Skip to main content
Search
Search
Quick Search anywhere
Quick Search anywhere
Quick Search anywhere
Quick Search anywhere
Quick Search anywhere
Quick Search anywhere
Advanced Search
Skip main navigation
No Access

Optimal recovery sequencing for enhanced resilience and service restoration in transportation networks

Published Online:December 18, 2014pp 218-246https://doi.org/10.1504/IJCIS.2014.066356

Abstract

Critical infrastructure resilience has become a national priority for the US Department of Homeland Security. Rapid and efficient restoration of service in damaged transportation networks is a key area of focus. The intent of this paper is to formulate a bi-level optimisation model for network recovery and to demonstrate a solution approach for that optimisation model. The lower-level problem involves solving for network flows, while the upper-level problem identifies the optimal recovery modes and sequences, using tools from the literature on multi-mode project scheduling problems. Application and advantages of this method are demonstrated through two examples.

Keywords

infrastructure resilience, optimisation, transportation networks, project scheduling

References

  • 1. Alcaraz, J. , Maroto, C. , Ruiz, R. (2003). ‘Solving the multimode resource-constrained project scheduling problem with genetic algorithms’. Journal of Operational Research Society. 54, 6, 614-626 Google Scholar
  • 2. Bard, J.F. (1984). ‘Optimality conditions for the bilevel programming problem’. Naval Research Logistics Quarterly. 31, 1, 13-26 Google Scholar
  • 3. Bialas, W.F. , Karwan, M.H. (1984). ‘Two-level linear programming’. Management Science. 30, 8, 1004-1020 Google Scholar
  • 4. Bocchini, P. , Frangopol, D.M. (2012). ‘Restoration of bridge networks after an earthquake: multicriteria intervention optimization’. Earthquake Spectra. 28, 2, 427-455 Google Scholar
  • 5. Boctor, F.F. (1996). ‘Resource-constrained project scheduling by simulated annealing’. International Journal of Production Research. 34, 8, 2335-2351 Google Scholar
  • 6. Bouleimen, K. , Lecocq, H. (2003). ‘A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version’. European Journal of Operational Research. 149, 2, 268-281 Google Scholar
  • 7. Bruneau, M. , Chang, S. , Eguchi, R. , Lee, G. , O’Rourke, T. , Reinhorn, A. , Shinozuka, M. , Tierney, K. , Wallace, W. , von Winterfeldt, D. (2003). ‘A framework to quantitatively assess and enhance the seismic resilience of communities’. Earthquake Spectra. 19, 4, 733-752 Google Scholar
  • 8. Bush, G.W. (2002). ‘Homeland Security Presidential Directive-3 (HSPD-3)’. Washington, DC Google Scholar
  • 9. Bush, G.W. (2003). ‘Homeland Security Presidential Directive-7 (HSPD-7)’. Washington, DC Google Scholar
  • 10. Chang, S. , Shinozuka, M. (2004). ‘Measuring improvements in the disaster resilience of communities’. Earthquake Spectra. 20, 3, 739-755 Google Scholar
  • 11. Chen, L. , Miller-Hooks, E. (2012). ‘Resilience: an indicator of recovery capability in intermodal freight transport’. Transportation Science. 46, 1, 109-123 Google Scholar
  • 12. Chen, W-N. , Zhang, J. , Chung, H.S-H. , Huang, R-Z. , Liu, O. (2010). ‘Optimizing discounted cash flows in project scheduling – an ant colony optimization approach’. IEEE Transactions on Systems, Man and Cybernetics, Part C. 40, 1, 64-77 Google Scholar
  • 13. Clausen, J. , Larsen, A. , Larsen, J. , Rezanova, N. (2010). ‘Disruption management in the airline industry – concepts, models and methods’. Computers and Operations Research. 37, 5, 809-821 Google Scholar
  • 14. Clinton, W. (1998). ‘Presidential Decision Directive PDD-63, Protecting America’s Critical Infrastructures’. Washington, DC Google Scholar
  • 15. Cutter, S.L. , Burton, C.G. , Emrich, C.T. (2010). ‘Disaster resilience indicators for benchmarking baseline conditions’. Journal of Homeland Security and Emergency Management. 7, 1, Article 51, 10.2202/1547-7355.1732 Google Scholar
  • 16. Damak, N. , Jarboui, B. , Siarry, P. , Loukil, T. (2009). ‘Differential evolution for solving, multi-mode resource-constrained project scheduling problems’. Computers & Operations Research. 36, 9, 2653-2659 Google Scholar
  • 17. Fiksel, J. (2003). ‘Designing resilient, sustainable systems’. Environmental Science and Technology. 37, 23, 5330-5339 Google Scholar
  • 18. Fisher, R.E. , Norman, M. (2010). ‘Developing measurement indices to enhance protection and resilience of critical infrastructures and key resources’. Journal of Business Continuity and Emergency Planning. 4, 3, 191-206 Google Scholar
  • 19. Hartmann, S. (2001). ‘Project scheduling with multiple modes: a genetic algorithm’. Annals of Operations Research. 102, 1–4, 111-135 Google Scholar
  • 20. Henry, D. , Ramirez-Marquez, J.E. (2012). ‘Generic metrics and quantitative approaches for system resilience as a function of time’. Reliability Engineering and System Safety. 99, 114-122 Google Scholar
  • 21. Holling, C. (1973). ‘Resilience and stability of ecological systems’. Annual Review of Ecology and Systematics. 4, 1-23 Google Scholar
  • 22. Jarboui, B. , Damak, N. , Siarry, P. , Rebai, A. (2008). ‘A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling problems’. Applied Mathematics and Computation. 195, 1, 299-308 Google Scholar
  • 23. Jozefowska, J. , Mika, M. , Rozychi, R. , Waligora, G. , Weglarz, J. (2001). ‘Simulated annealing for multimode resource-constrained project scheduling’. Annals of Operations Research. 102, 1–4, 137-155 Google Scholar
  • 24. Katina, P.F. , Hester, P.T. (2013). ‘Systemic determination of infrastructure criticality’. International Journal of Critical Infrastructures. 9, 3, 211-225 Abstract, Google Scholar
  • 25. Kolisch, R. , Drexl, A. (1997). ‘Local search for nonpreemptive multimode resource-constrained project scheduling’. IIE Transactions. 29, 11, 987-999 Google Scholar
  • 26. Luna, R. , Balakrishnan, N. , Dagli, C. (2011). ‘Postearthquake recovery of a water distribution system: discrete event simulation using colored Petri nets’. J. Infrastruct. Syst.. 17, 1, 25-34 Google Scholar
  • 27. Madni, A.A. , Jackson, S. (2009). ‘Towards a conceptual framework for resilience engineering’. IEEE Systems Journal. 3, 2, 181-191 Google Scholar
  • 28. Mori, M. , Tseng, C.C. (1997). ‘A genetic algorithm for multimode resource-constrained project scheduling’. European Journal of Operational Research. 100, 1, 134-141 Google Scholar
  • 29. Obama, B. (2013). ‘Presidential Policy Directive 21: Critical Infrastructure Security and Resilience’. Washington, DC Google Scholar
  • 30. Ozdamar, L. (1999). ‘A genetic algorithm approach to a general category project scheduling problem’. IEEE Transactions on Systems, Man and Cybernetics, Part C. 29, 1, 44-59 Google Scholar
  • 31. Park, J. , Seager, T.P. , Rao, P.S.C. , Convertino, M. , Linkov, I. (2013). ‘Integrating risk and resilience approaches to catastrophe management in engineering systems’. Risk Analysis. 33, 3, 356-366 Google Scholar
  • 32. Reagan, R. (1982). ‘Executive Order 13282, National Security Telecommunications Advisory Committee’. Washington, DC Google Scholar
  • 33. Rose, A. (2007). ‘Economic resilience to natural and man-made disasters; multidisciplinary origins and contextual dimensions’. Environmental Hazards. 7, 4, 383-398 Google Scholar
  • 34. Rose, A. , Liao, S-Y. (2005). ‘Modeling regional economic resilience to disasters: a computable general equilibrium analysis of water service disruptions’. Journal of Regional Science. 45, 1, 75-112 Google Scholar
  • 35. Sempier, T.T. , Swann, D.L. , Emmer, R. , Sempier, S.H. , Schneider, M. (2010). ‘Coastal community resilience index: a community self-assessment’. (accessed 17 June 2013), [online] http://www.masgc.org/pdf/masgp/08-014.pdf Google Scholar
  • 36. Tierney, K. , Bruneau, M. ‘Conceptualizing and measuring resilience: a key to disaster loss reduction’. TR News. 2007, 05, 14-17 Google Scholar
  • 37. Transportation Safety Administration (TSA) (2007). Transportation Systems Critical Infrastructure and Key Resources Sector-Specific Plan as Input to the National Infrastructure Protection Plan. Washington, DC Google Scholar
  • 38. Tseng, L-Y. , Chen, S-C. (2009). ‘Two-phase genetic local search algorithm for the multimode resource-constrained project scheduling problem’. IEEE Transactions on Evolutionary Computation. 3, 4, 848-857 Google Scholar
  • 39. Vugrin, E.D. , Camphouse, R.C. ‘Infrastructure resilience assessment through control design’. International Journal of Critical Infrastructures. 2011, 10, 7, 3, 243-260 Abstract, Google Scholar
  • 40. Vugrin, E.D. , Warren, D.E. , Ehlen, M.A. , Camphouse, R.C. , Gopalakrishnan, K. Peeta, S. (2010). ‘A framework for assessing the resilience of infrastructure and economic systems’. Sustainable and Resilient Critical Infrastructure Systems: Simulation, Modeling and Intelligent Engineering. New York:Springer-Verlag, Inc. Google Scholar
  • 41. Wang, J. , Qiao, C. , Yu, H. (2011). ‘On progressive network recovery after a major disruption’. Proceedings of IEEE INFOCOM 2011. 10–15 April, Shanghai Google Scholar
  • 42. Xu, N. , Guikema, S.D. , Davidson, R.A. , Nozick, L.K. , Çağnan, Z. , Vaziri, K. (2007). ‘Optimizing scheduling of post-earthquake electric power restoration tasks’. Earthquake Engineering and Structural Dynamics. 36, 2, 265-284 Google Scholar
Previous article
Next article