Optimal planning and campaign scheduling of biopharmaceutical processes using a continuous-time formulation. (4th August 2016)

• Record Type: Journal Article
• Authors: Vieira, Miguel; Pinto-Varela, Tânia; Moniz, Samuel; Barbosa-Póvoa, Ana P.; Papageorgiou, Lazaros G.
• Is Part Of: Computers & chemical engineering. Volume 91(2016)
• Page Start: 422
• Page End: 444
• Publication Date: 2016-08-04
• Subjects: Biopharmaceutical plants -- Planning and campaign scheduling -- Optimisation -- Mixed integer linear programming; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2016.04.009
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A novel formulation to address the planning and campaign scheduling constrains in biopharmaceuticals' production is proposed. Continuous/batch process steps, multiple intermediate deliveries, shelf-life restraints and lots traceability are considered. The model performance is verified with a set of industrial-based examples and promising results are obtained. The model has proven to effectively solve campaign planning/scheduling problems sensitive to changes in the tasks duration. Abstract: This work addresses the optimal planning and campaign scheduling of biopharmaceutical manufacturing processes, considering multiple operational characteristics, such as the campaign schedule of batch and/or continuous process steps, multiple intermediate deliveries, sequence dependent changeovers operations, product storage restricted to shelf-life limitations, and the track-control of the production/campaign lots due to regulatory policies. A new mixed integer linear programing (MILP) model, based on a Resource Task Network (RTN) continuous time single-grid formulation, is developed to comprise the integration of all these features. The performance of the model features is discussed with the resolution of a set of industrial problems with different data sets and process layouts, demonstrating the wide application of the proposed formulation. It is also performed a comparison with a related literature model, showing the advantages of the continuous-time approach and the generality of our model for the optimal production management of biopharmaceutical processes.