Design of engineering systems under uncertainty via real options and heuristic optimization

  • Hassan R
  • Neufville R
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This paper presents a practical procedure for using real options valuation in the design optimization of complex engineering systems. Recognition of future uncertainty in both design requirements and the operating environment is the point of departure – a significant shift away from traditional design practice that posits known values for key technical and economic factors. The process leads to the identification of an initial design and a strategy for implementing future expansions according to the way the future unfolds -- in contrast to the usual real options analyses that define a price for the option. Optimization of the design with the recognition of uncertainty leads to significant (greater than 10%) improvements in system performance. The optimization results in multiple desirable attributes, not only the maximization of expected value but also other considerations that may be useful to project managers, such as maximum possible loss or gain and the robustness of the design. A Value-at-Risk diagram conveniently displays these criteria. The optimization itself is extraordinarily complex, compared to standard options analyses, because the reality of design means that the performance of future states is not path independent (because the system evolves in response to its environment) so that the number of possible combinations is astronomical (over 10 to the power of 60 for the simple example problem). A Genetic Algorithm provides a practical solution to such problems, as demonstrated by a generic problem concerning the development of an offshore oil pipeline network.

Author-supplied keywords

  • adaptation
  • design under uncertainty
  • evolvability
  • flexibility
  • genetic algorithms
  • network optimization
  • optimization
  • petroleum production
  • real options
  • system architecture

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  • Rania Hassan

  • R de Neufville

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