E. Moreno, I. Ljubic
Two-stage stochastic programs (TSSP ) are a classic model where a decision must be made before the realization of a random event, allowing recourse actions to be performed after observing the random values. Benders decomposition is one of the most applied methods to solve TSSP with a large number of scenarios. In this paper we present a novel Benders adaptive-cuts method, where the Benders cuts are aggregated according to a partition of the scenarios, which is dynamically refined using the LP-dual information of the subproblems. This scenario aggregation/disaggregation is based on the Generalized Adaptive Partitioning Method (GAPM). Our new method can be interpreted as a compromise between the Benders single-cuts and multi-cuts methods, drawing on the advantages of both sides, by rendering the initial iterations faster (as for the single-cuts Benders) and ensuring the overall faster convergence (as for the multi-cuts Benders).
Keywords: Stochastic Programming, Adaptive-Partition, Network Flows, Algorithms
Scheduled
Methods and Applications of Operations Research
November 10, 2023 4:00 PM
CC4: Room 2