Janusz Granat

Interactive specification and analysis of aspiration-based preferences

Model based Decision Support Systems (DSS) often use multi-criteria optimization for selecting Pareto-optimal solutions. Such a selection is based on interactive specification of user preferences. This can be done by specification of aspiration and reservation levels for criteria. Diverse graphical user interface could be used for the specification of these levels as well as for the interpretation of results. In the approach presented in this paper the specified aspiration and reservation levels are used for generation of component achievement functions for corresponding criteria, which reflect the degree of satisfaction with given values of criteria. The paper outlines the methodological background and modular structure of a tool (called NCMA) for multi-criteria analysis of decision problems that can be represented as linear programming (LP) or mixed integer programming (MIP) problems. The MCMA has been used at IIASA for the analysis of decision problems in water quality management and land use for sustainable development planning. These experiences have shown that the MCMA tool is applicable also to a large LP and MIP problems. Other implementations of the same methodology have also been used for the analysis of non-linear problems in several engineering applications. specification of user preferences is described. The presented methodology of multi-criteria model analysis and the documented software is illustrated by a detailed tutorial example.

The interactive analysis of the multicriteria shortest path problem by the reference point method

Abstract The multicriteria shortest path problem is considered. The paper presents the interactive method of analyzing this problem by the reference point approach. The reference point guided labeling algorithm was developed. This algorithm finds the Pareto-optimal shortest path which is best attuned to the specified preferences.

IAC-DIDAS-N A Dynamic Interactive Decision Analysis and Support System for Multicriteria Analysis of Nonlinear Models

The decision analysis and support systems of DIDAS family—that is, Dynamic Interactive Decision Analysis and Support systems —are specially designed to support interactive work with a substantive model of a decision situation while using multicriteria optimization tools. They stress the learning aspects of such work, such as the right of a decision maker to change his priorities and preferences when learning new facts. DIDAS systems can be used either by analysts who want to analyse their substantive models, or by teams of analysts and decision makers, or even by decision makers working alone with a previously defined substantive model; in any case, we shall speak further about the user of the system.

Multicriteria analysis in telecommunications

Recently there has been observed a global trend towards increasing the range of tourism products and services as well as ways of their distribution. This steps forth the issue of developing an information system that would provide advisory support to tourists in adopting the decision on an individual travel. The theoretical and experimental research is performed on applying the methods of multi-criteria analysis for solving the problem of choosing an individual travel. Experimental research of the sensitivity of the solution conducted allows analyzing influence of the choice criteria on the result and allows formulating an insensitive solution. Based on the results of the research it is possible to make a motivated choice of a travel according to the tourists preferences.The paper presents an overview of multicriteria analysis techniques applied to the design of telecommunication networks. A special module called ISAAP for interactive multicriteria analysis is adapted for this purpose. We will also present the use of multicriteria analysis for supporting strategic decisions related to negotiating interconnection agreements in telecommunications.

Multi-objective modeling for engineering applications: DIDASN++ system

Abstract Modeling and simulation of various physical, technical, environmental or socioeconomic processes is often a preliminary step for using the resulting models in computer-aided design or decision support. In engineering computer-aided design, the decisions of the designer might be supported by multicriteria optimization - which in this case should not be considered as a tool for supporting the final choice of the design, but much more as a tool for helping in a flexible analysis of various design options or even various modeling and simulation options. The paper shows how multicriteria optimization techniques can be used for multi-objective analysis of a model from the beginning stages of model construction. With the advancement of computing technology and the methodology of decision support, it is now possible to revise this way basic approaches to modeling and simulation. Various formats of defining nonlinear and time-discrete models are discussed together with related problems of inverse and softly constrained multi-objective simulation. Algebraic differentiation and sensitivity analysis, fuzzy set representation of modeler preferences are also useful techniques of multi-objective modeling. Such techniques are illustrated by engineering applications of a software package DIDASN++ in mechanics and automatic control.

Multi-Objective Modeling for Engineering Applications in Decision Support

In engineering computer-aided design, the final choice of the design might be supported by multicriteria optimization; we show here, however, that multicriteria optimization can be also used as a tool of helping in a flexible analysis of various design options or various modeling and simulation variants, even from the beginning stages of model construction. Various formats of defining linear and nonlinear models are discussed together with related problems of inverse and softly constrained multi-objective simulation. Such techniques are illustrated by engineering applications of a software package DIDASN++ in mechanics, automatic control and ship navigation.

Modeling for Decision Support in Network-Based Services

In this introduction to the book, we discuss our current motivation for modeling and optimization applications involving network-based services. Our conceptual model of service science management and engineering (SSME) as the basis for better understanding network-based services embraces the confluence of network science, computational and analytical modeling, knowledge science and decision support. We segment the contributed articles into these four categories and provide a capsule summary of each paper.

Parametric Programming Approaches to Local Approximation of the Efficient Frontier

In this paper the set of the efficient solutions in criteria space is characterized locally by directional derivatives, which can be treated as a local linear approximation of the efficient frontier. The properties of the marginal function in nonlinear programming are applied. The results are used for building a prototype of a graphic interface for a decision support system.

Hermeneutic Cognitive Process and Its Computerized Support

The paper discusses the role of tacit knowledge (including emotive and intuitive knowledge) in cognitive processes, recalls an evolutionary theory of intuition, academic and organizational knowledge creation processes, describes diverse cognitive processes in a research group, but especially concentrates on individual hermeneutic processes and processes of knowledge exchange while distinguishing their hermeneutic and semantic role. The functionality and main features of the PrOnto system are shortly recalled, and the relation of changes in individualized ontological profile of the user to her/his hermeneutical perspective is postulated. An interpretation of such changes for both hermeneutic and semantic aspects of knowledge exchange is presented. Conclusions and future research problems are outlined.

A modular system of software tools for multicriteria model analysis

Applications of various analytical models in decision support systems motivate the development of efficient tools for the analysis of such models and their optimization. There are various approaches to the design of DSS based on analytical models. We focus here on developing well defined modular tools supporting selected phases of decision process, which can be used to build a DSS customized to a problem being solved.