IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v324y2025i1p308-323.html
   My bibliography  Save this article

Dynamic pharmaceutical product portfolio management with flexible resource profiles

Author

Listed:
  • Fei, Xin
  • Branke, Jürgen
  • Gülpınar, Nalân

Abstract

The pharmaceutical industry faces growing pressure to develop innovative, affordable products faster. Completing clinical trials on time is crucial, as revenue strongly depends on the finite patent protection. In this paper, we consider dynamic resource allocation for pharmaceutical product portfolio management and clinical trial scheduling, proposing a modelling framework, where resource profiles for ongoing clinical trials are flexible, with the possibility to add additional resources, thereby accelerating the completion of a clinical trial and enhancing pipeline profitability. Specifically, we treat both resource profiles and clinical trial scheduling as decision variables in the management of multiple pharmaceutical products to maximise the expected discounted profit, accounting for uncertainty in clinical trial outcomes. We formulate this problem as a Markov decision process and design a Monte Carlo tree search approach that can identify the best decision for each state by utilising a base policy to estimate value functions. We significantly improve the algorithm efficiency by proposing a statistical racing procedure using correlated sampling (common random numbers) and Bernstein’s inequality. We demonstrate the effectiveness of the proposed approach on a pharmaceutical drug development pipeline problem, finding that the proposed modelling framework with flexible resource profiles improves the resource efficiency and profitability, and the proposed Monte Carlo tree search algorithm outperforms existing approaches in terms of efficiency and solution quality.

Suggested Citation

  • Fei, Xin & Branke, Jürgen & Gülpınar, Nalân, 2025. "Dynamic pharmaceutical product portfolio management with flexible resource profiles," European Journal of Operational Research, Elsevier, vol. 324(1), pages 308-323.
  • Handle: RePEc:eee:ejores:v:324:y:2025:i:1:p:308-323
    DOI: 10.1016/j.ejor.2025.01.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221725000360
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2025.01.011?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Gómez Sánchez, Mariam & Lalla-Ruiz, Eduardo & Fernández Gil, Alejandro & Castro, Carlos & Voß, Stefan, 2023. "Resource-constrained multi-project scheduling problem: A survey," European Journal of Operational Research, Elsevier, vol. 309(3), pages 958-976.
    2. Hyeong Soo Chang & Michael C. Fu & Jiaqiao Hu & Steven I. Marcus, 2005. "An Adaptive Sampling Algorithm for Solving Markov Decision Processes," Operations Research, INFORMS, vol. 53(1), pages 126-139, February.
    3. Justin C. Goodson & Jeffrey W. Ohlmann & Barrett W. Thomas, 2013. "Rollout Policies for Dynamic Solutions to the Multivehicle Routing Problem with Stochastic Demand and Duration Limits," Operations Research, INFORMS, vol. 61(1), pages 138-154, February.
    4. Bertsimas, Dimitris & Griffith, J. Daniel & Gupta, Vishal & Kochenderfer, Mykel J. & Mišić, Velibor V., 2017. "A comparison of Monte Carlo tree search and rolling horizon optimization for large-scale dynamic resource allocation problems," European Journal of Operational Research, Elsevier, vol. 263(2), pages 664-678.
    5. Warren B. Powell, 2016. "Perspectives of approximate dynamic programming," Annals of Operations Research, Springer, vol. 241(1), pages 319-356, June.
    6. Stephen E. Chick & Jürgen Branke & Christian Schmidt, 2010. "Sequential Sampling to Myopically Maximize the Expected Value of Information," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 71-80, February.
    7. Tritschler, Martin & Naber, Anulark & Kolisch, Rainer, 2017. "A hybrid metaheuristic for resource-constrained project scheduling with flexible resource profiles," European Journal of Operational Research, Elsevier, vol. 262(1), pages 262-273.
    8. Satic, U. & Jacko, P. & Kirkbride, C., 2024. "A simulation-based approximate dynamic programming approach to dynamic and stochastic resource-constrained multi-project scheduling problem," European Journal of Operational Research, Elsevier, vol. 315(2), pages 454-469.
    9. Ben Issa, Samer & Patterson, Raymond A. & Tu, Yiliu, 2021. "Solving resource-constrained multi-project environment under different activity assumptions," International Journal of Production Economics, Elsevier, vol. 232(C).
    10. Nicola Secomandi, 2001. "A Rollout Policy for the Vehicle Routing Problem with Stochastic Demands," Operations Research, INFORMS, vol. 49(5), pages 796-802, October.
    11. Michael C. Fu & Jian-Qiang Hu & Chun-Hung Chen & Xiaoping Xiong, 2007. "Simulation Allocation for Determining the Best Design in the Presence of Correlated Sampling," INFORMS Journal on Computing, INFORMS, vol. 19(1), pages 101-111, February.
    12. Li, Haitao & Womer, Norman K., 2015. "Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 246(1), pages 20-33.
    13. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    14. Forman, Rebecca & Shah, Soleil & Jeurissen, Patrick & Jit, Mark & Mossialos, Elias, 2021. "COVID-19 vaccine challenges: What have we learned so far and what remains to be done?," Health Policy, Elsevier, vol. 125(5), pages 553-567.
    Full references (including those not matched with items on IDEAS)

    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. Yu Wu & Bo Zeng & Ming Jian, 2025. "ADP- and rollout-based dynamic vehicle routing for pick-up service via budgeting capacity," Flexible Services and Manufacturing Journal, Springer, vol. 37(2), pages 513-557, June.
    2. Shu Zhang & Jeffrey W. Ohlmann & Barrett W. Thomas, 2018. "Dynamic Orienteering on a Network of Queues," Transportation Science, INFORMS, vol. 52(3), pages 691-706, June.
    3. Wadi Khalid Anuar & Lai Soon Lee & Hsin-Vonn Seow & Stefan Pickl, 2021. "A Multi-Depot Vehicle Routing Problem with Stochastic Road Capacity and Reduced Two-Stage Stochastic Integer Linear Programming Models for Rollout Algorithm," Mathematics, MDPI, vol. 9(13), pages 1-44, July.
    4. Justin C. Goodson & Barrett W. Thomas & Jeffrey W. Ohlmann, 2016. "Restocking-Based Rollout Policies for the Vehicle Routing Problem with Stochastic Demand and Duration Limits," Transportation Science, INFORMS, vol. 50(2), pages 591-607, May.
    5. Juergen Branke & Wen Zhang, 2019. "Identifying efficient solutions via simulation: myopic multi-objective budget allocation for the bi-objective case," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(3), pages 831-865, September.
    6. Diana M. Negoescu & Peter I. Frazier & Warren B. Powell, 2011. "The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery," INFORMS Journal on Computing, INFORMS, vol. 23(3), pages 346-363, August.
    7. Majid Salavati-Khoshghalb & Michel Gendreau & Ola Jabali & Walter Rei, 2019. "A Rule-Based Recourse for the Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 53(5), pages 1334-1353, September.
    8. Daniel R. Jiang & Lina Al-Kanj & Warren B. Powell, 2020. "Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds," Operations Research, INFORMS, vol. 68(6), pages 1678-1697, November.
    9. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Zi Ding, 2015. "Sequential Selection with Unknown Correlation Structures," Operations Research, INFORMS, vol. 63(4), pages 931-948, August.
    10. Silva, Thiago A.O. & de Souza, Mauricio C., 2020. "Surgical scheduling under uncertainty by approximate dynamic programming," Omega, Elsevier, vol. 95(C).
    11. Wadi Khalid Anuar & Lai Soon Lee & Hsin-Vonn Seow & Stefan Pickl, 2022. "A Multi-Depot Dynamic Vehicle Routing Problem with Stochastic Road Capacity: An MDP Model and Dynamic Policy for Post-Decision State Rollout Algorithm in Reinforcement Learning," Mathematics, MDPI, vol. 10(15), pages 1-70, July.
    12. Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan & Qu, Xiaobo, 2022. "Dynamic stochastic electric vehicle routing with safe reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    13. Briseida Sarasola & Karl Doerner & Verena Schmid & Enrique Alba, 2016. "Variable neighborhood search for the stochastic and dynamic vehicle routing problem," Annals of Operations Research, Springer, vol. 236(2), pages 425-461, January.
    14. Bosse, Alexander & Ulmer, Marlin W. & Manni, Emanuele & Mattfeld, Dirk C., 2023. "Dynamic priority rules for combining on-demand passenger transportation and transportation of goods," European Journal of Operational Research, Elsevier, vol. 309(1), pages 399-408.
    15. Ilya O. Ryzhov, 2016. "On the Convergence Rates of Expected Improvement Methods," Operations Research, INFORMS, vol. 64(6), pages 1515-1528, December.
    16. Goodson, Justin C. & Thomas, Barrett W. & Ohlmann, Jeffrey W., 2017. "A rollout algorithm framework for heuristic solutions to finite-horizon stochastic dynamic programs," European Journal of Operational Research, Elsevier, vol. 258(1), pages 216-229.
    17. Yijie Peng & Chun-Hung Chen & Michael C. Fu & Jian-Qiang Hu, 2016. "Dynamic Sampling Allocation and Design Selection," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 195-208, May.
    18. Tschernutter, Daniel & Feuerriegel, Stefan, 2025. "Data-driven dynamic police patrolling: An efficient Monte Carlo tree search," European Journal of Operational Research, Elsevier, vol. 321(1), pages 177-191.
    19. Bin Han & Ilya O. Ryzhov & Boris Defourny, 2016. "Optimal Learning in Linear Regression with Combinatorial Feature Selection," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 721-735, November.
    20. Alejandro Toriello & William B. Haskell & Michael Poremba, 2014. "A Dynamic Traveling Salesman Problem with Stochastic Arc Costs," Operations Research, INFORMS, vol. 62(5), pages 1107-1125, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:324:y:2025:i:1:p:308-323. 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.