A parallel approach to the analytic hierarchy process decision support tool

Abstract

The Multiple Criteria Decision Aiding methods dedicated to discrete problems follow different philosophies and strategies for selecting, clustering or ranking alternatives. This work presents a tool using one such method—the Analytic Hierarchy Process (AHP). The Decision Maker (DM) can structure his criteria as a hierarchy tree having the alternatives as leaf nodes. The DM must then build matrices for each node by performing pairwise comparisons between its children. The AHP finds the weights of each child concerning the parent criterion by calculating the elements of the eigenvector corresponding to the maximum eigenvalue of the comparison matrix. Weights are then combined in order to obtain the influence of each alternative on the top of the hierarchy. A DM expects that a Decision Support Tool works faster than he/she does. In order to achieve speed a parallel approach was developed. Parallel implementations described in this work follow different message-passing strategies and capitalise on the fact that the vector of weights for each matrix can be calculated independently. The authors used a network of four Inmos Transputers. Research will focus on finding which implementation will run faster and how the DMs options affect the speedups obtainable.