The basic problem of decision making is to choose the best alternative from a set of competing alternatives that are evaluated under conflicting criteria. In general, the process is to evaluate decision elements by quantifying the subjective judgments. The Analytic Hierarchy Process (AHP) provides us with a comprehensive framework for solving such problems. As pointed out by Saaty, AHP "enables us to cope with the intuitive, the rational, and the irrational, all at the same time, when we make multicriteria and multiactor decisions". Furthermore, in most organizations decisions are made collectively, regardless of whether the organization is public or private. It is sometimes difficult to achieve consensus among group members, or for all members of a group to meet. The purpose of this dissertation was two-fold: First, we developed a new aggregation method - Minimum Distance Method (MDM) - to support group decision process and to help the decision makers achieve consensus under the framework of AHP. Second, we evaluated the performance of aggregation methods by using accuracy and group disagreement criteria. The evaluations were performed through simulation and empirical tests. MDM • employs the general distance concept, which is very appealing to the compromise nature of a group decision making. • preserves all of the characteristics of the functional equations approach proposed by Aczel and Saaty. • is based on a goal programming model, which is easy to solve by using a commercial software such as LINDO. • provides the weighted membership capability for participants. • allows for sensitivity analysis to investigate the effect of importance levels of decision makers in the group. The conclusions include the following: • Simulation and empirical tests show that the two most important factors in the aggregation of pairwise comparison judgments are the probability distribution of error terms and the aggregation method. • Selection of the appropriate aggregation method can result in significant improvements in decision quality. • The MDM outperforms the other aggregation methods when the pairwise comparison judgments have large variances. • Some of the prioritization methods, such as EV[AA'], EV[A'A], arithmetic and geometric mean of EV[AA'] and EV[A'A], can be dropped from consideration due to their poor performance
| Identifer | oai:union.ndltd.org:pdx.edu/oai:pdxscholar.library.pdx.edu:open_access_etds-2221 |
| Date | 01 January 1996 |
| Creators | Zhou, Sida |
| Publisher | PDXScholar |
| Source Sets | Portland State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations and Theses |
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