Simulation-based Methods for Studying Reliability and Preventive Maintenance of Public Infrastructure

  • Abhijit Gosavi
  • Susan Murray
Part of the Springer Series in Reliability Engineering book series (RELIABILITY)


In recent times, simulation has made significant progress as a tool for improving the performance of complex stochastic systems that arise in various domains in the industrial and service sectors. In particular, what is remarkable is that simulation is being increasingly used in diverse domains, e.g., devising strategies needed for emergency response to terrorist threats in homeland security systems and civil engineering of bridge structures for motor vehicle transport. In this chapter, we will focus on (1) describing some of the key decision-making problems underlying (a) response to emergency bomb-threat scenarios in a public building, and (b) prevention of catastrophic failures of bridges used for motor-vehicle transport; (2) providing an overview of simulation-based technologies that can be adopted for solving the associated problems. Our discussion will highlight some performance measures applicable to emergency response and prevention that can be estimated and improved upon via discrete-event simulation. We will describe two problem domains in which measurement of these metrics is critical for optimal decision-making. We believe that there is a great deal of interest currently, within both the academic world and the government sector, in enhancing our homeland security systems. Simulation already plays a vital role in this endeavor. The nature of the problems in this chapter is unconventional and quite unlike that seen commonly in classical simulation-based domains of manufacturing and service industries.


Reinforcement Learning Failure Probability Emergency Response Markov Decision Process Preventive Maintenance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Bertsekas D (1995) Dynamic programming and optimal control, vol 2. Athena, Nashua, NHzbMATHGoogle Scholar
  2. Bertsekas D, Tsitsiklis J (1996) Neuro-dynamic programming, Athena, Nashua, NHzbMATHGoogle Scholar
  3. Chung HY, Manuel L, Frank KH (2003) Optimal inspection scheduling with alternative fatigue reliability formulations for steel bridges. Applications of statistics and probability in civil engineering. Proceedings of ICASP2003, San Francisco, July 6–9. Millpress, RotterdamGoogle Scholar
  4. Frangopol DM, Lin KY, Estes AC (1997) Life-cycle cost design of deteriorating structures. J Struct Eng 123(10):1390–1401CrossRefGoogle Scholar
  5. Frangopol DM, Kong JS, Gharaibeh ES (2001) Reliability-based life-cycle management of highway bridges. J Comput Civ Eng 15(1):27–34CrossRefGoogle Scholar
  6. Frangopol DM, Kallne M-J, van Noorrtwijk JM (2004) Probabilistic models for life-cycle performance of deteriorating structures: review and future directions. Prog Struct Eng Mater 6:197–212CrossRefGoogle Scholar
  7. Fujiwara O, Makjamroen T, Gupta KK (1987) Ambulance deployment analysis: A case study of Bangkok. Eur J Oper Res 31:9–18CrossRefGoogle Scholar
  8. Golabi K, Shepard R (1997) Pontis: A system for maintenance optimization and improvement for US bridge networks. Interfaces 27(1):71–88CrossRefGoogle Scholar
  9. Gosakan M (2008) Modeling emergency response by building the campus incident case study. Final report. Leonard Wood Institute, MOGoogle Scholar
  10. Gosavi A (2003) Simulation-based optimization: parametric optimization and reinforcement learning. Kluwer Academic Publishers, Norwell, MA, USAzbMATHGoogle Scholar
  11. Gosavi A (2004) Reinforcement learning for long-run average cost. Eur J Oper Res 155:654–674zbMATHCrossRefMathSciNetGoogle Scholar
  12. Harewood SI (2002) Emergency ambulance deployment in Barbados: A multi-objective approach. J Oper Res Soc 53:185–192zbMATHCrossRefGoogle Scholar
  13. Henderson SG, Mason AJ (2004) Ambulance service planning: simulation and data visualisation. In: Brandeau ML, Sainfort F, Pierskalla WP (eds) Operations research and health care: a handbook of methods and applications. Kluwer, Norwell, MA, USAGoogle Scholar
  14. Hlupic V (2000) Simulation software: An operational research society survey of academic and industrial users. In: Joines JA, Barton RR, Kang K, Fishwick PA (eds) Proceedings of the 2000 winter simulation conference. Society for Computer Simulation International, San Diego, CA, USA, pp 1676–1683Google Scholar
  15. Ingolfsson A, Erkut E, Budge S (2003) Simulation of single start station for Edmonton EMS. J Oper Res Soc 54:736–746zbMATHCrossRefGoogle Scholar
  16. Juan A, Faulin J, Serrat C, Bargueño V (2008a) Improving availability of time-dependent complex systems by using the SAEDES simulation algorithms. Reliab Eng System Saf 93(11):1761–1771CrossRefGoogle Scholar
  17. Juan A, Faulin J, Serrat C, Sorroche M, Ferrer A (2008b) A simulation-based algorithm to predict time-dependent structural reliability. In: Rabe M (ed) Advances in simulation for production and logistics applications. Fraunhofer IRB Verlag, Stuttgart, pp 555–564Google Scholar
  18. Kleijnen J (2008) Design and analysis of simulation experiments. Springer, New York, NY, USAzbMATHGoogle Scholar
  19. Kolesar P, Swersey A (1985) The deployment of urban emergency units: a survey. TIMS Stud Manag Sci 22:87–119Google Scholar
  20. Kong JS, Frangopol DM (2003) Life-cycle reliability-based maintenance cost optimization of deteriorating structures with emphasis on bridges. J Struct Eng 129(6):818–828CrossRefGoogle Scholar
  21. Larson RC (1975) Approximating the performance of urban emergency service systems. Oper Res 23(5):845–868zbMATHCrossRefGoogle Scholar
  22. Law AM, Kelton WD (2000) Simulation modeling and analysis, 3rd edn. McGraw Hill, New York, NY, USAGoogle Scholar
  23. Martin M (2007) Distraught graduate student brings chaos to campus. Missouri Miner, March 1.Google Scholar
  24. Melchers RE (1999) Structural reliability analysis and prediction, 2nd edn. John Wiley, Cichester, UKGoogle Scholar
  25. Menk P, Mills M (1999) Domestic operations law handbook. Center for Law and Military Operations, US Army Office of the Judge Advocate General, Charlottesville, VA, USAGoogle Scholar
  26. Murray S, Ghosh K (2008) Modeling emergency response: a case study. Proceedings of American Society for Engineering management conference, West Point, NY, November. Curran Associates, Red Hook, NY, USAGoogle Scholar
  27. Pandey MD (1998) Probabilistic models for condition assessment of oil and gas pipelines. NDT&E Int 31(5):349–358CrossRefGoogle Scholar
  28. Pidd M, de Silva FN, Eglese RW (1996) A simulation model for emergency evacuation. Eur J Oper Res 90:413–419zbMATHCrossRefGoogle Scholar
  29. Robelin C-A, Madanat S (2007) History-dependent bridge deck maintenance and replacement optimization with Markov decision processes. J Infrastruct Syst 13(3):195–201Google Scholar
  30. Sommer A, Nowak A, Thoft-Cristensen P (1993) Probability-based bridge inspection strategy. J Struct Eng 119(12):3520–3526CrossRefGoogle Scholar
  31. Sutton R, Barto A (1998) Reinforcement learning: an introduction. MIT Press, Cambridge, MA, USAGoogle Scholar
  32. Walker W, Chaiken J, Ignall E (eds) (1979) Fire department deployment analysis. North Holland Press, New YorkGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Abhijit Gosavi
    • 1
  • Susan Murray
    • 1
  1. 1.Engineering Management and Systems EngineeringMissouri University of Science and TechnologyRollaUSA

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