Constraint programming for scheduling a fermentation plant

Abstract

This study examines the scheduling challenges faced by a real-world biomanufacturing site. This fermentation factory is a multi-product, multi-stage facility where batches are scheduled to meet customer demand. Importantly, in the production process of these batches, they are split and mixed into subbatches. Between stages, the intermediate product’s expiration should be avoided by maintaining a continuous flow without waiting in the factory, resulting in zero-wait policies. Sequence-dependent cleaning operations are also required between processing steps to avoid cross-contamination. Recent advances in constraint programming (CP) have demonstrated strong performance on standard scheduling problems. In this work, we investigate how effectively the biomanufacturing scheduling problems can be modeled with CP. Then, we investigate the performance of state-of-the-art CP solvers for solving real, large-scale biomanufacturing scheduling instances. Our empirical evaluation shows that CP Optimizer is more effective than Google OR Tools CP SAT. We show that a warm-start strategy based on domain knowledge improves the performance of the CP approach. Our empirical results show that relaxing the zero-wait constraints results in lower optimality gaps. Finally, we make our set of problem instances and CP model publicly available, thereby extending the scheduling literature with large-scale, realistic benchmarking instances obtained by our industrial collaboration.