By Witold Pedrycz
Fuzzy Multicriteria Decision-Making: types, Algorithms and Applications addresses theoretical and functional gaps in contemplating uncertainty and multicriteria elements encountered within the layout, making plans, and keep an eye on of complicated platforms. together with all prerequisite wisdom and augmenting a few elements with a step by step clarification of extra complicated ideas, the authors offer a scientific and entire presentation of the innovations, layout technique, and certain algorithms. those are supported via many numeric illustrations and a few program situations to encourage the reader and make a few summary thoughts extra tangible.
Fuzzy Multicriteria Decision-Making: versions, Algorithms and Applications will entice a large viewers of researchers and practitioners in disciplines the place decision-making is paramount, together with quite a few branches of engineering, operations examine, economics and administration; it's going to even be of curiosity to graduate scholars and senior undergraduate scholars in classes similar to choice making, administration, danger administration, operations examine, numerical tools, and knowledge-based systems.Content:
Chapter 1 Decision?Making in approach venture, making plans, Operation, and keep watch over: Motivation, goals, and uncomplicated suggestions (pages 1–19):
Chapter 2 Notions and ideas of Fuzzy units: An advent (pages 21–62):
Chapter three chosen layout and Processing points of Fuzzy units (pages 63–102):
Chapter four non-stop types of Multicriteria Decision?Making and their research (pages 103–136):
Chapter five advent to choice Modeling with Binary Fuzzy kinfolk (pages 137–153):
Chapter 6 development of Fuzzy choice family (pages 155–191):
Chapter 7 Discrete versions of Multicriteria Decision?Making and their research (pages 193–246):
Chapter eight Generalization of a vintage method of facing Uncertainty of knowledge for Multicriteria selection difficulties (pages 247–261):
Chapter nine staff Decision?Making: Fuzzy types (pages 263–291):
Chapter 10 Use of Consensus Schemes in workforce Decision?Making (pages 293–333):
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Extra resources for Fuzzy Multicriteria Decision-Making: Models, Methods and Applications
For instance, “x” can be a decision variables and fuzzy set A is an elastic constraint characterizing feasible values and decision-maker preferences. In this case A(v) denotes the grade of preference in favor of “v” as the value of “x”. This interpretation prevails in fuzzy optimization and decision analysis. For instance, we may be interested in finding a comfortable value of temperature. The membership degree of a candidate temperature value “v” reflects our degree of satisfaction with the particular temperature value chosen.
But, in the case of a person’s height, imprecision remains. 3). In contrast, probability is a set function, a mapping whose universe is a set of subsets of a domain. Second, there are differences between fuzziness, generality, and ambiguity. A notion is general when it applies to a multiplicity of objects and keeps only a common essential property. An ambiguous notion stands for several unrelated objects. Therefore, from this point of view fuzziness does not mean either generality or ambiguity and applications of fuzzy sets exclude these categories.
Fuzzy Sets and Systems, 114, (1), 43–58. L. (1995) A sequential selection process in group decision-making with linguistic assessment. Information Sciences, 85 (2), 223–239. L. S. (1979) Multiple Objective Decision-Making: Methods and Applications, SpringerVerlag, Berlin. L. and Yoon, K. (1981) Multiple Attribute Decision-Making: Methods and Applications – A State-of-the-Art Survey, Springer-Verlag, Berlin. Keeney, R. and Raifa, H. , New York. S. (1962) The Structure of Scientific Revolutions, University of Chicago Press, Chicago.
Fuzzy Multicriteria Decision-Making: Models, Methods and Applications by Witold Pedrycz