Tuesday, 16:15 - 16:40 h, Room: MA 144


Marcus Poggi
On a class of stochastic programs with endogenous uncertainty: Algorithm and applications

Coauthor: Bruno Flach


We study a class of stochastic programming problems with endogenous uncertainty - i.e., those in which the probability distribution of the random parameters is decision-dependent - which is formulated as a Mixed Integer Non-Linear Programming (MINLP) problem. The proposed methodology consists of: (i) a convexification technique for polynomials of binary variables; (ii) an efficient cut-generation algorithm; and (iii) the incorporation of importance sampling concepts into the stochastic programming framework so as to allow the solution of large instances of the problem. We discuss the error tolerance of the approach and its impact on the resulting algorithm efficiency. Computational results are obtained in the context of the humanitarian logistics problem, they demonstrate the effectiveness of the proposed methodology by solving instances significantly larger than those reported in related works. Other applications in this class of stochastic problems are presented.


Talk 3 of the contributed session Tue.3.MA 144
"Nonlinear stochastic optimization" [...]
Cluster 22
"Stochastic optimization" [...]


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