By Iwona Skalna, Bogdan Rębiasz, Bartlomiej Gawel, Beata Basiura, Jerzy Duda, Janusz Opila, Tomasz Pelech-Pilichowski
This booklet indicates how universal operation administration tools and algorithms will be prolonged to accommodate obscure or obscure details in decision-making difficulties. It describes tips to mix determination timber, clustering, multi-attribute decision-making algorithms and Monte Carlo Simulation with the mathematical description of obscure or imprecise details, and the way to imagine such info. furthermore, it discusses a huge spectrum of real-life administration difficulties together with forecasting the plain intake of metal items, making plans and scheduling of construction approaches, undertaking portfolio choice and economic-risk estimation. it's a concise, but accomplished, reference resource for researchers in decision-making and decision-makers in enterprise agencies alike.
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W. Lu. 2001. An approximate approach for ranking fuzzy numbers based on left and right dominance. Computers and Mathematics with Applications 41(12): 1589–1602. 29. , and D. Filev. 1999. On ranking fuzzy numbers using valuations. International Journal of Intelligent Systems 14(12): 1249–1268. Chapter 3 Fuzzy Random Variable and the Dempster-Shafer Theory of Evidence Abstract This chapter presents the concept of uncertainty propagation in real world desicion problems, where some input parameters are stochastic while information about others is partial and is represented by fuzzy random variable.
B. L. S. Chin. 2006. On the centroids of fuzzy numbers. Fuzzy Sets and Systems 157(7): 919–926. 21. J. Jamal. 2010. Centroid-point of ranking fuzzy numbers and its application to health related quality of life indicators. International Journal on Computer Science and Engineering 2(8): 2773–2777. 22. Yager, R. 1981. A procedure for ordering fuzzy subsets of the unit interval. Information Sciences 24(2): 143–161. 23. Saneifard, R. 2009. Ranking L-R fuzzy numbers with weighted averaging based on levels.
J. Jamal. 2010. Centroid-point of ranking fuzzy numbers and its application to health related quality of life indicators. International Journal on Computer Science and Engineering 2(8): 2773–2777. 22. Yager, R. 1981. A procedure for ordering fuzzy subsets of the unit interval. Information Sciences 24(2): 143–161. 23. Saneifard, R. 2009. Ranking L-R fuzzy numbers with weighted averaging based on levels. International Journal of Industrial Mathematics 1(2): 163–173. 24. , and A. Mert. 2007. On methods of defuzzification of parametrically represented fuzzy numbers.