Refining is a high temperature, high energy expenditure and large scale logistics machining process in the steel-making and continuous casting (SCC) production. The refining process is mainly characterized by the time-critical and component-critical design. However, the designed activities are often interlinked and quite uncertain; some of the stages may have to be iterated several times to meet the design criteria if the components of the molten steel do not meet the designed criteria for the casting process and the do not meet the established target dates. So, how to generate a robust and optimal schedule to handle the uncertainties for the refining process in a limited time by a computationally efficient manner is becoming critical for the production of iron and steel. After the seperable problem formulation is created, The stochastic dynamic programming method is adopt to the obtained subproblems which are relaxed by Lagrangian relaxation multipliers, the actual schedule is dynamically constructed based on the dual solution. The method has been tested by using practical data from the Shanghai Bashan steel plant in China and could get near optimal solutions in a limited time; the uncertain number of iterations is effectively handled for the production.
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