Simulation-optimization framework for stochastic optimization of R&D pipeline management

The simulation‐based optimization framework (Sim‐Opt) uses a twin‐loop computational architecture, which combines mathematical programming and discrete event simulation, to address this problem. This article extends our earlier work to present methods for integrating information from the inner loop...

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Bibliographic Details
Published in:AIChE journal Vol. 49; no. 1; pp. 96 - 112
Main Authors: Subramanian, Dharmashankar, Pekny, Joseph F., Reklaitis, Gintaras V., Blau, Gary E.
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.01.2003
Wiley Subscription Services
American Institute of Chemical Engineers
Subjects:
Applied sciences
Biological and medical sciences
Exact sciences and technology
General pharmacology
Medical sciences
Operational research and scientific management
Operational research. Management science
Optimization. Search problems
Pharmaceutical technology. Pharmaceutical industry
Pharmacology. Drug treatments
Drug
Computer simulation
Decision making
Project management
Linear programming
Mixed integer programming
Information management
Dynamical system
Integrated management
Optimization
Research and development
Stochastic programming
Product development
Pharmaceutical industry
Discrete event system
ISSN:0001-1541, 1547-5905
Online Access:Get full text
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Summary:The simulation‐based optimization framework (Sim‐Opt) uses a twin‐loop computational architecture, which combines mathematical programming and discrete event simulation, to address this problem. This article extends our earlier work to present methods for integrating information from the inner loop (Sim‐Opt time lines, reactive adjustment) and using it in the outer risk‐control loop (Stochastic Optimization loop) to obtain statistically significant improvements in the solutions to the underlying stochastic optimization problem. Two classes of information can be obtained from the inner loop time lines: the first pertaining to portfolio selection and the second resource crowding associated with the chosen operation policy. Methods presented quantify the information on these two classes, and a three‐step heuristic incorporates this information in the outer risk‐control loop to drive the system toward improving solutions with respect to the mean net present value (NPV) of the portfolio and the probability of delivering a positive NPV. This method was used on a pharmaceutical product development case study, consisting of 11 projects, 154 activities, 14 resource types and a 20‐year planning horizon with respect to patent expiration. Basic algorithm engineering efforts are also described to significantly improve the performance of formulation generation, the generation of a heuristic lower bound and the identification of cut families to effectively apply branch‐and‐cut methods.
Bibliography:ArticleID:AIC690490110
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ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0001-1541
1547-5905
DOI:10.1002/aic.690490110