This paper presents a flexible genetic algorithm optimisation approach for multiobjective 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 sequencedependent 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 multiobjective GA with the embedded Monte Carlo simulation were demonstrated by comparison with a deterministic GA tested with Monte Carlo simulation postoptimisation.
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