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Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

Jankauskas, K; Farid, SS; (2019) Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach. Computers & Chemical Engineering , 128 pp. 35-52. 10.1016/j.compchemeng.2019.05.023. Green open access

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Abstract

This paper presents a flexible genetic algorithm optimisation approach for multi-objective biopharmaceutical planning problems under uncertainty. The optimisation approach combines a continuous-time heuristic model of a biopharmaceutical manufacturing process, a variable-length multi-objective genetic algorithm, and Graphics Processing Unit (GPU)-accelerated Monte Carlo simulation. The proposed approach accounts for constraints and features such as rolling product sequence-dependent changeovers, multiple intermediate demand due dates, product QC/QA release times, and pressure to meet uncertain product demand on time. An industrially-relevant case study is used to illustrate the functionality of the approach. The case study focused on optimisation of conflicting objectives, production throughput, and product inventory levels, for a multi-product biopharmaceutical facility over a 3-year period with uncertain product demand. The advantages of the multi-objective GA with the embedded Monte Carlo simulation were demonstrated by comparison with a deterministic GA tested with Monte Carlo simulation post-optimisation.

Type: Article
Title: Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.compchemeng.2019.05.023
Publisher version: https://doi.org/10.1016/j.compchemeng.2019.05.023
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: Multi-objective, Uncertainty, Biopharmaceutical, Capacity planning, Scheduling, Genetic algorithm
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10090093
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