IAC-DIDAS-N: A Dynamic Interactive Decision Analysis and Support System for Multicriteria Analysis of Nonlinear Models with Nonlinear Model Generator Supporting Model Analysis

Kreglewski, T., Paczynski, J., Granat, J., & Wierzbicki, A.P. (1988). IAC-DIDAS-N: A Dynamic Interactive Decision Analysis and Support System for Multicriteria Analysis of Nonlinear Models with Nonlinear Model Generator Supporting Model Analysis. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-88-112

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Abstract

This paper is one of the series of 11 Working Papers presenting the software for interactive decision support and software tools for developing decision support systems. These products constitute the outcome of the contracted study agreement between the System and Decision Sciences Program at IIASA and several Polish scientific institutions. The theoretical part of these results is presented in the IIASA Working Paper WP-88-071 entitled "Theory, Software and Testing Examples in Decision Support Systems". This volume contains the theoretical and methodological backgrounds of the software systems developed within the project.

This paper presents the user documentation for decision analysis and support systems of DIDAS family designed for supporting decision problems when the model of the system under study can be formulated in terms of set of nonlinear equations. The program presented in the paper, called IAC-DIDAS-N is provided with a nonlinear model generator and editor that support definition, edition and symbolic differentiation of nonlinear models for multiobjective decision analysis. A specially introduced standard of defining nonlinear programming models for multiobjective optimization helps to connect the model generator with other parts of the system. Optimization runs involved in interactive, multiobjective decision analysis are performed by a new version of nonlinear programming algorithm specially adapted for multiobjective problems. This algorithm is based on shifted penalty functions and projected conjugate directions techniques.

An attachment to this paper presents user documentation for a pilot version of a nonlinear model generator with facilities for symbolic differentiation and other means of fundamental model analysis.

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