A heuristic method to rank the alternatives in the AHP synthesis

This paper proposes a heuristic method (Bayesian cosine maximization method (BCCM)) to rank the alternatives in the Analytic Hierarchy Process (AHP) synthesis, based on the multiplicative AHP model, which focuses on the revision of the pair-wise comparison matrices (PCMs) and derivation of the priority vectors from the PCMs in whole hierarchy, considering both the consistency of the PCMs and total consistency. An Eight-step algorithm for the AHP synthesis is developed to how to revise the PCMs in the uncertainty context and generate the final priority vector of the alternatives, which obtains more accurate estimates of the priority vectors and provides a global AHP framework based on the multiplicative AHP model. Finally, two numerical examples and corresponding comparison with several other methods are implemented to illustrate the application and efficiency of the proposed BCCM. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This paper proposes a heuristic method (BCCM) to rank alternatives in AHP synthesis, with an eight-step algorithm developed to guide the revision of PCMs and generation of final priority vectors, providing a more accurate estimation and a global framework based on the multiplicative AHP model.