A hybrid programming model for optimal production planning under demand uncertainty in refinery

Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve,the hybrid programming model, along with an error <= 0.5% when the deviation/mean <= 20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor (0.1-0.2) can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.5% and 0.4% enhancement, respectively.