Application of linear programming to derive the local weight in the analytic hierarchy process

The analytic hierarchy process (AHP) has become more developed in both the areas of theory and practice. The important topic here, is how to drive the local weight vector from a pairwise reciprocal matrix. In the literature, there are several methods used to accomplish this. Most recently, Hosseinian et al. (2009) suggested the LP‐GW‐AHP because it obviously provides better weights. In this article, the LP‐GW‐AHP method is applied to multi‐level decision problems, and the weights were compared with Saaty's eigenvector. According to our findings, of the LP‐GW‐AHP method, Saaty's eigenvector method differs slightly from that derived in the local weight values.