Fuzzy Reasoning in Decision Making and Optimization - Softcover

Inhaltsangabe

1. Fuzzy Sets and Fuzzy Logic.- 1.1 Fuzzy sets.- 1.2 Operations on fuzzy sets.- 1.3 The extension principle.- 1.4 t-norm-based operations on fuzzy numbers.- 1.5 Product-sum of triangular fuzzy numbers.- 1.6 Hamacher-sum of triangular fuzzy numbers.- 1.7 t-norm-based addition of fuzzy numbers.- 1.8 A functional relationship between t-norm-based addition and multiplication.- 1.9 On generalization of Nguyen's theorems.- 1.10 Measures of possibility and necessity.- 1.11 A law of large numbers for fuzzy numbers.- 1.12 Metrics for fuzzy numbers.- 1.13 Possibilistic mean value and variance of fuzzy numbers.- 1.14 Auxiliary lemmas.- 1.15 Fuzzy implications.- 1.16 Linguistic variables.- 2. Fuzzy Multicriteria Decision Making.- 2.1 Averaging operators.- 2.2 Obtaining maximal entropy OWA operator weights.- 2.3 OWA Operators for Ph.D. student selection.- 2.4 Possibility and necessity in weighted aggregation.- 2.5 Benchmarking in linguistic importance weighted aggregations.- 3. Fuzzy Reasoning.- 3.1 The theory of approximate reasoning.- 3.2 Aggregation in fuzzy system modeling.- 3.3 Multiple fuzzy reasoning schemes.- 3.4 Some properties of the compositional rule of inference.- 3.5 Computation of the compositional rule of inference under t-norms.- 3.6 On the generalized method-of-case inference rule.- 4. Fuzzy Optimization.- 4.1 Possibilistic linear equality systems.- 4.2 Sensitivity analysis of ãx = b? and ã?x = b??..- 4.3 Possibilistic systems with trapezoid fuzzy numbers.- 4.4 Flexible linear programming.- 4.5 Fuzzy linear programming with crisp relations.- 4.6 Possibilistic linear programming.- 4.7 Possibilistic quadratic programming.- 4.8 Multiobjective possibilistic linear programming.- 5. Fuzzy Reasoning for Fuzzy Optimization.- 5.1 Fuzzy reasoning for FMP.- 5.1.1 Extension to nonlinear FMP.- 5.1.2 Relation to classical LP problems.- 5.1.3 Crisp objective and fuzzy coefficients in constraints.- 5.1.4 Fuzzy objective function and crisp constraints.- 5.1.5 Relation to Zimmermann's soft constraints.- 5.1.6 Relation to Buckley's possibilistic LP.- 5.2 Optimization with linguistic variables.- 5.3 Multiobjective optimization with lingusitic variables.- 5.4 Interdependent multiple criteria decision making.- 5.4.1 The linear case.- 5.4.2 Application functions.- 5.5 MOP with interdependent objectives.- 5.6 Additive linear interdependences.- 5.7 Additive nonlinear interdependences.- 5.8 Compound interdependences.- 5.9 Biobjective interdependent decision problems.- 6. Applications in Management.- 6.1 Nordic Paper Inc.- 6.1.1 Outline of a macro algorithm.- 6.2 A fuzzy approach to real option valuation.- 6.2.1 Probabilistic real option valuation.- 6.2.2 A hybrid approach to real option valuation.- 6.3 The Woodstrat project.- 6.3.1 Fuzzy hyperknowledge support systems.- 6.3.2 Cognitive maps for hyperknowledge representation.- 6.3.3 Adaptive FCM for strategy formation.- 6.4 Soft computing methods for reducing the bullwhip effect.- 6.4.1 The bullwhip effect, some additional details.- 6.4.2 Explanations for the bullwhip effect: standard results.- 6.4.3 Demand signal processing.- 6.4.4 Order batching.- 6.4.5 Price variations.- 6.4.6 A fuzzy approach to demand signal processing.- 6.4.7 A fuzzy logic controller to demand signal processing.- 6.4.8 A hybrid soft computing platform for taming the bullwhip effect.- 7. Future Trends in Fuzzy Reasoning and Decision Making.- 7.1 Software agents and agent-based systems.- 7.2 Intelligence and software agents.- 7.3 Scenario agents.- 7.3.1 The scenario agent: basic functionality.- 7.4 Scenarios and scenario planning: key features.- 7.5 Forecasting.- 7.6 Industry foresight.- 7.7 The scenario agent.- 7.7.1 Support for OW scenarios.- 7.7.2 Support for model-based scenarios.- 7.7.3 Support for scenario building and foresight.- 7.8 Interpretation agent.- 7.8.1 The interpretation agent: basic functionality.- 7.9 Coping with imprecision.- 7.10 Interpretation in a business environment.- 7.11 Mental models and cogniti

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