NDLI: A scenario tree approach to multi-period project selection problem using real-option valuation method

Content Provider	Springer Nature Link
Author	Rafiee, Majid Kianfar, Farhad
Copyright Year	2011
Abstract	Multistage stochastic programs are effective for solving long-term planning problems under uncertainty. Multi-period project portfolio selection problems can be modeled by multistage stochastic programs. These models utilize a set of scenarios and corresponding probabilities to model the multivariate random data process (costs or revenues, available budget, chance of success). For most practical problems, the optimization problem that contains all possible scenarios is too large. Due to computational complexity, this program is often approximated by a model involving a (much) smaller number of scenarios. The scenario reduction algorithms determine a subset of the initial scenario set and assign new probabilities to the preserved scenarios such that optimal values and approximate solution sets remain close to each other. This paper presents a stochastic optimization model of pharmaceutical multi-period project selection problem using a real-option valuation method. Subsequently, it reduces scenarios via existing algorithms to generate a new (smaller) scenario tree. Model with reduced scenarios has been solved in this research. Numerical results indicate improved portfolios and show the effectiveness of the proposed algorithm.
Starting Page	411
Ending Page	420
Page Count	10
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	56
Issue Number	1-4
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-03-29
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Project selection Scenario tree Stochastic optimization Real option valuation Scenario reduction Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics/Supply Chain Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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