed February 2004.
9. Knoll, J. E., W. F. Anderson, T. C. Strickland, R. K. Hubbard, and R. Malik. 2012. Low-input production of biomass from perennial grasses in the coastal plain of Georgia, USA. Bioenergy Research 5 (1): 206-214. Available at: http://link.springer.com/article/10.1007%2Fs12155-011-9122-x. Accessed May 2012.
10. Maccrimmon, K. R. 1968. Decision making among multiple attribute alternatives: A survey and consolidated approach. RAND Memorandum, RM-4823-ARPA. 78 pages. Available at: http://www.rand.org/pubs/research_memoranda/RM4823.html. Accessed December 1968.
11. Mantoam, J. E., M. Milan, M. L. Gimenez, and L. Th. Romaneli. 2014. Embodied energy of SC harvesters. Biosystem Engineering. 155-166. Available at: http://www.sciencedirect.com/science/article/pii/S1537511013001992. Accessed January 2014.
12. Mathanker, S. K., H. Gan, J. C. Buss, B. Lawson, A. C. Hansen, and K. C. Ting. 2015. Power requirements and field performance in harvesting EC and SC. Biomass and Bioenergy 75: 227-234. Available at: http://www.sciencedirect.com/science/article/pii/S0961953415000616. Accessed April 2015.
13. Mislevy, P., and R. C. Fluck. 1992. Harvesting operations and energetics of tall grasses for biomass energy production: a case study. Biomass Bioenergy 3 (6): 381-387. Available at: http://www.sciencedirect.com/science/article/pii/096195349290033M. Accessed June 1992.
14. Mislevy, P., F. G.Martin, M. B. Adjei, and D. J. Miller. 1995. Agronomic characteristics of US 72-1153 energycane for biomass. Biomass and Bioenergy. 449-457. Available at: http://www.sciencedirect.com/science/article/pii/096195349500050X. Accessed May 1995.
15. Richard, E. P., and W. F. Anderson. 2014. SC, EC, and napiergrass. In: Karlen DL, editor. Cellulosic energy cropping systems. John Wiley & Sons, Ltd. p. 91-108.
16. Salassi, M. E. and Barker, F.G. 2008. Reducing harvest costs through coordinated SC harvest and transport operations in Louisiana. Journal Assoc SC Technol 28: 32-41. Available at: http://agris.fao.org/agris-search/search.do?recordID=US201301692925.
17. Shakouri, H., M. Nabaee, and S. Aliakbarisani. 2014. A quantitative discussion on the assessment of power supply technologies: DEA (data envelopment analysis) and SAW (simple additive weighting) as complementary methods for the “Grammar”. Energy (64): 640-647. Available at: http://www.sciencedirect.com/science/article/pii/S0360544213008712. Accessed January 2014.
18. Wang, Y. J. 2015. A fuzzy multi-criteria decision-making model based on simple additive weighting method and relative preference relation. Applied Soft Computing 30: 412-420. Available at: http://www.sciencedirect.com/science/article/pii/S1568494615000903. Accessed May 2015.
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