Algorithms and applications for a class of bilevel MILPs

We study a class of bilevel mixed-integer linear programs with the following restrictions: all upper level variables x are binary, the lower level variables y occur in exactly one upper level constraint gamma x + beta y >= c, and the lower level objective function is min (y)beta y. We propose a new cut generation algorithm to solve this problem class, based on two simplifying assumptions. We then propose a row-and-column generation algorithm that works independently of the assumptions. We apply our methods to two problems: one is related to the optimal placement of measurement devices in an electrical network, and the other is the minimum zero forcing set problem, a variant of the dominating set problem. We exhibit computational results of both methods on the application-oriented instances as well as on randomly generated instances. (C) 2018 Elsevier B.V. All rights reserved.