The α-Discounting Method (α-D MCDM) Compared with Established MCDM Methods: Comparisons with AHP, ANP, TOPSIS, VIKOR, PROMETHEE, ELECTRE, and Thirty-Five Other Multi-Criteria Decision-Making Methods, with Worked Numerical Examples

This book provides a systematic conceptual and numerical comparison between the α-Discounting Method for Multi-Criteria Decision Making (α-D MCDM) and approximately forty established MCDM techniques. Unlike conventional weighting approaches that require coherent or acceptably consistent judgments, α-Discounting transforms systems of potentially contradictory preference equations through a scalar discounting parameter, α. This parameter measures the degree of inconsistency, while the corresponding normalized solution generates a usable vector of criteria weights. The study positions α-Discounting primarily as a weight-derivation method and examines its relationships with AHP, ANP, entropy weighting, CRITIC, BWM, FUCOM, TOPSIS, VIKOR, PROMETHEE, ELECTRE, and numerous other weighting, outranking, utility-based, optimization, fuzzy, and uncertainty-oriented methods. The first part presents theoretical comparisons focusing on preference elicitation, inconsistency management, computational structure, and practical applicability. The second part applies the methods to a shared decision problem through computer-verified numerical examples, making it possible to identify areas of agreement and divergence among their rankings. Most ranking methods produce the same ordering when supplied with α-Discounting weights, while the exceptions reveal the effects of different compromise principles, distance measures, weighting philosophies, and treatments of individual regret. The volume demonstrates that α-Discounting complements rather than replaces established ranking procedures by providing a flexible mechanism for deriving weights from inconsistent, interval-valued, n-wise, or nonlinear expert judgments.