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Decision-Making for Pharmaceutical R&D Project Management.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Decision-Making for Pharmaceutical R&D Project Management./
Author:	Wang, Hua.
Description:	1 online resource (169 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
Subject:	Chemical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29392010click for full text (PQDT)
ISBN:	9798841799962

Decision-Making for Pharmaceutical R&D Project Management.
Wang, Hua.

Decision-Making for Pharmaceutical R&D Project Management. - 1 online resource (169 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2022.

Includes bibliographical references

Project management is particularly important in the pharmaceutical industry as better planning reduces time-to-market, prolonging a product's profitable period under patent. The goal of this thesis is to apply the principles of mathematical optimization in the context of project management with an application for decision-making in the pharmaceutical industry.We first study the multi-mode resource constrained project scheduling problem (MRCPSP), which we generalize to account for the case of alternative prerequisite activities (MRCPSP-AP). The MRCPSP-AP arises in many real-world applications when two or more activities can serve as the required precursor to some subsequent activity, and it aims to determine not only the optimal schedule but also the optimal activity network. We propose new models and compare their numerical tractability through comprehensive computational studies on literature benchmarks. We also extend the well-known critical path method to handle the existence of alternative prerequisites, allowing us to precalculate tight time windows for each activity.We then turn our attention to an important application domain, where we extend MRCPSP models to project management in pharmaceutical industries. The research and development (R&D) management in any major research pharmaceutical company is constantly faced with the need to make complicated activity scheduling and resource allocation decisions, as they carry out scientific work to develop new therapeutic products. We develop a decision support tool that allows practitioners to determine portfolio-wide optimal schedules in a systematic, quantitative, and largely automated fashion. Our tool is based on a novel mixed-integer linear optimization model that extends archetypal MRCPSP models in order to accommodate multiple rich features that are pertinent to the Chemistry, Manufacturing, and Controls (CMC) activities carried out within the pharmaceutical R&D setting. The tool addresses this problem at the operational level, determining schedules that are optimal in light of chosen business objectives under activity sequencing, resource availability, and deadline constraints. Applying the tool to current workload data demonstrates its tractability for practical adoption. We further illustrate how, by utilizing the tool under different input instances, one may conduct various tactical analyses to assess the system's ability to cope with sudden changes or react to shifting management priorities.In addition, we establish a superstructure that accounts for all possible drug development paths that can be chosen during the pharmaceutical R&D process. Then, a general optimization model is formulated based on this superstructure representation for optimal activity and resource planning, as well as development path selection. Computational experiments, including synthetic data derived from the real-world portfolio, are conducted as a demonstration of the efficacy of the model. We propose heuristic and decomposition strategies for solving real-life large-scale portfolio instances, which can help identify feasible solutions for supporting decision-making.Finally, motivated by the pharmaceutical R&D environment setting, we study the MRCPSP under a general form of uncertainty, namely activity outcome uncertainty. We propose a novel tree-based algorithm to simulate the reactive decision-making process in the context of a rolling horizon, which assesses the resilience of a project scheduling solution against various types of activity outcome uncertainties. Reactive procedures are presented that can increase the quality resilience of the given baseline schedule facing activity outcome uncertainty.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841799962Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Decision makingIndex Terms--Genre/Form:

542853
Electronic books.

Decision-Making for Pharmaceutical R&D Project Management.
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500 $a Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
500 $a Advisor: Gounaris, Chrysanthos.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2022.
504 $a Includes bibliographical references
520 $a Project management is particularly important in the pharmaceutical industry as better planning reduces time-to-market, prolonging a product's profitable period under patent. The goal of this thesis is to apply the principles of mathematical optimization in the context of project management with an application for decision-making in the pharmaceutical industry.We first study the multi-mode resource constrained project scheduling problem (MRCPSP), which we generalize to account for the case of alternative prerequisite activities (MRCPSP-AP). The MRCPSP-AP arises in many real-world applications when two or more activities can serve as the required precursor to some subsequent activity, and it aims to determine not only the optimal schedule but also the optimal activity network. We propose new models and compare their numerical tractability through comprehensive computational studies on literature benchmarks. We also extend the well-known critical path method to handle the existence of alternative prerequisites, allowing us to precalculate tight time windows for each activity.We then turn our attention to an important application domain, where we extend MRCPSP models to project management in pharmaceutical industries. The research and development (R&D) management in any major research pharmaceutical company is constantly faced with the need to make complicated activity scheduling and resource allocation decisions, as they carry out scientific work to develop new therapeutic products. We develop a decision support tool that allows practitioners to determine portfolio-wide optimal schedules in a systematic, quantitative, and largely automated fashion. Our tool is based on a novel mixed-integer linear optimization model that extends archetypal MRCPSP models in order to accommodate multiple rich features that are pertinent to the Chemistry, Manufacturing, and Controls (CMC) activities carried out within the pharmaceutical R&D setting. The tool addresses this problem at the operational level, determining schedules that are optimal in light of chosen business objectives under activity sequencing, resource availability, and deadline constraints. Applying the tool to current workload data demonstrates its tractability for practical adoption. We further illustrate how, by utilizing the tool under different input instances, one may conduct various tactical analyses to assess the system's ability to cope with sudden changes or react to shifting management priorities.In addition, we establish a superstructure that accounts for all possible drug development paths that can be chosen during the pharmaceutical R&D process. Then, a general optimization model is formulated based on this superstructure representation for optimal activity and resource planning, as well as development path selection. Computational experiments, including synthetic data derived from the real-world portfolio, are conducted as a demonstration of the efficacy of the model. We propose heuristic and decomposition strategies for solving real-life large-scale portfolio instances, which can help identify feasible solutions for supporting decision-making.Finally, motivated by the pharmaceutical R&D environment setting, we study the MRCPSP under a general form of uncertainty, namely activity outcome uncertainty. We propose a novel tree-based algorithm to simulate the reactive decision-making process in the context of a rolling horizon, which assesses the resilience of a project scheduling solution against various types of activity outcome uncertainties. Reactive procedures are presented that can increase the quality resilience of the given baseline schedule facing activity outcome uncertainty.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Chemical engineering. $3 560457
650 4 $a Biomedical engineering. $3 535387
650 4 $a Pharmaceutical sciences. $3 3173021
653 $a Decision making
653 $a Pharmaceutical
653 $a Research and development
653 $a Project management
655 7 $a Electronic books. $2 lcsh $3 542853
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690 $a 0541
690 $a 0454
690 $a 0572
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Carnegie Mellon University. $b Chemical Engineering. $3 3174217
773 0 $t Dissertations Abstracts International $g 84-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29392010 $z click for full text (PQDT)

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