By Marek Makowski, Andrzej P. Wierzbicki (auth.), Professor Xinghuo Yu, Professor Janusz Kacprzyk (eds.)

ISBN-10: 3540370080

ISBN-13: 9783540370086

ISBN-10: 3642535348

ISBN-13: 9783642535345

Soft computing has supplied subtle methodologies for the advance of clever choice aid platforms. quickly advances in gentle computing applied sciences, similar to fuzzy common sense and structures, man made neural networks and evolutionary computation, have made to be had robust challenge illustration and modelling paradigms, and studying and optimisation mechanisms for addressing glossy determination making concerns. This publication offers a complete insurance of updated conceptual frameworks in extensively perceived choice help platforms and winning functions. various from different current books, this quantity predominately makes a speciality of utilized determination aid with tender computing. components lined contain making plans, administration finance and management in either the non-public and public sectors.

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Thus, Pareto-optimality is an essentially weaker concept than single-criterion optimality. Pareto-optimality does not tell us which decisions to choose, it tells us only which decisions to avoid. This nonuniqueness of Pareto-optimal decisions has been considered a drawback in classical decision analysis; thus, in addition to a substantive model, a complete preferential model was usually assumed that specified adefinite utility or value function whose maximum defined - hopefully, uniquely - "the optimal" decision and outcome.

This initial phase might be supplemented, if necessary, with various other techniques of using reference points when supporting choice. Experience in multi-objective model analysis shows that the initial phase of learning by freely changing reference points might be most important, particularly for supporting the development of intuition of the decision maker or the modeler. 7 Sensitivity analysis In mathematical programming sensitivity analysis is typically understood as analysis of changes of an optimal solution caused by change of the data in the model.

This method might work in a much more general setting than the method of using weighting coefficients, but it is more complicated mathematically. At first glance, an appealing mathematical method would be to minimize a distance measure or simply a norm of the difference between the goal and the attainable objective vector. Such techniques of norm minimization were first used historically, either in the displaced ideal method of Zeleny [89] or in the broad family of goal programming techniques starting with the work of Charnes and Cooper [13].

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Applied Decision Support with Soft Computing by Marek Makowski, Andrzej P. Wierzbicki (auth.), Professor Xinghuo Yu, Professor Janusz Kacprzyk (eds.)

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