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One-stage R&D portfolio optimization with an application to solid oxide fuel cells

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Abstract

This paper provides an overview of the one-stage R&D portfolio optimization problem. It provides a novel problem model that can be solved with stochastic combinatorial optimization methods. Current solution methods are reviewed and a new method that scales to large problems, Stochastic Gradient Portfolio Optimization (SGPO), is proposed. Although SGPO is a heuristic method, we prove global convergence in certain conditions. SGPO is numerically compared to current optimization methods on a test case involving Solid Oxide Fuel Cells.

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Authors and Affiliations

  1. Department of Operations Research and Financial Engineering, Princeton University, Sherrerd Hall, Princeton, NJ, 08544, USA

    Lauren Hannah & Warren Powell

  2. Regional Energy System Technology Analysis Center, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94551, USA

    Jeffrey Stewart

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  1. Lauren Hannah
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  2. Warren Powell
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Correspondence to Lauren Hannah.

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Hannah, L., Powell, W. & Stewart, J. One-stage R&D portfolio optimization with an application to solid oxide fuel cells. Energy Syst 1, 141–163 (2010). https://doi.org/10.1007/s12667-009-0008-3

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