(749a) A Generalized State-Space Model for Online Scheduling
We have developed a general state-space model, particularly motivated by an online scheduling perspective, that allows modeling (1) task-delays and unit breakdowns with a new, more intuitive convention over that of Subramanian et al., 2012, (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. Further, we propose new methods for updating the state of the process, as well as methods to enforce additional constraints, based on feedback information, on future decisions. We demonstrate the effectiveness of this model on a case study from the field of bio-manufacturing.
Through this new state-space model, we have enabled a natural way to handle routinely encountered processing features and disturbance information in online scheduling, in general. The proposed model, therefore, greatly extends and enables the possible application of mathematical programming based online scheduling solutions to diverse application settings. Finally, it is important to note, that although our model uses state task network based representation, these generalizations can also be adapted to resource task network based representation (Pantelides, 1994).
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Martagan, T.; Krishnamurthy, A.; Leland, P.; Maravelias, C.T. Performance Guarantees and Optimal Purification Decisions for Engineered Proteins. Operations Research, 6 (1), (2018), 18-41.
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