A methodology for augmenting sparse pairwise comparison matrices in AHP: applications to energy systems

Multiple-attribute decision making (MADM) techniques can be used to provide a systematic approach to selection problems in energy engineering and management. They may be used for selecting the best technologies or policies based on environmental, technical, and socio-economic criteria. Among the many available MADM techniques, the analytic hierarchy process (AHP) has become one of the most widely used due to its effective hierarchical decomposition of complex problems. However, AHP may be tedious due to the large number of pairwise comparisons needed in large problems. Furthermore, in many cases, relevant information may also be available for determining criteria weights based on past decisions that have proven satisfactory in retrospect. Thus, we propose a simple methodology for augmenting sparse pairwise comparisons in AHP through a non-linear programming model that extracts a set of consistent weights from a priori ranking of a subset of alternatives. Two case studies on the ranking of bioethanol feedstocks and of CO₂ storage sites are then shown to illustrate this technique.