Abstract In this paper, we introduce robust optimization and stochastic programming strategies for addressing demand uncertainty in steelmaking continuous casting operations. Robust optimization framework was first employed to develop a deterministic robust counterpart optimization model and to guarantee that the production schedule be feasible for the varying demands. Then, a two-stage scenario based stochastic programming framework was studied for the scheduling of steelmaking and continuous operations under demand uncertainty. To make the resulting stochastic programming problem computationally tractable, a scenario reduction method has been applied to reduce the number of scenarios to a small set of representative realizations. Results from both the robust optimization and stochastic programming methods demonstrate robustness under demand uncertainty and the robust solution is slightly better than the stochastic solution.
|Name||Computer Aided Chemical Engineering|
- continuous casting
- robust optimization
- two-stage stochastic programming