A multi-period two stage stochastic programming based decision support system for strategic planning in process industries: a case of an integrated iron and steel company

Abstract

The paper introduces the application of a generic, multiple period, two stage stochastic programming based Decision Support System (DSS) in an integrated steel company. We demonstrate that a generic, user friendly stochastic optimization based DSS can be used for planning in a probabilistic demand situation. We conduct a set of experiments based on the stochastic variability of the demand of finished steel. A two stage stochastic programming with recourse model is implemented in the DSS, and tested with real data from a steel company in North America. This application demonstrates the need for stochastic optimization in the process industry. The value of stochastic solution resulted from the implementation of steel company real data in the DSS is 1.61%.