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• W2890265132 abstract "The implementation of efficient strategic decisions such as process design and capacity investment under uncertainty, during the product development process, is critical for the pharmaceutical industry. However, to tackle these problems the widely used multi-stage/scenario-based optimization formulations are still ineffective, especially for the first-stage (here-and-now) solutions where uncertainty has not yet been revealed. This study extends the authors’ previous work addressing the stochastic product-launch planning problem, by developing a new Multi-Objective Integer Programming model, embedded in a unified decision-making framework, to obtain the final design strategy that “maximizes” productivity while considering the decision-maker preferences. An approximation of the efficient Pareto-front is determined, and a subsequent Pareto solutions analysis is made to guide the decision process. The developed approach clearly identifies the process designs and production capacities that “maximize” productivity as well as the most promising solutions region for investment. Moreover, a good balance between investment and capacity allocation was achieved." @default.
• W2890265132 created "2018-09-27" @default.
• W2890265132 creator A5065919267 @default.
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• W2890265132 date "2018-11-01" @default.
• W2890265132 modified "2023-09-30" @default.
• W2890265132 title "Strategic decision-making in the pharmaceutical industry: A unified decision-making framework" @default.
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• W2890265132 doi "https://doi.org/10.1016/j.compchemeng.2018.09.010" @default.
• W2890265132 hasPublicationYear "2018" @default.
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