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Dynamic scheduling of multi-product continuous biopharmaceutical facilities: A hyper-heuristic framework

Oyebolu, FB; Allmendinger, R; Farid, SS; Branke, J; (2019) Dynamic scheduling of multi-product continuous biopharmaceutical facilities: A hyper-heuristic framework. Computers and Chemical Engineering , 125 pp. 71-88. 10.1016/j.compchemeng.2019.03.002. Green open access

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Abstract

The biopharmaceutical industry is increasingly interested in moving from batch to semi-continuous manufacturing processes. These continuous bioprocesses are more failure-prone and process failure is more consequential. In addition, the probability of failure is dependent on process run time which generally is determined independent of scheduling considerations. This work presents a discrete-event simulation of continuous bioprocesses in a scheduling environment. Dynamic scheduling policies are investigated to make operational decisions in a multi-product manufacturing facility and react to process failure events and uncertain demand. First, different scheduling policies are adapted from the stochastic lot sizing literature and a novel look-ahead scheduling policy is proposed. Then, policy parameters (including process run time) are tuned using evolutionary algorithms. Our results demonstrate that the tuned policies perform much better than a policy that estimates policy parameters based on service level considerations and a policy based on a fixed cyclical sequence.

Type: Article
Title: Dynamic scheduling of multi-product continuous biopharmaceutical facilities: A hyper-heuristic framework
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.compchemeng.2019.03.002
Publisher version: https://doi.org/10.1016/j.compchemeng.2019.03.002
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Stochastic economic lot scheduling problem, Hyper-heuristics, Biopharmaceutical manufacture, Perfusion, Simulation optimisation, Machine failure
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10076496
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