Abstract
Maintaining a rich research and development (R&D) pipeline is the key to remaining competitive in many industrial sectors. Due to its nature, R&D activities are subject to multiple sources of uncertainty, the modeling of which is compounded by the ability of the decision maker to alter the underlying process. In this paper, we present a multi-stage stochastic programming framework for R&D pipeline management, which demonstrates how essential considerations can be modeled in an efficient manner including: (i) the selection and scheduling of R&D tasks with general precedence constraints under pass/fail uncertainty, and (ii) resource planning decisions (expansion/contraction and outsourcing) for multiple resource types. Furthermore, we study interdependencies between tasks in terms of probability of success, resource usage and market impact. Finally, we explore risk management approaches, including novel formulations for value at risk and conditional value at risk.
Original language | English (US) |
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Pages (from-to) | 616-628 |
Number of pages | 13 |
Journal | European Journal of Operational Research |
Volume | 215 |
Issue number | 3 |
DOIs | |
State | Published - Dec 16 2011 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Modeling and Simulation
- Management Science and Operations Research
- Information Systems and Management
Keywords
- Project scheduling
- Research and development pipeline
- Resource planning
- Stochastic programming