We present a generalized state-space model formulation particularly motivated by an online scheduling perspective, which allows modeling (1) task-delays and unit breakdowns; (2) fractional delays and unit downtimes, when using discrete-time grid; (3) variable batch-sizes; (4) robust scheduling through the use of conservative yield estimates and processing times; (5) feedback on task-yield estimates before the task finishes; (6) task termination during its execution; (7) post-production storage of material in unit; and (8) unit capacity degradation and maintenance. Through these proposed generalizations, we enable a natural way to handle routinely encountered disturbances and a rich set of corresponding counter-decisions. Thereby, greatly simplifying and extending the possible application of mathematical programming based online scheduling solutions to diverse application settings. Finally, we demonstrate the effectiveness of this model on a case study from the field of bio-manufacturin... [more]

A simulation model can accurately capture the details of product recipes in a batch process. By incorporating enhanced capabilities for making key assignment decisions in the simulation executive a model can mimic the experiential knowledge and rules employed in operating a process. As the process complexity and problem size increase using the mathematical programming (MP) techniques to generate schedules becomes increasingly difficult. A simulation run typically takes very little computation time and generates a schedule that is verifiable. Moreover, the model can be used to explore a wide range of parametric space to evaluate alternate policies and the impact of process uncertainties. Although there is no guarantee of optimality, the quality of schedules thus generated is very good and can be deployed in operations. In this paper the decision-making capabilities of the BATCHES simulator are presented with its application to a set of scheduling problems reported extensively in the lit... [more]