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Use of a Simple Multi-Attribute Value Function Incorporating Visual Interactive Sensitivity Analysis for Multiple Criteria Decision Making

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Readings in Multiple Criteria Decision Aid

Abstract

V·I·S·A is a computer program for multiple criteria decision aid, based on a simple weighted multi-attribute value function, incorporating a hierarchical structure of criteria and visual interactive sensitivity analysis. The use of a model of this kind as an aid to multi-attribute decision making is nothing new. Long before MCDM became an established field of study in the 1970’s use of models of this kind been reported in the literature, for example, Churchman and Ackoff, 1954. Many popular approaches to decision aiding adopt the same framework, for example, Edwards’ SMART (Edwards, 1982), the Analytic Hierarchy Process developed by Saaty (Saaty, 1980), Social Cost Benefit Analysis (Lichfield et al., 1975). The approach is best suited to the problem of choosing a preferred alternative from a set of well defined alternatives, or to indicate a preference ordering over such a set of alternatives. There are many other approaches suited to this type of problem, for example, the Electre methods and Promethee. However, few approaches have been developed to handle a situation in which the decision maker wants to take account of very many criteria; in such a situation those methods incorporating a hierarchical structure of criteria are most appropriate, namely the multi-attribute value function and the Analytic Hierarchy Process.

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References

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© 1990 Springer-Verlag Berlin · Heidelberg

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Belton, V., Vickers, S. (1990). Use of a Simple Multi-Attribute Value Function Incorporating Visual Interactive Sensitivity Analysis for Multiple Criteria Decision Making. In: Bana e Costa, C.A. (eds) Readings in Multiple Criteria Decision Aid. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-75935-2_14

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  • DOI: https://doi.org/10.1007/978-3-642-75935-2_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-75937-6

  • Online ISBN: 978-3-642-75935-2

  • eBook Packages: Springer Book Archive

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