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Deriving the partial values in MCDM by goal programming
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DOI：10.1023/A:1018930723267.png)
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摘要
En 中文
Preference for a set of alternatives evaluated under multiple criteria is frequently expressed in the form of pairwise comparisons. We propose a linear goal programming model for deriving the partial and overall preference values of the alternatives directly from pairwise comparisons. This model can be a useful alternative to AHP. The partial values represent the contribution of the criteria to the overall preference. Simulation experiments, which are conducted to compare the performance of the model with that of two other existing models; show that the model has good performance.
Keyword:
goal programming
ordinary least squares regression
preference ordering
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