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A Stochastic Optimization Model to Improve Production Planning and R&D Resource Allocation in Biopharmaceutical Production Processes

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  • Robert L. Schmidt

    (School of Business Administration, University of Southern California, Los Angeles, California 90089)

Abstract

The increasing cost of health care has brought pressure to reduce pharmaceutical costs, and because manufacturing and R&D are significant cost factors, these areas have been targeted as potential sources of cost reduction. Manufacturing costs are particularly high in the biotechnology industry because process technologies are relatively new. Contamination, genetic instability, and other factors complicate production planning and make bioprocess systems unreliable. This paper presents a Markov decision process model that combines features of engineering design models and aggregate production planning models to obtain a hybrid model that links biological and engineering parameters to optimize operations performance. Using tissue plasminogen activator as a specific example, the paper shows how the hybrid modeling approach not only improves production planning, but also provides accurate information on the operating performance of bioprocesses that can be used to predict the financial impact of process changes. Therefore, the model can be used to guide investments in manufacturing process improvement and R&D (e.g., genetic modifications). Although stochastic production models are not commonly used in process design, this paper shows how a combined engineering/production model can facilitate a concurrent design approach to reduce cost in bioprocess development.

Suggested Citation

  • Robert L. Schmidt, 1996. "A Stochastic Optimization Model to Improve Production Planning and R&D Resource Allocation in Biopharmaceutical Production Processes," Management Science, INFORMS, vol. 42(4), pages 603-617, April.
  • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:4:p:603-617
    DOI: 10.1287/mnsc.42.4.603
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    Cited by:

    1. Irina Dezhina & V. Kiseleva, 2008. "State, Science and Business in Russia's Innovation System," Research Paper Series, Gaidar Institute for Economic Policy, issue 115P.
    2. Ray R. Hashemi & Louis A. Le Blanc, 2000. "Resource Allocation through Negotiation and Compromise: A Database Approach," Group Decision and Negotiation, Springer, vol. 9(4), pages 325-345, July.
    3. Jeffrey S. Stonebraker, 2002. "How Bayer Makes Decisions to Develop New Drugs," Interfaces, INFORMS, vol. 32(6), pages 77-90, December.
    4. Sahling, Florian & Hahn, Gerd J., 2019. "Dynamic lot sizing in biopharmaceutical manufacturing," International Journal of Production Economics, Elsevier, vol. 207(C), pages 96-106.
    5. H. S. Yan, 2000. "Hierarchical Stochastic Production Planning with Delay Interaction," Journal of Optimization Theory and Applications, Springer, vol. 104(3), pages 659-689, March.

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