A Synoptic Taxonomy of Discrete Multi-Criteria Decision Analysis Methods for Ranking

The application of MCDA (Multi-Criteria Decision Analysis) methods spans a wide variety of modern decision situations. These are associated with conflicting DM (Decision-Maker) opinions and values, differing criteria importance, and contrasting performance of the solution alternatives. The DMs who lack thorough academic knowledge about MCDA are unable to appreciate the breadth of methods available and choose the most suitable one for the DP (Decision Problem) they are facing. This paper presents a new taxonomy of discrete MCDA methods for ranking titled ProBCA (Problem-Based Characterisation Approach). It allows a broad spectrum of DMs to efficiently select the most appropriate method for their DP at hand. In contrast to other MCDA taxonomies, ProBCA is based on the problem-centric philosophy and is presented in an easily comprehensible format, which makes it applicable for the widest spectrum of potential users and tasks. ProBCA features a collection of 300 methods dedicated to 3 purposes: formulate the solution alternatives, define relevant criteria, and aggregate the decision information to rank the discrete alternatives. ProBCA is applied by filtering for appropriate problem descriptors grouped into 17 pre-defined parameters to reveal the most suitable methods for ordering options by merit. ProBCA seeks to popularise the use of MCDA methods across the various industries and operational levels by guiding the user through problem characterisation