Risk-adjusted budget allocation models with application in homeland security

IIE Transactions (Institute of Industrial Engineers)

Volumn 43, Issue 12, 2011, Pages 819-839

  Hu, Jian ^a   Homem De Mello, Tito ^b   Mehrotra, Sanjay ^a  

^a Northwestern University   (United States)

^b UNIVERSIDAD ADOLFO IBÁÑEZ   (Chile)

Author keywords

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

Indexed keywords

ALLOCATION POLICIES; BUDGET ALLOCATION; DECISION MAKERS; HOME LAND SECURITY; LOWER AND UPPER BOUNDS; MULTI-CRITERIA; PROPERTY LOSS; RISK-ADJUSTED; ROBUST OPTIMIZATION; SAMPLE AVERAGE APPROXIMATION; SOLUTION METHODOLOGY; STOCHASTIC DOMINANCE; SURFACE METHODS; URBAN AREAS; WEIGHTED SUM;

DECISION MAKING; RESOURCE ALLOCATION; RISKS; SECURITY SYSTEMS; STOCHASTIC MODELS; STOCHASTIC PROGRAMMING; STOCHASTIC SYSTEMS;

BUDGET CONTROL;

EID: 80053472366     PISSN: 0740817X     EISSN: 15458830     Source Type: Journal    
DOI: 10.1080/0740817X.2011.578610     Document Type: Article

References (49)

1. Al-Najjar, N., Baliga, S. and David, B. (2008) Market forces meet behavioral biases: cost misallocation and irrational pricing. RAND Journal of Economics, 39(1), 214-237.
2. Azaiez, M. and Bier, V. (2007) Optimal resource allocation for security in reliability systems. European Journal of Operational Research, 181, 773-786. (Pubitemid 46413583)
3. Bazaraa, M.S., Sherali, H.D. and Shetty, C.M. (2006) Nonlinear Programming Theory and Algorithms, third edition, Wiley, New York, NY.
4. Bier, V., Haphuriwat, M., Meonoyo, N., Zimmerman, J.R. and Culpen. A.M. (2008) Optimal resource allocation for defense of targets based on differing measures of attractiveness. Risk Analysis, 28, 763-770. (Pubitemid 351847236)
5. Birge, J. (2009). Uses of sub-sample estimates to reduce errors in optimization models. Working paper. Booth School of Business, The University of Chicago, Chicago, IL. Available at http://www.mendeley.com/proffles/john-birge/ publications/working-paper/, accessed February 10, 2011.
6. Caudle, S. (2005). Homeland security capabilities-based planning: lessons from the defense community. Homeland Security Affairs, 1(2). Available at http://www.hsaj.org/?article=1.2.2, accessed February 10, 2011.
7. Chauvenet, A.L.M., Baxter, P.W.J. McDonald-Madden, E. and Possing-ham, H.P. (2010) Optimal allocation of conservation effort among subpopulations of a threatened species: how important is patch quality? Ecological Applications, 20(3), 789-797.
8. Dentcheva, D. and Ruszczyiiski, A. (2003) Optimization with stochastic dominance constraints. SIAM Journal on Optimization, 14(2), 548566.
9. Dentcheva, D. and Ruszczynski, A. (2004) Optimality and duality theory for stochastic optimization problems with nonlinear dominance constraints. Mathematical Programming, 99, 329-350. (Pubitemid 40871726)
10. Dentcheva, D. and Ruszczynski, A. (2006) Portfolio optimization with stochastic dominance constraints. Journal of Banking Finance, 30(1), 433-451. (Pubitemid 43257143)
11. Dentcheva, D. and Ruszczynski, A. (2009) Optimization with multivari-ate stochastic dominance constraints. Mathematical Programming, 117, 111-127.
12. Dougherty, C. and Psacharopoulos, G. (1977) Measuring the cost of mis-allocation of investment in education. Journal of Human Resources, 12(4), 446-459.
13. Farrow, S. and Stuart, S. (2009) The benefit-cost analysis of security focused regulations. Journal ofHomeland Security and Emergency Management, 6(1), Article 25.
14. Flyvbjerg, B. Bruzelius, N. and Rothengatter, W. (2003) Megaprojects and Risk: An Anatomy ofAmbition, Cambridge University Press, Cambridge, UK.
15. Gollmer, R., Gotzes, U., Neise, F. and Schultz, R. (2007) Risk modeling via stochastic dominance in power systems with dispersed generation. Department of Mathematics, University of Duisburg-Essen, Duisburg, Germany.
16. Haimes, Y.Y. (2004) Risk Modeling, Assessment, and Management, Wiley, New York, NY.
17. Hoeffding, W. (1963) Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58, 13-30.
18. Homem-de-Mello, T. and Mehrotra, S. (2009). A cutting surface method for linear programs with polyhedral stochastic dominance constraints. SIAM Journal on Optimization, 20(3), 1250-1273.
19. Hougham, M. (2000) Syllabus for the APMP Examination, 2nd ed. Association for Project Management, UK.
20. Hu, J., Homem-de-Mello, T. and Mehrotra, S. (2010) Sample average approximation of stochastic dominance constrained programs. Mathematical Programming (to appear).
21. Hu, J. and Mehrotra, S. (2010) Robust and stochastically weighted multi-objective optimization models and reformulations. Available at http://www.optimization-online.org/DBHTML/2010/10/2762.html, accessed February 10, 2011.
22. Huang, C., Kira, D. and Vertinsky, I. (1978) Stochastic dominance rules for multiattribute utility functions. Review of Economic Studies, 41, 611-616.
23. Karlaftis, M.G., Kepaptsoglou, K.L. and Lambropoulos, S. (2007) Fund allocation for transportation network recovery following natural disasters. Journal of Urban Planning and Development, 133(1), 8289.
24. Karoui, N.E. and Meziou, A. (2006) Constrained optimization with respect to stochastic dominance: application to portfolio insurance. Mathematical Finance, 16(1), 103-117. (Pubitemid 43437435)
25. Keeney, R.L. and Raiffa, H. (1976) Decisions with Multiple Objectives: Preferences and Value Tradeoffs, John Wiley & Sons, New York, NY.
26. Lerche, I. and Paleologos, E.K. (2001) Environmental Risk Analysis, McGraw-Hill, Englewood Cliffs, NJ.
27. Levy, H. (2006) Stochastic Dominance: Investment Decision Making under Uncertainty, Springer, New York, NY.
28. Lin, M.H., Tsai, J.F. and Ye, Y. (2009) Budget allocation in a competitive communication spectrum economy. EURASIP Journal on Advances in Signal Processing. Available at http://www.hindawi.com/ journals/asp/2009/ 963717/, accessed February 10, 2011.
29. Luedtke, J. (2008) New formulations for optimization under stochastic dominance constraints. SIAM Journal on Optimization, 19(3), 14331450.
30. Mak, W.K., Morton, D.P. and Wood, R.K. (1999) Monte Carlobounding techniques for determining solution quality in stochastic programs. Operations Research Letters, 24, 47-56.
31. Miller, F.P., Vandome, A.F. and McBrewster, J. (eds) (2010) Cost Overrun: Cost, Budget, Cost Escalation, Infrastructure, Building, Project, Information Technology, Telescopic Sight, Sydney Opera House, Concorde, Alphascript Publishing, Beau, Bassin Mauritius.
32. Muller, A. and Stoyan, D. (2002) Comparison Methods for Stochastic Models and Risks, John Wiley & Sons, Chichester, UK.
33. Nie, Y., Wu, X. and Homem-de-Mello, T. (2009) Optimal path problems with second-order stochastic dominance constraints. Transportation Research Board 88th Annual Meeting, Washington DC, January 11-15, 2009. Paper #09-1599.
34. Nowak, M. (2004) Interactive approach in multicriteria analysis based on stochastic dominance. Control and Cybernetics, 33(3), 463-476. (Pubitemid 40401391)
35. Parnell, G., Dillon-Merrill, R. and Bresnick, T. (2006) Integrating risk management with security and antiterrorism resource allocation decision making, in The McGraw-Hill Homeland Security Handbook, Kamien, D.G. (ed), McGraw-Hill, New York, NY, pp. 431-461.
36. Paxson, C. and Schady, N.R. (2002) The allocation and impact of social funds: spending on school infrastructure in Peru. The World Bank Economic Review, 16(2), 297-319. (Pubitemid 36049029)
37. Phillips, L.D. and Bana e Costa, C.A. (2007) Transparent prioritisa-tion, budgeting and resource allocation with multi-criteria decision analysis and decision conferencing. Annals ofOperations Research, 154(1), 51-68.
38. Powell, R. (2007) Defending against terrorist attacks with limited resources. American Political Science Review, 101, 527541.
39. Reifel, C. (2006) Quantitative risk analysis for homeland security resource allocation. Master's thesis, Naval Postgraduate School, Monterey, CA.
40. Roman, D., Darby-Dowman, K. and Mitra, G. (2006) Portfolio construction based on stochastic dominance and target return distributions. Mathematical Programming, 108, 541-569. (Pubitemid 43999772)
41. Shaked, M. and Shanthikumar, J.G. (1994) Stochastic Orders and their Applications, Academic Press, Boston.
42. Sriboonchita, S., Wong, W., Dhompongsa, S. and Nguyen, H.T. (2009) Stochastic Dominance and Applications to Finance, Risk and Economics, Chapman & Hall.
43. Steuer, R.E. and Na, P. (2003) Multiple criteria decision making combined with finance: a categorized bibliographic study. European Journal of Operational Research, 150(3), 496-515.
44. Tseng, C.-L., Lin, K.Y. and Sundararajan, S.K. (2005) Managing cost overrn risk in project funding allocation. Annals ofOperations Research, 135, 127-153. (Pubitemid 41227405)
45. Willis, H.H. and Kleinmuntz, D. (2007) Applying optimal capital allocation methods to homeland security resources: a case study of California's allocation of the buffer zone protection plan grants. Presented at the DHS University Network Summit on Research and Education, Washington DC, March 15-16, 2007.
46. Willis, H.H., Morral, A.R., Kelly, T.K. and Medby, J.J. (2005) Estimating Terrorism Risk. RAND Corporation, Santa Monica, 2005. Available at: http://www.rand.org/ pubs/monographs/2005/RAND-MG388.pdf, accessed February 10, 2011.
47. Wright, D.P., Liberatore, M.J. and Nydick, R.L. (2006) A survey of operations research models and applications in homeland security. Interfaces, 36(6), 514-529. (Pubitemid 44905061)
48. Zaras, K. and Martel, J. (1994) Multiattribute Analysis Based on Stochastic Dominance, Kluwer Academic Publishers, Dordrecht, The Netherlands.
49. Zhuang, J. and Bier, V. (2007) Balancing terrorism and natural disasters- defensive strategy with endogenous attacker effort. Operations Research, 55, 976-991. (Pubitemid 351159542)