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A simulation‐optimization framework for research and development pipeline management
Author(s) -
Subramanian Dharmashankar,
Pekny Joseph F.,
Reklaitis Gintaras V.
Publication year - 2001
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690471010
Subject(s) - computer science , stochastic programming , pipeline (software) , mathematical optimization , scheduling (production processes) , optimization problem , discrete event simulation , task (project management) , event (particle physics) , operations research , industrial engineering , engineering , systems engineering , mathematics , simulation , physics , quantum mechanics , programming language
Abstract The Research and Development Pipeline management problem has far‐reaching economic implications for new‐product‐development‐driven industries, such as pharmaceutical, biotechnology and agrochemical industries. Effective decision‐making is required with respect to portfolio selection and project task scheduling in the face of significant uncertainty and an ever‐constrained resource pool. The here‐and‐now stochastic optimization problem inherent to the management of an R&D Pipeline is described in its most general form, as well as a computing architecture, Sim‐Opt, that combines mathematical programming and discrete event system simulation to assess the uncertainty and control the risk present in the pipeline. The R&D Pipeline management problem is viewed in Sim‐Opt as the control problem of a performance‐oriented, resource‐constrained, stochastic, discrete‐event, dynamic system. The concept of time lines is used to study multiple unique realizations of the controlled evolution of the discrete‐event pipeline system. Four approaches using various degrees of rigor were investigated for the optimization module in Sim‐Opt, and their relative performance is explored through an industrially motivated case study. Methods are presented to efficiently integrate information across the time lines from this framework. This integration of information demonstrated in a case study was used to infer a creative operational policy for the corresponding here‐and‐now stochastic optimization problem.