Abstract
We propose a continuous-time scheduling model for the logistic and blending operations of copper concentrates with uncertain composition. The formulation, based on the Multi-Operation Sequencing (MOS) model, gives rise to a large-scale nonconvex mixed-integer nonlinear programming (MINLP) model. We adopt a two-step MILP-NLP decomposition strategy and enhance the MILP relaxation to propose schedules that significantly reduce the optimality gaps. The bounded uncertainty in element composition of the concentrates is addressed by an extended robust MOS model, which combines robust optimization and flexibility analysis techniques. The effectiveness of the models and the solution strategy is validated with an illustrative example and an industrial case study.
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