2026-09-19T07:24:08Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/282952022-01-13T07:54:23Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Richard de Neufville.
Mittal, Geetanjali, 1979-
Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering.
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
2005-09-26T19:37:45Z
2005-09-26T19:37:45Z
2004
2004
http://hdl.handle.net/1721.1/28295
55589561
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.
Includes bibliographical references (p. 128-133).
This thesis highlights the effectiveness of Real Options Analysis (ROA) in capacity planning decisions for engineering projects subject to uncertainty. This is in contrast to the irreversible decision-making proposed by the deterministic strategies based on expected estimates of parameters drawn years in advance. Effectiveness is measured by three metrics: cost efficiency, capacity sufficiency and Value at Risk. The study documents the effects of uncertainty on planning facilities with high fixed-costs. It addresses engineers and planners by presenting fundamental insights of ROA without expecting Options-pricing knowledge a priori. The main idea is demonstrated via a case study of hydropower capacity planning. An analytical probabilistic capacity planning tool is developed to compare results given by traditional valuation and ROA. The tool may be useful for determining resource utilization policies and decision-making in the construction of such plants. Two specific options have been examined: (1) Vary size and timing of capacity increment (2) Defer hydropower plant construction to observe demand by relying on low fixed-cost and high operational-cost facilities in the initial years. The conclusion is that dynamic capacity planning approach is more effective if the forecasts are pessimistic or optimistic but not necessarily if realized parameters are similar to forecasts. Decisions based on distribution of driving factors and outcomes may be better aligned with the management's overall risk preferences than those based solely on expected mean of these parameters.
by Geetanjali Mittal.
S.M.
138 p.
8642943 bytes
8660860 bytes
application/pdf
application/pdf
en_US
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Civil and Environmental Engineering.
Real options approach to capacity planning under uncertainty
Thesis
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