Risk-adjusted budget allocation models with application in homeland security

Jian Hu, Tito Homem-De-Mello, Sanjay Mehrotra

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


This article presents and studies models for multi-criteria budget allocation problems under uncertainty. The proposed models incorporate uncertainties in decision maker's weights using a robust weighted sum approach. The risk averseness of the decision maker in satisfying random risk-related constraints is ensured by using stochastic dominance. A sample average approximation approach together with a cutting surface method is used to solve this model. An analysis for the computation of statistical lower and upper bounds is also given. The proposed models are used to study the budget allocation to ten urban areas in the United States under the Urban Areas Security Initiative. Here the decision maker considers property losses, fatalities, air departures, and average daily bridge traffic as separate criteria. The properties of the proposed modeling and solution methodology are discussed using a RAND Corporation-proposed allocation policy and the current government budget allocation as two benchmarks. The budget results are discussed under several parameter scenarios.

Original languageEnglish
Pages (from-to)819-839
Number of pages21
JournalIIE Transactions (Institute of Industrial Engineers)
Issue number12
StatePublished - Dec 2011
Externally publishedYes


  • Multi-criteria
  • homeland security
  • resource allocation
  • risk
  • robust optimization
  • stochastic dominance
  • stochastic programming


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