AN ACTION LEARNING-EVALUATION PROCEDURE FOR MULTIPLE CRITERIA DECISION-MAKING PROBLEMS

In this paper we focus on an extension of the Analytic Hierarchy Proce ss (AHP) that accommodates ambiguity on the part of the decision maker (DM), and facilitates the exploration of the decision domain. We prop ose a systematic action learning process that builds confidence as it converges from numeric interval estimates to numeric point estimates. Our Multiple Criteria Decision Making (MCDM) problem procedure structu res the problem as a hierarchy, evaluates all objects using pairwise c omparisons that accommodate vagueness and ambiguity, uses interval pri oritization techniques, and does synthesis using the linear additive v alue function. This action learning process facilitates the understand ing of key stakeholders, which is imperative for the successful implem entation of the subsequent decision.