<mods:mods version="3.3" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mods:titleInfo><mods:title>Design and Implementation of Model-based Decision Support Systems</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">M.</mods:namePart><mods:namePart type="family">Makowski</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Decision making often requires the analysis of large amount of data and complex relations. Computerized tools designed and implemented for such purposes are called Decision Support Systems (DSS). A DSS, which is typically a problem specific tool, usually helps in the evaluation of consequences of given decisions and may advise what decision would be the best for achieving a given set of goals. In such cases, an analysis of a mathematical model can support rational decision making. &#13;
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The paper provides an overview of the methodology of the design and deals with practical aspects related to implementations of model-based decision support systems. In particular, different approaches to the analysis of a model using simulation and optimization are summarized. Various optimization techniques are discussed in this context, including multi-criteria optimization used for a model analysis. The paper summarizes also problems of hardware selection and of software development. Modular software tools applicable to DSSs, including a tool for data interchange, are characterized. Selected issues of implementations of modular solvers and of applications of artificial neural nets to decision support are also presented.</mods:abstract><mods:originInfo><mods:dateIssued encoding="iso8601">1994-12</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>WP-94-086</mods:publisher></mods:originInfo><mods:genre>Monograph</mods:genre></mods:mods>