Rolling-Horizon Simulation Optimization For A Multi-Objective Biomanufacturing Scheduling Problem
Kim Van Den Houten (TU Delft - Algorithmics)
Mathijs De Weerdt (TU Delft - Algorithmics)
David M.J. Tax (TU Delft - Pattern Recognition and Bioinformatics)
Esteban Freydell (DSM)
Eva Christoupoulou (Systems Navigator)
Alessandro Nati (Systems Navigator)
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Abstract
We study a highly complex scheduling problem that requires the generation and optimization of production schedules for a multi-product biomanufacturing system with continuous and batch processes. There are two main objectives here; makespan and lateness, which are combined into a cost function that is a weighted sum. An additional complexity comes from long horizons considered (up to a full year), yielding problem instances with more than 200 jobs, each consisting of multiple tasks that must be executed in the factory. We investigate whether a rolling-horizon principle is more efficient than a global strategy. We evaluate how cost function weights for makespan and lateness should be set in a rolling-horizon approach where deadlines are used for subproblem definition. We show that the rolling-horizon strategy outperforms a global search, evaluated on problem instances of a real biomanufacturing system, and we show that this result generalizes to problem instances of a synthetic factory.