Rigorous approach to scheduling of sterile drug product manufacturing. (2nd November 2016)

• Record Type: Journal Article
• Authors: Eberle, Lukas; Capón-García, Elisabet; Sugiyama, Hirokazu; Graser, Andreas; Schmidt, Rainer; Hungerbühler, Konrad
• Is Part Of: Computers & chemical engineering. Volume 94(2016)
• Page Start: 221
• Page End: 234
• Publication Date: 2016-11-02
• Subjects: Mathematical optimization -- Mixed-integer linear programming -- Scheduling -- Batch production -- Industrial application -- Campaigning; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2016.07.028
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract : Highlights: Immediate-precedence model of sterile drug product manufacturing. Pharma-specific constraints incorporated, e.g., sterile holding time, and campaigns. Planning horizon segmentation through weekends or night shifts. Validated and applied through full scale industrial case studies. Increase of capacity (i.e., workload) by up to 7.7%, compared to expert schedules. Abstract: Optimizing the scheduling of liquid drug product manufacturing is paramount for pharmaceutical companies in their increasingly competitive environment and requires the modelling of industry-specific constraints. Such constraints include: (i) changing sequence-dependent setup times; (ii) maintaining a sterile production environment (e.g., through sterile holding times); (iii) periods with limited or no plant activity (e.g., no workforce during weekends); and (iv) demand timing (i.e., delivery deadline and release date constraints). In this work, an immediate precedence model is formulated to optimize the scheduling of liquid drug product manufacturing, considering the industry-specific constraints. The primary objective is to minimize the production makespan. Four case studies comprising up to 38 batches from a real multi-product facility illustrate the performance of the rigorous optimization approach. The makespan could be reduced by up to 7.9% compared to expert schedules.