Heuristic aggregation of individual judgments in AHP group decision making using simulated annealing algorithm

• SA algorithm is used as aggregation procedure in AHP group decision making.
• The group priority vector is obtained by minimization of the Euclidean distance.
• Objective search for maximum consensus between individuals within the group.
• Proposed procedure is invariant to any prioritization method.
• Proposed procedure performs better or at least equally to several other procedures.

There are various aggregation procedures for obtaining a group priority vector within Analytic Hierarchy Process (AHP) supported decision making processes. This paper will introduce a heuristic aggregation procedure based on simulated annealing (SA) algorithm to be used for the purposes of obtaining a group priority vector at any node of an AHP hierarchy. The proposed procedure performs its aggregation process by minimizing the group Euclidean distance (GED) (consensus measure) across group weights and judgments, and the group vector obtained in this way is invariant to any prioritization method. In other words, there is no need to have individual priority vectors as is required by some other aggregation procedures. Along with SA minimization of the GED, the group rank reversal (minimum violation) criterion is implemented as a control mechanism, as well as the consensus measure based on the ranks of alternatives. The presented procedure is compared with several reported combinations of different prioritization methods and group aggregation procedures. Five examples from literature are used to show that the proposed procedure performs better or at least equally to several other well known combinations of prioritization and aggregation in AHP group decision making frameworks.