# The Analytic Hierarchy Process And The Multi Attribute Utility Theory Essay

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ntroduction
Usually several alternative possibilities will be encountered in daily like. The selection (or called decision making) has become a kind of psychology. By the decision making process we will get a final choice. There are a lot different decision-making methods published and widely used, such as weighted-point method, matrix approach, vendor performance matrix approach, vendor profile analysis (VPA), analytic hierarchy process (AHP), and multiple objective programming (MOP) Here followed is a short introduction of two different decision-making methods: the Analytic Hierarchy Process and the Multi-Attribute Utility theory.

When the decision-maker facing a complex problem with multiple conflicting and subjective criteria (for example location or investment selection, projects ranking and so forth), the decision-making method based on multi-criteria is so called the Analytic Hierarchy Process (AHP). AHP is originally proposed by Saaty in 1980[1]. It is based on four steps: problem modeling, weights valuation, weights aggregation and sensitivity analysis. To analysis the problem for the decision making process, it can be divided into three parts: goal, criteria and alternatives. First of all, AHP shows an advantage that it permits a hierarchical structure of the criteria, which provides users with a better focus on specific criteria and sub-criteria when allocating the weights. [1] Secondly, The AHP method uses a ratio scale, so it doesn’t require units in the comparison, and the decision make doesn’t need to make a numerical judgment, which is better than interval scales.

The Wikipedia explains the Multi-Attribute Utility Theory (MAUT) that it is used to represent the preferences of an agent over bundles of goods under ...

... middle of paper ...

...ocation to make a final decision on which projects to initiate. [8]

Figure 1 shows the AHP decision tree for selecting the best new product development project. According to the figure, within all criteria, there are few sub-criteria but they are all limited. The overall estimation of these factors provides a final answer for the new project.

Figure1 The AHP decision tree for selecting the best new product development project (NPD). [8]

Conclusion
In conclusion, both AHP and MAUT are important decision-making methods and they provide a more efficient way for the businessmen or social workers to get a final decision. These scientific methods should be applied based on their advantages and disadvantages in a whole view. There is no such stable conclusion that which one is better. The MAUT is more suitable for bundles of goods under a large number of uncertainties.

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