A joint chance-constraint programming approach for a stochastic lot-sizing problem

International audienceWe consider a stochastic lot-sizing problem and study a chance-constraint programming formulation involving joint probabilistic constraints. We propose a solution approach based on a "partial sample approximation". This method does not require the use of any additional binary variables and thus leads to significantly reduced computation time as compared to the previously published "sample approximation" method