On Maximizing Discrimination in Multiple Criteria Decision Making

Abstract

We examine an LP/DEA-based technique for establishing an overall ranking of alternatives that are ranked on multiple criteria, which themselves are ranked. This two-stage process involves one LP in the first stage, and N LPs in the second stage to rank N alternatives. We find that the information from N + 1 LPs can be obtained by solving two LPs. In many cases, the solution of one LP, which can be done by inspection, is almost as informative as the two-stage procedure. We also indicate when the second stage would be redundant. If maximum technical discrimination between the alternatives is sought, we consider how this might be achieved by aggressive cross-evaluation via N LPs. We also show how to identify a subset of the alternatives that would be ranked in the first place under any ordering of the criteria, and thus play an important role in the evaluation procedure.