An action learning evaluation procedure for multiple criteria decision making problems |
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Authors: | Noel Bryson Ayodele Mobolurin |
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Institution: | School of Business, Howard University, Washington, DC 20 059, USA |
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Abstract: | In this paper we focus on an extension of the Analytic Hierarchy Process (AHP) that accommodates ambiguity on the part of the decision maker (DM), and facilitates the exploration of the decision domain. We propose 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 structures the problem as a hierarchy, evaluates all objects using pairwise comparisons that accommodate vagueness and ambiguity, uses interval prioritization techniques, and does synthesis using the linear additive value function. This action learning process facilitates the understanding of key stakeholders, which is imperative for the successful implementation of the subsequent decision. |
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Keywords: | Hierarchical decomposition Multiple criteria decision making AHP Vagueness Vague real numbers Action learning |
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