IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v238y2014i1p327-338.html
   My bibliography  Save this article

An automated planning engine for biopharmaceutical production

Author

Listed:
  • Leachman, Robert C.
  • Johnston, Lenrick
  • Li, Shan
  • Shen, Zuo-Jun

Abstract

We introduce an optimization-based production planning tool for the biotechnology industry. The industry’s planning problem is unusually challenging because the entire production process is regulated by multiple external agencies – such as the US Food and Drug Administration – representing countries where the biopharmaceutical is to be sold. The model is structured to precisely capture the constraints imposed by current and projected regulatory approvals of processes and facilities, as well as capturing the outcomes of quality testing and processing options, facility capacities and initial status of work-in-process. The result is a supply chain “Planning Engine” that generates capacity-feasible batch processing schedules for each production facility within the biomanufacturing supply chain and an availability schedule for finished product against a known set of demands and regulations. Developing the formulation based on distinct time grids tailored for each facility, planning problems with more than 27,000 boolean variables, more than 130,000 linear variables and more than 80,000 constraints are automatically formulated and solved within a few hours. The Planning Engine’s development and implementation at Bayer Healthcare’s Berkeley, CA manufacturing site is described.

Suggested Citation

  • Leachman, Robert C. & Johnston, Lenrick & Li, Shan & Shen, Zuo-Jun, 2014. "An automated planning engine for biopharmaceutical production," European Journal of Operational Research, Elsevier, vol. 238(1), pages 327-338.
  • Handle: RePEc:eee:ejores:v:238:y:2014:i:1:p:327-338
    DOI: 10.1016/j.ejor.2014.03.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221714002161
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2014.03.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stefansson, Hlynur & Sigmarsdottir, Sigrun & Jensson, Pall & Shah, Nilay, 2011. "Discrete and continuous time representations and mathematical models for large production scheduling problems: A case study from the pharmaceutical industry," European Journal of Operational Research, Elsevier, vol. 215(2), pages 383-392, December.
    2. Francesco Gaglioppa & Lisa A. Miller & Saif Benjaafar, 2008. "Multitask and Multistage Production Planning and Scheduling for Process Industries," Operations Research, INFORMS, vol. 56(4), pages 1010-1025, August.
    3. Kopanos, Georgios M. & Méndez, Carlos A. & Puigjaner, Luis, 2010. "MIP-based decomposition strategies for large-scale scheduling problems in multiproduct multistage batch plants: A benchmark scheduling problem of the pharmaceutical industry," European Journal of Operational Research, Elsevier, vol. 207(2), pages 644-655, December.
    4. Steven T. Hackman & Robert C. Leachman, 1989. "A General Framework for Modeling Production," Management Science, INFORMS, vol. 35(4), pages 478-495, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sahling, Florian & Hahn, Gerd J., 2019. "Dynamic lot sizing in biopharmaceutical manufacturing," International Journal of Production Economics, Elsevier, vol. 207(C), pages 96-106.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Baumann, Philipp & Trautmann, Norbert, 2014. "A hybrid method for large-scale short-term scheduling of make-and-pack production processes," European Journal of Operational Research, Elsevier, vol. 236(2), pages 718-735.
    2. Haeussler, S. & Stampfer, C. & Missbauer, H., 2020. "Comparison of two optimization based order release models with fixed and variable lead times," International Journal of Production Economics, Elsevier, vol. 227(C).
    3. Ghadimi, Foad & Aouam, Tarik & Haeussler, Stefan & Uzsoy, Reha, 2022. "Integrated and hierarchical systems for coordinating order acceptance and release planning," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1277-1289.
    4. Craig A. Tovey, 2002. "Tutorial on Computational Complexity," Interfaces, INFORMS, vol. 32(3), pages 30-61, June.
    5. Ming Zhao & Minjiao Zhang, 2020. "Multiechelon Lot Sizing: New Complexities and Inequalities," Operations Research, INFORMS, vol. 68(2), pages 534-551, March.
    6. Cherchye, Laurens & De Rock, Bram & Kerstens, Pieter Jan, 2018. "Production with storable and durable inputs: Nonparametric analysis of intertemporal efficiency," European Journal of Operational Research, Elsevier, vol. 270(2), pages 498-513.
    7. Kefeli, Ali & Uzsoy, Reha & Fathi, Yahya & Kay, Michael, 2011. "Using a mathematical programming model to examine the marginal price of capacitated resources," International Journal of Production Economics, Elsevier, vol. 131(1), pages 383-391, May.
    8. Mustafa, Faizan E & Ahmed, Ijaz & Basit, Abdul & Alvi, Um-E-Habiba & Malik, Saddam Hussain & Mahmood, Atif & Ali, Paghunda Roheela, 2023. "A review on effective alarm management systems for industrial process control: Barriers and opportunities," International Journal of Critical Infrastructure Protection, Elsevier, vol. 41(C).
    9. Voll, Philip & Jennings, Mark & Hennen, Maike & Shah, Nilay & Bardow, André, 2015. "The optimum is not enough: A near-optimal solution paradigm for energy systems synthesis," Energy, Elsevier, vol. 82(C), pages 446-456.
    10. Phillip O. Kriett & Sebastian Eirich & Martin Grunow, 2017. "Cycle time-oriented mid-term production planning for semiconductor wafer fabrication," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4662-4679, August.
    11. Pecchioli, Bruno & Moroz, David, 2023. "Do geographical appellations provide useful quality signals? The case of Scotch single malt whiskies," Economic Modelling, Elsevier, vol. 124(C).
    12. Chatterjee A K & Mukherjee, Saral, 2006. "Unified Concept of Bottleneck," IIMA Working Papers WP2006-05-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    13. Sankaranarayanan, Sriram & Feijoo, Felipe & Siddiqui, Sauleh, 2018. "Sensitivity and covariance in stochastic complementarity problems with an application to North American natural gas markets," European Journal of Operational Research, Elsevier, vol. 268(1), pages 25-36.
    14. Pulluru, Sai Jishna & Akkerman, Renzo, 2018. "Water-integrated scheduling of batch process plants: Modelling approach and application in technology selection," European Journal of Operational Research, Elsevier, vol. 269(1), pages 227-243.
    15. Tom Rihm & Norbert Trautmann & Adrian Zimmermann, 2018. "MIP formulations for an application of project scheduling in human resource management," Flexible Services and Manufacturing Journal, Springer, vol. 30(4), pages 609-639, December.
    16. Minjiao Zhang & Simge Küçükyavuz & Hande Yaman, 2012. "A Polyhedral Study of Multiechelon Lot Sizing with Intermediate Demands," Operations Research, INFORMS, vol. 60(4), pages 918-935, August.
    17. Pürgstaller, Peter & Missbauer, Hubert, 2012. "Rule-based vs. optimisation-based order release in workload control: A simulation study of a MTO manufacturer," International Journal of Production Economics, Elsevier, vol. 140(2), pages 670-680.
    18. Karina Copil & Martin Wörbelauer & Herbert Meyr & Horst Tempelmeier, 2017. "Simultaneous lotsizing and scheduling problems: a classification and review of models," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(1), pages 1-64, January.
    19. Silvente, Javier & Aguirre, Adrián M. & Zamarripa, Miguel A. & Méndez, Carlos A. & Graells, Moisès & Espuña, Antonio, 2015. "Improved time representation model for the simultaneous energy supply and demand management in microgrids," Energy, Elsevier, vol. 87(C), pages 615-627.
    20. Sahling, Florian & Hahn, Gerd J., 2019. "Dynamic lot sizing in biopharmaceutical manufacturing," International Journal of Production Economics, Elsevier, vol. 207(C), pages 96-106.

    More about this item

    Keywords

    Biopharmaceutical production;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:238:y:2014:i:1:p:327-338. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.