A stochastic optimization approach is presented to manage risk in the short-term scheduling of multiproduct batch plants with uncertain demand. The problem is modeled using a two-stage stochastic optimization approach accounting for the maximization of the expected profit. The model is also extended to incorporate the availability of option contracts, thus providing significant flexibility within the uncertain environment. Management of risk is explicitly addressed by adding a control measure as a new objective to be considered, thus leading to multiobjective optimization formulations. Three alternative methodologies are assessed and compared. The importance of considering the uncertainty not only in the decision-making process but also in the control of the variability of outcomes is highlighted. Parametric solutions appealing to decision makers with different attitudes toward risk are obtained.

The uncertainty present in any process environment and related not only to variable market demand but also to operational disturbances is usually unavoidable, and therefore, poor performance may be attained with the execution of deterministic optimal schedules. In this work, the short-term scheduling problem in chemical batch processes with variable processing times is addressed with the aim to identify robust schedules able to face the major effects driving the operation of batch processes with uncertain times, i.e., idle and wait times. The problem is modeled using a two-stage stochastic approach accounting for the minimization of a weighted combination of the expected makespan and the expected wait times. The formulation is extended to explicitly manage risk by optimizing three different robustness criteria. The application of the proposed formulation to academic and industrially based examples shows the efficiency of the proposed approach and highlights the importance of considering the uncertainty in the short-term scheduling level.

No abstract

No abstract