By Iwona Skalna, Bogdan Rębiasz, Bartlomiej Gawel, Beata Basiura, Jerzy Duda, Janusz Opila, Tomasz Pelech-Pilichowski

ISBN-10: 331926494X

ISBN-13: 9783319264943

This publication indicates how universal operation administration tools and algorithms could be prolonged to accommodate obscure or obscure details in decision-making difficulties. It describes tips on how to mix choice timber, clustering, multi-attribute decision-making algorithms and Monte Carlo Simulation with the mathematical description of obscure or obscure info, and the way to imagine such info. furthermore, it discusses a huge spectrum of real-life administration difficulties together with forecasting the obvious intake of metal items, making plans and scheduling of creation procedures, undertaking portfolio choice and economic-risk estimation. it's a concise, but accomplished, reference resource for researchers in decision-making and decision-makers in company companies alike.

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2001. Fuzzy programming with fuzzy decisions and fuzzy simulationbased genetic algorithm. Fuzzy Sets and Systems 122(2): 253–262. 35. R¸ebiasz, B. 2013. Selection of efficient portfoliosprobabilistic and fuzzy approach, comparative study. Computers & Industrial Engineering 64(4): 1019–1032. 26 1 Fuzzy Numbers 36. , S. Uejima, and K. Asai. 1982. Linear regression analysis with fuzzy model. IEEE Transactions on Systems, Man, and Cybernetics, SMC-12, 6:903–907. 37. Tanaka, H. 1987. Fuzzy data analysis by possibilistic linear models.

The method aims to determine the value of fˆ( X), where Xˆ = (X 1 , X 2 , . . , X m ) is a vector of variables burdened with uncertainty. It is assumed that there are k (k < m) random variables (X 1 , X 2 , . . , X k ) and m − k fuzzy variables X˜ k+1 , X˜ k+2 , . . , X˜ m . Additionally, it is assumed that there may be defined subsets X K of correlated variables X i ; X K = {X i | i ∈ K }, K ∈ K s . In such case, K is the subset of correlated variable indices, and K s is the set of indices of the selected subsets of correlated variables.

Journal of Mathematical Analysis and Applications 114(2): 409–422. 7. , and P. Kloeden. 1994. Metric spaces of fuzzy sets: Theory and applications. World Scientific. 8. Kruse, R. 1982. The strong law of large numbers for fuzzy random variables. Information Science 28(3): 233–241. 9. Krätschmer, V. 2001. A unified approach to fuzzy random variables. Fuzzy Sets and Systems 123(1): 1–9. 10. , and B. Liu. 2003. Fuzzy random variables: A scalar expected value operator. Fuzzy Optimization and Decision Making 2(2): 143–160.

### Advances in Fuzzy Decision Making. Theory and Practice by Iwona Skalna, Bogdan Rębiasz, Bartlomiej Gawel, Beata Basiura, Jerzy Duda, Janusz Opila, Tomasz Pelech-Pilichowski

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