Utility-maximizing resource control: Diffusion limit and asymptotic optimality for a two-bottleneck model

Hengqing Ye, David D. Yao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


Stochastic programming can effectively describe many decision-making problems in uncertain environments. Unfortunately,such programs are often computationally demanding to solve. In addition, their solution can be misleading when there is ambiguity in the choice of a distribution for the random parameters. In this paper, we propose a model that describes uncertainty in both the distribution form (discrete, Gaussian, exponential, etc.) and moments (mean and covariance matrix). We demonstrate that for a wide range of cost functions the associated distributionally robust (or min-max) stochastic program can be solved efficiently. Furthermore, by deriving a new confidence region for the mean and the covariance matrix of a random vector, we provide probabilistic arguments for using our model in problems that rely heavily on historical data. These arguments are confirmed in a practical example of portfolio selection, where our framework leads to better-performing policies on the "true" distribution underlying the daily returns of financial assets.
Original languageEnglish
Pages (from-to)613-623
Number of pages11
JournalOperations Research
Issue number3
Publication statusPublished - 1 May 2010

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research


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