Our preferences are summarized in the matrix below: In this example we judge Rome to be more preferable that Barcelona, much more preferable than NY and we find that SF is as equally less preferable to Rome as Barcelona. In the matrix below A=Barcelona, B=Rome, C=NY, D=SF. Note that number of judgements that are required diminishes with every selection: user starts with 3 unique comparisons for Barcelona, then only 2 for Rome (because the Rome to Barcelona is inverse of Barcelona to Rome) and only one for SF. We perform a pairwise comparison between Barcelona and the rest of spots, then Rome and finally NYC. We want evaluate our vacation spots with respect to sightseeing. Consistency of the selection and the judgements involved is derived from the estimation of the eigenvalue of the decision matrix. The choice is then calculated using linear algebra transformation of the decision matrix. After carrying out pair-wise comparisons on criteria with respect to the goal and then choices with respect to each criterion one obtains a matrix representation of the model (aka the decision matrix). Scale ranges from 1 (equal weight) to 9 (where one option is extremely more preferable than the other). Saaty proposed 9 grade value scale to compare choices in the model. Selection of preferences for both criteria and choices is done via pair-wise comparison. According to AHP to make a rational decision you should build a model as shown on the figure below in which the goal rests on selection criteria which depend on choices. Let me illustrate the AHP with the following example: Imagine you are deciding on a vacation spot out of 4 choices A,B,C,D (for instance SF, NY, Rome and Barcelona) and using three criteria 1,2,3 (for example cost, night life and sightseeing). The AHP was proposed by Thomas Saaty who developed it while at the US Disarment Agency and the Wharton School, UPenn. KEYWORDS Group Decision Support System (GDSS), Multi-Criteria Decision Making (MCDM), Analytic Hierarchy Process (AHP), Tender Evaluation.The Analytic Hierarchy Process is a solution / theory that is part of the Operations Research - a branch of economics that attempts to apply modeling and statistics to decision selection and execution of decisions within the enterprise. A prototype system based on tendering process has been developed to test the GRAHP model in terms of its applicability and robustness. This process is transparent to Decision Makers because the degree of data inconsistency is visible. However, the generated values in the decision matrix tables can still be altered by following the guidelines which in turn serve the purpose of improving the consistency of the decision matrix table. It is a technique where decision matrix tables are automatically filled in based on ranked data. The enhanced AHP method used is the Guided Ranked AHP (GRAHP). In this paper, we propose an enhanced AHP model for GDSS tendering. In order to be effectively used in GDSS, AHP needs to be customized so that it is more user friendly with ease of used features. Analytic Hierarchy Process (AHP) is the multi-criteria decision making (MCDM) that has been applied in GDSS. Abstract : Application of model base in group decision making that makes up a Group Decision Support System (GDSS) is of paramount importance.
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