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Jeudi 28 Novembre
| Heure: |
10:30 - 11:30 |
| Lieu: |
Salle C314 |
| Résumé: |
Benders Adaptive-Cuts Method for Two-Stage Stochastic Programs |
| Description: |
Eduardo Moreno 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. For example, many classic optimization problems, like network flows or facility location problems, became TSSP if we consider, for example, a random demand.
Benders decomposition is one of the most applied methods to solve TSSP with a large number of scenarios. The main idea behind the Benders decomposition is to solve a large problem by replacing the values of the second-stage subproblems with individual variables, and progressively forcing those variables to reach the optimal value of the subproblems, dynamically inserting additional valid constraints, known as Benders cuts. Most traditional implementations add a cut for each scenario (multi-cut) or a single-cut that includes all 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). We formalize this hybridization of Benders decomposition and the GAPM, by providing sufficient conditions under which an optimal solution of the deterministic equivalent can be obtained in a finite number of iterations. 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).
Computational experiments on three TSSPs validate these statements, showing that the new method outperforms the other implementations of Benders method, as well as other standard methods for solving TSSPs, in particular when the number of scenarios is very large.
Joint work with Ivana Ljubic (ESSEC Business School). |
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