Technical Papers
Feb 11, 2014

Risk-Based Two-Step Optimization Model for Highway Transportation Investment Decision-Making

Publication: Journal of Transportation Engineering
Volume 140, Issue 5

Abstract

A new methodology is introduced for project selection that explicitly addresses issues of achieving maximized overall project benefits by selecting a subcollection of candidate projects for possible implementation at a given budget level while controlling the total risk of the expected project benefits within an acceptable lower bound. The covariance value is utilized to denote the risk of the expected benefits of jointly implementing two projects, in which each project maintains a range of possible benefits and a probability distribution. The summation of all covariance values corresponding to all possible project implementation combinations represents the total risk of the expected benefits. The methodology contains two-step optimizations. First, the Markowitz mean-variance model is employed to establish the lower-bound risk of project benefits for a given budget level. Second, the conventionally accepted zero/one knapsack model for project selection is augmented to incorporate the lower-bound risk established from the first-step optimization as one additional chance constraint. In this way, the overall benefits of projects selected for implementation are maximized while controlling the total risk of the expected project benefits within a lower bound for a certain percentage of time. The proposed model is applied for a 6-year statewide interstate highway project selection and programming. Cross comparisons are made in the consistencies of project selection results generated from the basic knapsack model, proposed two-step enhanced knapsack model, and current state highway programming practice.

Get full access to this article

View all available purchase options and get full access to this article.

References

AASHTO. (2003). User benefit analysis for highways, Washington, DC.
Albanese, C., Jackson, K., and Wiberg, P. (2004). “A new Fourier transform algorithm for value-at-risk.” Quant. Finance, 4(3), 328–338.
Armstrong, R. D., Kung, P. S., Sinha, P., and Zoltners, A. A. (1983). “A computational study of a multiple-choice knapsack algorithm.” ACM Trans. Math. Software, 9(2), 184–198.
Bazaraa, M. S., Jarvis, J. J., and Sherali, H. D. (2005). Linear programming and network flows, 2nd Ed., Wiley, Hoboken, NJ.
Bodie, Z., Kane, A., and Marcus, A. J. (2005). Investments, 6th Ed., McGraw-Hill, Boston.
Cesare, M. J., Santamaria, C., Turkstra, C. J., and Vanmarcke, E. (1994). “Risk-based bridge management: Optimization and inspection scheduling.” Can. J. Civ. Eng., 21(6), 897–902.
Chu, P. C., and Beasley, J. E. (1998). “A genetic algorithm for the multidimensional knapsack problem.” J. Heuristics, 4(1), 63–86.
Dentcheva, D., and Ruszczynski, A. (2006). “Portfolio optimization with stochastic dominance constraints.” J. Bank. Finance, 30(2), 433–451.
Dyer, M. E., Riha, W. O., and Walker, J. (1995). “A hybrid dynamic programming/branch-and-bound algorithm for the multiple-choice knapsack problem.” J. Comput. Appl. Math., 58(1), 43–54.
Efton, E. S., and Gruber, M. S. (1991). Portfolio theory and investment analysis, Wiley, New York.
Feighan, K. J., Shahin, M. Y., Sinha, K. C., and White, T. D. (1988). “An application of dynamic programming and other mathematical techniques to pavement management systems.”, Transportation Research Board, Washington, DC, 90–98.
Federal Highway Administration (FHWA). (1996). Asset management: Advancing the state of the art into the 21st century through public-private dialogue, Washington, DC.
Federal Highway Administration (FHWA). (1998). “Highway statistics, 1997.”, Washington, DC.
Federal Highway Administration (FHWA). (2000). Highway economic requirements system, Washington, DC.
Geoffroy, D. N., and Shufon, J. J. (1992). “Network level pavement management in New York State: A goal-oriented approach.”, Transportation Research Board, Washington, DC, 57–65.
Ghorbani, S., and Rabbani, M. (2009). “A new multi-objective algorithm for a project selection problem.” Adv. Eng. Software, 40(1), 9–14.
Hall, J., and White, A. (1998). “Incorporating volatility updating into the historical simulation method for value-at-risk.” J. Risk, 1(1), 5–19.
Harper, W. V., et al. (1990). “Stochastic optimization subsystem of a network-level bridge management system.”, Transportation Research Board, Washington, DC, 68–74.
Hillier, F. S., and Lieberman, G. J. (2006). Introduction to operations research, 8th Ed., McGraw-Hill, New York.
Jha, M. K., and Abdullah, J. (2006). “A Markovian approach for optimizing highway life-cycle with genetic algorithms by considering maintenance of roadside appurtenances.” Model. Simulat. Appl. Optim., 343(4–5), 404–419.
King, A. J., and Wallace, S. W. (2012). Modeling with stochastic programming, Springer, New York.
Levy, H. (1992). “Stochastic dominance and expected utility: Survey and analysis.” Manage. Sci., 38(4), 555–593.
Li, Z., Madanu, S., Wang, Y., Abbas, M., and Zhou, B. (2010). “A heuristic approach for selecting highway investment alternatives.” Comput. Aided Civ. Infrastruct. Eng., 25(6), 427–439.
Li, Z., and Sinha, K. C. (2004). “A methodology for multicriteria decision making in highway asset management.”, Transportation Research Board, Washington, DC, 79–87.
Lorie, J. H., and Savage, L. J. (1955). “Three problems in rationing capital.” J. Educ. Bus., 28(4), 229–239.
Lounis, Z. (2006). “Risk-based maintenance optimization of aging highway bridge decks.” Advances in engineering structures, mechanics and construction, Springer, Rotterdam, Netherlands.
Luenberger, D. G. (1998). Investment science, Oxford University Press, New York.
Magazine, M. J., and Oguz, O. (1984). “A heuristic algorithm for the multidimensional zero-one knapsack problem.” Eur. J. Oper. Res., 16(3), 319–326.
Markowitz, H. M. (1987). Portfolio selection, Wiley, New York.
Martello, S., and Toth, P. (1990). Knapsack problems: Algorithms and computer implementations, Wiley, Chichester, U.K.
Mitra, D., and Wang, Q. (2005). “Stochastic traffic engineering for demand uncertainty and risk-aware network revenue management.” IEEE/ACM Trans. Network., 13(2), 221–233.
Neumann, L. A. (1997). “Methods for capital programming and project selection.” National Cooperative Highway Research Program (NCHRP) Synthesis 243, Transportation Research Board, Washington, DC.
Ravirala, V., and Grivas, D. A. (1995). “Goal-programming methodology for integrating pavement and bridge programs.” J. Transp. Eng., 345–351.
Sinha, P., and Zoltners, A. A. (1979). “The multiple-choice knapsack problem.” Oper. Res., 27(3), 503–515.
Teng, J. Y., and Tzeng, G. H. (1996). “A multiobjective programming approach for selecting non-independent transportation investment alternatives.” Transport. Res. B Methodolog., 30(4), 291–307.
Volgenant, A., and Zoon, J. A. (1990). “An improved heuristic for multidimensional 0-1 knapsack problems.” J. Oper. Res. Soc., 41(10), 963–970.
Weingartner, H. M. (1963). Mathematical programming and the analysis of capital budgeting problems, Prentice-Hall, Englewood Cliffs, NJ.
Wolfe, P. (1959). “The simplex method for quadratic programming.” Econometrica, 27(3), 382–398.
Wu, J., Yue, W., and Wang, S. (2006). “Stochastic model and analysis for capacity optimization in communication networks.” Comput. Commun., 29(12), 2377–2385.
XPRESS Solver Engine LP/QP/MIP [Computer software]. Incline Village, NV, Frontline Systems.
Zimmerman, K. A. (1995). “Pavement management methodologies to select projects and recommend preservation treatments.” National Cooperative Highway Research Program (NCHRP) Synthesis 222, Transportation Research Board, Washington, DC.

Information & Authors

Information

Published In

Go to Journal of Transportation Engineering
Journal of Transportation Engineering
Volume 140Issue 5May 2014

History

Received: Jul 1, 2013
Accepted: Nov 25, 2013
Published online: Feb 11, 2014
Published in print: May 1, 2014
Discussion open until: Jul 11, 2014

Permissions

Request permissions for this article.

Authors

Affiliations

Bei Zhou
Lecturer, Dept. of Traffic Engineering, Chang’an Univ., Xi’an 710064, P. R. China.
M.ASCE
Associate Professor, Dept. of Civil, Architectural and Environmental Engineering, Illinois Institute of Technology, Chicago, IL 60616 (corresponding author). E-mail: [email protected]
Harshingar Patel
Graduate Research Assistant, Dept. of Civil, Architectural and Environmental Engineering, Illinois Institute of Technology, Chicago, IL 60616.
Arash M. Roshandeh
Postdoctoral Researcher, School of Civil Engineering, Purdue Univ., West Lafayette, IN 47906.
Yuanqing Wang
Professor, Dept. of Traffic Engineering, Chang’an Univ., Xi’an 710064, P. R. China.

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share