Portfolio-Wide Optimization of Pharmaceutical R&D Activities Using Mathematical Programming

Hua Wang (), Jon Dieringer (), Steve Guntz (), Shankarraman Vaidyaraman (), Shekhar Viswanath (), Nikolaos H. Lappas (), Sal Garcia-Munoz () and Chrysanthos E. Gounaris ()
Additional contact information
Hua Wang: Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Jon Dieringer: Synthetic Molecule Design and Development, Eli Lilly and Company, Indianapolis, Indiana 46225
Steve Guntz: Synthetic Molecule Design and Development, Eli Lilly and Company, Indianapolis, Indiana 46225
Shankarraman Vaidyaraman: Synthetic Molecule Design and Development, Eli Lilly and Company, Indianapolis, Indiana 46225
Shekhar Viswanath: Synthetic Molecule Design and Development, Eli Lilly and Company, Indianapolis, Indiana 46225
Nikolaos H. Lappas: Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213;
Sal Garcia-Munoz: Synthetic Molecule Design and Development, Eli Lilly and Company, Indianapolis, Indiana 46225
Chrysanthos E. Gounaris: Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Interfaces, 2021, vol. 51, issue 4, 262-279

Abstract: 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. This paper describes how 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 multimode resource-constrained project scheduling 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 on 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.

Keywords: pharmaceutical R&D; CMC (chemistry manufacturing and controls); project scheduling; mixed-integer linear optimization (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:51:y:2021:i:4:p:262-279

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