Rolling-Horizon Simulation Optimization For A Multi-Objective Biomanufacturing Scheduling Problem

Conference Paper (2023)
Author(s)

K.C. van den Houten (TU Delft - Algorithmics)

Mathijs M. De Weerdt (TU Delft - Algorithmics)

David M.J. Tax (TU Delft - Pattern Recognition and Bioinformatics)

Esteban Freydell (DSM)

Eva Christoupoulou (Systems Navigator)

A Nati (Systems Navigator)

Research Group
Algorithmics
Copyright
© 2023 K.C. van den Houten, M.M. de Weerdt, D.M.J. Tax, Esteban Freydell, Eva Christoupoulou, Alessandro Nati
DOI related publication
https://doi.org/10.1109/WSC60868.2023.10408070
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 K.C. van den Houten, M.M. de Weerdt, D.M.J. Tax, Esteban Freydell, Eva Christoupoulou, Alessandro Nati
Research Group
Algorithmics
Pages (from-to)
1912-1923
ISBN (print)
979-8-3503-6967-0
ISBN (electronic)
979-8-3503-6966-3
Reuse Rights

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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.

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