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Bernard Fortz (bfortzulb.ac.be) Abstract: We show how we can linearize probabilistic linear constraints with binary variables when all coefficients are distributed according to either $\mathcal{N}(\mu_i,\lambda \mu_i)$, for some $\lambda >0$ and $\mu_i>0$, or $\Gamma(k_i,\theta)$ for some $\theta >0$ and $k_i>0$. The constraint can also be linearized when the coefficients are independent and identically distributed if they are, besides, either positive or strictly stable random variables. Keywords: probabilistic constraint, combinatorial optimization, continuous distributions Category 1: Stochastic Programming Category 2: Combinatorial Optimization Citation: Download: [PDF] Entry Submitted: 10/22/2009 Modify/Update this entry  
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