Efficient Branch and Bound Methods for Pairing Selection

During the past few decades, a number of methods for selection of input-output pairings for decentralized control have been proposed. Most of these available methods require evaluation of every alternative in order to find the optimal pairings. As the number of alternatives grows rapidly with problem size, pairing selection through an exhaustive search can be computationally forbidding for large-scale process. In this paper, we present novel branch and bound (BAB) approaches for pairing selection using relative gain array and μ-interaction measure as the selection criteria to overcome this difficulty. We demonstrate the computational efficiency of the proposed BAB approaches by applying them to randomly generated matrices as well as to the Tennessee Eastman benchmark example.