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• W2891353136 abstract "• Fast GA approaches for capacity planning and scheduling of multi-product, multi-site biomanufacture are presented. • The key features include the chromosome encoding strategy, algorithms capturing the capacity planning objectives, and a meta-optimisation strategy to tune the GA. • The proposed approaches are validated on two literature examples. • The proposed GA approaches are shown to achieve near-exact solutions and are demonstrated as a valid alternative to MILP models. The previous research work in the literature for capacity planning and scheduling of biopharmaceutical manufacture focused mostly on the use of mixed integer linear programming (MILP). This paper presents fast genetic algorithm (GA) approaches for solving discrete-time MILP problems of capacity planning and scheduling in the biopharmaceutical industry. The proposed approach is validated on two case studies from the literature and compared with MILP models. In case study 1, a medium-term capacity planning problem of a single-site, multi-suite, multi-product biopharmaceutical manufacture is presented. The GA is shown to achieve the global optimum on average 3.6 times faster than a MILP model. In case study 2, a larger long-term planning problem of multi-site, multi-product bio-manufacture is solved. Using the rolling horizon strategy, the GA is demonstrated to achieve near-optimal solutions (1% away from the global optimum) as fast as a MILP model." @default.
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• W2891353136 date "2019-02-01" @default.
• W2891353136 modified "2023-09-24" @default.
• W2891353136 title "Fast genetic algorithm approaches to solving discrete-time mixed integer linear programming problems of capacity planning and scheduling of biopharmaceutical manufacture" @default.
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• W2891353136 doi "https://doi.org/10.1016/j.compchemeng.2018.09.019" @default.
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