Novel Optimization Approach of Stochastic Planning Models

The problem of planning under uncertainty is addressed. Short term production planning with a time horizon of a few weeks or months and long-range planning including capacity expansion options are considered. Based on the postulation of general probability distribution functions describing process uncertainty, a two-stage stochastic programming formulation is developed where the objective is to determine an optimal plan (i.e., process utilization levels, purchases and sales of materials) and/or an optimal capacity expansion policy that maximize an expected profit. A decomposition-based optimization approach is proposed, where planning decisions are taken by coupling economic optimality and plan feasibility without requiring an a priori discretization of the uncertainty. The proposed algorithmic procedure features a highly parallel solution structure which can be exploited for computational efficiency. Three example problems are presented to illustrate the steps of the novel planning under uncertainty optimization algorithm