Orateur
Jean Pauphilet
(London Business School)
Description
Benders Decomposition (BD) is a well-known optimization technique for large-scale two-stage mixed-integer problems by decomposing a problem into a pure integer master problem and a continuous separation problem. To accelerate BD, we propose Random Partial Benders Decomposition (RPBD), a decomposition method that randomly retains a subset of the continuous second-stages variables within the master problem. Unlike existing problem-specific partial decomposition approaches, RPBD is universally applicable and simple to implement. We present both computational evidence and theoretical analysis to demonstrate the efficiency and robustness of RPBD.
Authors
Jean Pauphilet
(London Business School)
Yupeng Wu
(London Business School)
