Optimization for Production Planning using Probabilistic Simple Temporal Networks
A.G. Kalandadze (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Mathijs de Weerdt – Mentor (TU Delft - Algorithmics)
K.C. van den Houten – Mentor (TU Delft - Algorithmics)
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Abstract
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.