Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process

Haddad, Malik, Sanders, David, Bausch, Nils, Tewkesbury, Giles, Gegov, Alexander and Hassan, Mohamed (2019) Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process. In: Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1.

Abstract

This paper presents an expert system to select a most suitable discrete Multi-Criteria Decision Making (MCDM) method using an approach that analyses problem characteristics, MCDM methods characteristics, risk and uncertainty in inputs and applies sensitivity analysis to the inputs for a decisional problem. Outcomes of this approach can provide decision makers with a suggested candidate method that delivers a robust outcome. Numerical examples are presented where two MCDM methods are compared and one is recommended by calculating the minimum percentage change in criteria weights and performance measures required to alter the ranking of any two alternatives. A MCDM method will be recommended based on a best compromise in minimum percentage change required in inputs to alter the ranking of alternatives.

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