A new methodology for sensitivity and stability analysis of analytic network models

In this paper we develop a methodology to study the sensitivity and the stability of models built using the Analytic Network Process. We study two types of stability: core and solution stability. The former deals with finding the region of the perturbation space in which the initial solution (i.e., the alternative that is ranked first) obtained from the ANP model remains most preferred. The latter deals with finding the regions of the perturbation space in which the solutions that were not initially most preferred (i.e., alternatives that were not ranked first) become most preferred (i.e., they are ranked first). The methodology consists of three stages: generation of the perturbation space, finding the boundaries of the regions in the perturbation space in which the different alternatives are ranked first, and finding the stability regions.

► We study the sensitivity and stability of models based on the Analytic Network Process (ANP). ► Our methodology combines numerical perturbations of the ANP model with optimization modeling. ► We introduce three types of stability: core, solution and perturbation stability. ► Core and solution stability are studied using the concept of Euclidean center.