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Multi-Objective Pharmaceutical Portfolio Optimization under Uncertainty of Cost and Return

Author

Listed:
  • Mahboubeh Farid

    (Captario AB, 411 38 Gothenburg, Sweden)

  • Hampus Hallman

    (Captario AB, 411 38 Gothenburg, Sweden)

  • Mikael Palmblad

    (Captario AB, 411 38 Gothenburg, Sweden)

  • Johannes Vänngård

    (Captario AB, 411 38 Gothenburg, Sweden)

Abstract

This paper presents the study of multi-objective optimization of a pharmaceutical portfolio when both cost and return values are uncertain. Decision makers in the pharmaceutical industry encounter several challenges in deciding the optimal selection of drug projects for their portfolio since they have to consider several key aspects such as a long product-development process split into multiple phases, high cost and low probability of success. Additionally, the optimization often involves more than a single objective (goal) with a non-deterministic nature. The aim of the study is to develop a stochastic multi-objective approach in the frame of chance-constrained goal programming. The application of the results of this study allows pharmaceutical decision makers to handle two goals simultaneously, where one objective is to achieve a target return and another is to keep the cost within a finite annual budget. Finally, the numerical results for portfolio optimization are presented and discussed.

Suggested Citation

  • Mahboubeh Farid & Hampus Hallman & Mikael Palmblad & Johannes Vänngård, 2021. "Multi-Objective Pharmaceutical Portfolio Optimization under Uncertainty of Cost and Return," Mathematics, MDPI, vol. 9(18), pages 1-11, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2339-:d:639703
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    References listed on IDEAS

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    1. Cinzia Colapinto & Raja Jayaraman & Simone Marsiglio, 2017. "Multi-criteria decision analysis with goal programming in engineering, management and social sciences: a state-of-the art review," Annals of Operations Research, Springer, vol. 251(1), pages 7-40, April.
    2. Bruce L. Miller & Harvey M. Wagner, 1965. "Chance Constrained Programming with Joint Constraints," Operations Research, INFORMS, vol. 13(6), pages 930-945, December.
    3. Nitin R. Patel & Suresh Ankolekar, 2015. "Dynamically Optimizing Budget Allocation for Phase 3 Drug Development Portfolios Incorporating Uncertainty in the Pipeline," Springer Books, in: Zoran Antonijevic (ed.), Optimization of Pharmaceutical R&D Programs and Portfolios, edition 127, chapter 0, pages 181-200, Springer.
    4. A. Charnes & W. W. Cooper & G. H. Symonds, 1958. "Cost Horizons and Certainty Equivalents: An Approach to Stochastic Programming of Heating Oil," Management Science, INFORMS, vol. 4(3), pages 235-263, April.
    5. A. Charnes & W. W. Cooper, 1957. "Management Models and Industrial Applications of Linear Programming," Management Science, INFORMS, vol. 4(1), pages 38-91, October.
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