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Book Contents

Ch. 9
Building Model-Driven Decision Support Systems

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Analytical Hierarchy Process (AHP)

The Analytical Hierarchy Process technique (cf., Saaty, 1980; Saaty, 1990) can be characterized as a multi-criteria decision technique that can combine qualitative and quantitative factors in the overall evaluation of alternatives. This section provides a brief introduction to AHP with an emphasis on the general methodology.

The first step is to develop a hierarchical representation of a problem (see Figure 9.2). At the top of the hierarchy is the overall objective and the decision alternatives are at the bottom. Between the top and bottom levels are the relevant attributes of the decision problem, such as selection criteria. The number of levels in the hierarchy depends on the complexity of the problem and the decision-makerís model of the problem hierarchy.

Next, in step 2 one needs to generate relational data for comparing the alternatives. This requires a decision-maker to make pairwise comparisons of elements at each level relative to the next higher level in the hierarchy. In the Analytical Hierarchy Process a relational scale of real numbers from 1 to 9 is used to assign preferences.

Figure 9.2 A Hierarchical Representation

Using the comparisons of Step 2 the relative priority of each attribute is determined in Step 3. In addition, a "consistency ratio" should be calculated. A user of a DSS based on the AHP model like Expert Choice (http://www.expertchoice.com) has the option of redoing the comparison matrix.

In Step 4, the priorities or weights of the lowest level alternatives relative to the top-most objective are determined and displayed. AHP facilitates a comprehensive and logical analysis of problems for which considerable uncertainty exists.

A number of software packages implement AHP. The best known and most widely used is Expert Choice (visit URL http://www.expertchoice.com). HIPRE 3+ is known for its user friendliness and it is based on a fully graphic interface. It is the first fully graphical mouse-driven implementation of AHP and value tree analysis. HIPRE 3+ lets you combine different approaches such as AHP and value functions in one model. Check URL http://www.hut.fi.

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