An advanced decision support system is presented to answer aggregate planning questions regarding the trade-off between demand (product-mix) and supply (capacity) in a multi period stochastic setting. This tool improves the effectiveness and efficiency of sales and operation planning meetings by accounting for both revenues and costs that are relevant at the intermediate planning horizon. We
develop a multi product, multi routing model, where a routing consists of a sequence of operations on different resources. Given customer demand in each time period, the model obtains the optimal production quantities in every period for each alternative routing, while explicitly taking into account the stochastic nature of both demand patterns and production lead times. This is the key difference between our approach and traditional aggregate planning models. At the same time, an optimal capacity level for each resource is obtained. We include trade-offs between level and chase strategies by charging costs for inventory, work-in-process, backorders, setups, regular time, overtime, etc. Outsourcing is considered as an alternative source with a stochastic lead time. The methodology builds upon a queueing network to estimate product’s lead time distribution and associated quoted lead time with a service level. More system improvements can be obtained by proper lot sizing. This model is a mixed integer non-linear programming problem. We show that the search process of the differential evolution algorithm is efficient to find stable results within acceptable time limits. A scenario analysis reveals interesting managerial insights.