Abstract
We present a generalized state-space model formulation particularly motivated by an online scheduling perspective, which allowsmodeling (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-manufacturing.
Original language | English (US) |
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Article number | 69 |
Journal | Processes |
Volume | 5 |
Issue number | 4 |
DOIs | |
State | Published - Dec 1 2017 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Bioengineering
- Chemical Engineering (miscellaneous)
- Process Chemistry and Technology
Keywords
- Bio-manufacturing
- Mixed-integer linear programming
- Model predictive control
- State-spacemodel
- Uncertainty