Proximity Benders: a decomposition heuristic for stochastic programs

is eventually found and the method terminates. As convergence may require a large amount of computing time for hard instances, the method unsatisfactory from a heuristic point of view. Proximity Search is a recently-proposed heuristic paradigm in which the problem at hand is modified and iteratively solved with the aim of producing a sequence of improving feasible solutions. As such, Proximity Search and Benders decomposition naturally complement each other, in particular when the emphasis is on seeking high-quality, but not necessarily optimal, solutions. In this paper, we investigate the use of Proximity Search as a tactical tool to drive Benders decomposition, and computationally evaluate its performance as a heuristic on instances of different stochastic programming problems.