Benders decomposition for the large-scale probabilistic set covering problem

Jie Liang, Cheng-Yang Yu, Wei Lv, Wei-Kun Chen, Yu-Hong Dai

Abstract: Highlights•We propose a Benders decomposition algorithm for probabilistic set covering problems.•The number of variables in the reformulation is independent of the scenarios size.•The Benders cuts can be separated by an efficient polynomial-time algorithm.•We present initial cuts, heuristic, and cutting planes to speed up the algorithm.•We demonstrate the efficiency of the proposed algorithm over CPLEX.