Production planning in the biomanufacturing sector presents significant challenges due to uncertainties in job durations caused by biological variability, environmental conditions, and raw material quality. Traditional scheduling methods typically fail to adapt to these uncertainties, leading to suboptimal outcomes. This research addresses this issue at DSM-Firmenich, focusing on optimizing production planning while maximizing profit, adhering to deadlines, and efficiently utilizing resources. We propose an integrated approach using Mixed-Integer Linear Programming (MILP) and Constraint Programming (CP) models, alongside Probabilistic Simple Temporal Networks (PSTNs) to handle uncertainty in real-time scheduling. The study introduces an offline optimization procedure for proactive scheduling decisions and a reactive real-time algorithm for adjustments of the planned schedule. This work showcases the potential of applying PSTNs in biomanufacturing and sets the stage for future research aimed at enhancing real-time execution strategies in factory environments.

Learning curves display predictions of the chosen model’s performance for different training set sizes. They can help estimate the amount of data required to achieve a minimal error rate, thus aiding in reducing the cost of data collection. However, our understanding and knowledge of the various shapes of learning curves and their applicability are still insufficient. Despite the presence of a curve that demonstrates a high level of accuracy on average, this parametric model can still exhibit inadequate performance in certain scenarios. Therefore, the objective of this research is to identify specific patterns in the datasets that influence the selection of a particular parametric curve model. To accomplish this, I conduct experiments to assess the performance of different parametric learning curves including power, exponential and Morgan-Mercer-Flodin (mmf) based on the number of features, classes, outliers, and machine learning models. I find that mmf and exponential curves outperform power law for all machine learning models. All curves work best with Logistic Regression, Bernoulli Naive Bayers and Multinomial Naive Bayers models. Exponential and mmf curves provide better results than power law for a small number of classes. Mmf also outperforms power law for the majority of numbers of features and outlier percentages.