Real-Time Emergency Response Fleet Deployment: Concepts, Systems, Simulation & Case Studies

  • Ali Haghani
  • Saini Yang
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 38)


Dynamic response to emergencies requires real time information from transportation agencies, public safety agencies and hospitals as well as the many essential operational components. In emergency response operations, good vehicle dispatching strategies can result in more efficient service by reducing vehicles’ travel times and system preparation time and the coordination between these components directly influences the effectiveness of activities involved in emergency response. In this chapter, an integrated emergency response fleet deployment system is proposed which embeds an optimization approach to assist the dispatch center operators in assigning emergency vehicles to emergency calls, while having the capability to look ahead for future demands. The mathematical model deals with the real time vehicle dispatching problem while accounting for the service requirements and coverage concerns for future demand by relocating and diverting the on-route vehicles and remaining vehicles among stations. A rolling-horizon approach is adopted in the model to reduce the relocation sites in order to save computation time. A simulation program is developed to validate the model and to compare various dispatching strategies


Emergency Vehicle Response Time Deployment Real-Time Optimization Simulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ball, M. O and F.L. Lin, Reliability Model Applied to Emergency Service Vehicle Location, Operations Research 41, pp.18-36 (1993).Google Scholar
  2. Brotcorne L, G. Laporte and F. Semet, Ambulance Location and Relocation Models, Eur J Opl Res, Vol 147, pp. 451-463 (2003).CrossRefGoogle Scholar
  3. Brown, G.G. and R. McBride, Solving Generalized Networks, Management Science, Vol. 20, pp. 1497-1523 (1985).Google Scholar
  4. Carter, G. and E. Ignall, A Simulation Model of Fire Department Operations, IEEE System Science and Cybernetics, Vol. 5, pp. 282-293 (1970).Google Scholar
  5. Catrysse, D. and Van Wassenhove, L.N, A Survey of Algorithms for the Generalized Assignment Problem,European Journal of Operational Research, Vol. 60, pp. 260-272 (1993).CrossRefGoogle Scholar
  6. Chabini, I., Discrete Dynamic Shortest Path Problems in Transportation Application, Transportation Research Record, No. 1645,pp. 170-175 (1998).CrossRefGoogle Scholar
  7. Chaiken, J. and R. Larson, Methods for Allocating Urban Emergency Units: A Survey, Management Science, 19, pp. 110-130 (1998).Google Scholar
  8. Chang, E., Traffic Estimation for Proactive Freeway Traffic Control, Transportation Research Record, No.1679, pp. 81-86 (1999).CrossRefGoogle Scholar
  9. Chang, M. F. and D. C. Gazis, Traffic Density Estimation with Consideration of Lane Changing, Transportation Science, Vol. 9, No. 4, pp. 308-320 (1975).CrossRefGoogle Scholar
  10. Chu, P.C. and J.E. Beasley, A Genetic Algorithms for the Generalized Assignment Problem, Camp. Operations Research 24 (1), pp.17-23 (1997).CrossRefGoogle Scholar
  11. Church, R. L. and C. Revelle, The Maximal Covering Location Problem, Papers of the Regional Science Association, Vol. 32 , pp. 101-118 (1974).CrossRefGoogle Scholar
  12. Cooke, K. and E. Halsey, The Shortest Route Through a Network with Time-Dependent Internodal Transit Times, Journal of Mathematical Analysis and Applications, Vol. 14, pp. 493-498 (1966).CrossRefGoogle Scholar
  13. Cragg, C. A., and M. J. Demetsky, Final Report: Simulation Analysis of Route Diversion Strategies for Freeway Iincident Management, VTRC 95-R11, Traffic Research Advisory Committee, FHWA, USDOT (1995).Google Scholar
  14. Daskin, M., A Maximum Expected Covering Location Model Formulation, Properties and Heuristic Solution. Transportation Science, Vol. 17, 48-70 (1983)Google Scholar
  15. Dijkstra, E. W., A Note on Two Problems in Connexion with Graphs, Numerische Mathematik, 1, pp. 269-271 (1959).CrossRefGoogle Scholar
  16. Eldor, M., Demand predictors for computerized freeway control systems”, Proceedings of the 7th International Symposium on Transportation and Traffic Theory, Kyoto, Japan, pp. 341-358 (1977).Google Scholar
  17. Fisher, M. L., An Applications Oriented Guide to Lagrangian Relaxation, Interface, Vol. 15, pp. 10-21 (1985).Google Scholar
  18. Fisher, M.L., R. Jaikumar and L.N. Wassenhove, A Multiplier Adjustment Method for the Generalized Assignment Problems”. Management Science 32, 1986, pp. 1095-1103 (1986).Google Scholar
  19. Fitzsimmons, J., A Methodology for Emergency Ambulance Deployment, Management Science, Vol. 19, No. 6, pp. 627-636 (1973).Google Scholar
  20. Gafarian, A.V., J. Paul, and T. L. Ward, Discrete Time Series Models of a Freeway Density Process, Proceedings of the 7th International Symposium on Transportation and Traffic Theory, Kyoto, Japan, pp.387-411 (1977).Google Scholar
  21. Gendreau, M, G. Laporte and F., Semet, A Dynamic Model and Parallel Tabu Search Heuristic for Real-Time Ambulance Relocation, Parallel Comput, 27, pp. 1641–1653 (2001).CrossRefGoogle Scholar
  22. Gendreau, M, G. Laporte, and F. Semet, The Maximal Expected Coverage Relocation Problem for Emergency Vehicles, Journal of Operation Research Society, Vol. 57, (1), pp. 22-28 (2005).CrossRefGoogle Scholar
  23. Goldberg, J., Dietrich, R., Chen, J., M. Mitwasi, Validating and Applying a Model for Locating Emergency Medical Vehicles in Tucson, AZ, European Journal of Operational Research, No.49, pp. 308-324 (1990).CrossRefGoogle Scholar
  24. Goldberg, J. and F. Szidarovszky, Method for Solving Nonlinear Equations Used in Evaluating Emergency Vehicle Busy Probabilities, Operations Research, Vol. 39, pp. 903-916 (1991a).CrossRefGoogle Scholar
  25. Goldberg, J., and L. Paz, Locating Emergency Vehicle Bases when Service Time Depends on Call Location, Transportation Science, Vol. 25, No.4, pp. 264-280 (1991b).Google Scholar
  26. Haghani, A., H. Hu, and Q. Tian, An Optimization Model for Real-Time Emergency Vehicle Dispatching and Routing, Proceeding CD of the 82 nd annual meeting of the Transportation Research Board, Washington, D.C., 2003.Google Scholar
  27. Hakimi, S., Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph, Operations Research 12, pp. 450-459 (1964).Google Scholar
  28. Hall, R., The Fastest Path through a Network with Random Time-Dependent Travel Times”, Transportation Science, Vol. 20, No. 3, pp. 182-188 (1986).Google Scholar
  29. Hoffman, C. and Janko, J., Travel Time as a Basis of the LISB Guidance Strategy, Proceedings of IEEE Road Traffic Control Conference, IEEE, New York, pp. 6-10 (1988).Google Scholar
  30. Hogan, K. and C. ReVelle, Concepts and Applications of Backup Coverage, Management Science, Vol. 32, pp. 1434-1444 (1986).Google Scholar
  31. Huisken, G., Soft-Computing Techniques Applied to Short-term Traffic Flow Forecasting, Systems Analysis Modeling Simulation, Vol.43-2, pp. 165-173 (2003).CrossRefGoogle Scholar
  32. Ignall, E.D., P. Kolesar, and W.E. Walker, Using Simulation To Develop and Validate Analytic Models: Some Case Studies, Operations Research, Vol. 26, No. 2, pp. 237-253 (1978).Google Scholar
  33. Kaysi, I., M. Ben-Akiva and H. Koutsopulos, An Integrated Approach to Vehicle Routing and Congestion Prediction for Real-Time Driver Guidance, Transportation Research Record, Vol. 1408, pp. 66-74 (1993).Google Scholar
  34. Kolesar, P., W. E. Walker, J. Hausner, Determining the Relation between Fire Engine Travel Times and Travel Distances in New York City Companies, Oper. Res., 23(4), pp. 614–627 (1975a).Google Scholar
  35. Larson, R., A Hypercube Queuing Model for Facility Location and Redistricting in Urban Emergency Services, Comput. & Ops. Res., Vol. 1, pp. 67-95 (1974).CrossRefGoogle Scholar
  36. Larson, R., Approximating the Performance of Urban Emergency Service Systems, Operations Research, Vol.23, No.5, pp. 845-868 (1975).Google Scholar
  37. Lorena, L.A.N. and M.G. Narciso, Relaxation Heuristics for a Generalized Assignment Problem, European Journal of Operational Research, Vol. 19, No. 3, pp. 600-610 (1996).CrossRefGoogle Scholar
  38. Lorena, L., M. G. Narciso, J. E. Beasley (2002); A Constructive Genetic Algorithm for the Generalized Assignment Problem,∼ lorena/gap/CGA-PGA-2000.pdfGoogle Scholar
  39. Marinov, V. and C. Revelle, Siting Emergency Services, in Facility Location: A Survey of articles, applications and methods, edited by: Drezner, Z, Springer Series in Operations Research, pp. 199-222 (1995).Google Scholar
  40. Martello, S., W. R. Pulleyblank, P. Toth, and D. de Werra, Balanced Optimization Problems, Operations Research Letters 3, pp.275-278 (1984).CrossRefGoogle Scholar
  41. Nahi, N.E., Freeway Ttraffic Data Processing, Proceedings of the IEEE, 61, No. 5, pp. 537-541 (1973).CrossRefGoogle Scholar
  42. Narciso, M.G. and L.A.N. Lorena, “Lagrangian/surrogate relaxation for generalized assignment problems”, European Journal of Operational Research, Vol. 114, No. 1, pp. 165-177 (1999).CrossRefGoogle Scholar
  43. Nicholson H. and C. D. Swann, The Prediction of Traffic Flow Volumes Based on Spectral Analysis, Transportation Research Record, Vol. 8, pp. 533-538 (1974).Google Scholar
  44. Nulty, W. G. and M. A. Trick, GNO/PC Generalized Network Optimization System, O.R. Letters, Vol. 2, pp. 101-112 (1988).CrossRefGoogle Scholar
  45. ReVelle, C., and K. Hogan., A Reliability-Constrained Siting Model with Local Estimates of Busy Fractions, Environment and Planning B: Planning and Design, 15, pp. 143-152 (1988).CrossRefGoogle Scholar
  46. Revelle, C., Extension and Prediction in Emergency Service Siting Models, European Journal of Operational Research, Vol. 40, pp. 58-69 (1989).CrossRefGoogle Scholar
  47. Revelle, C. , “A Perspective on Location Science, Location Science, 5, No.1” pp. 3-13 (1997).CrossRefGoogle Scholar
  48. Ross, G. T. and M. S. Soland, A Branch and Bound Algorithm for the Generalized Assignment Problem, Mathematical Programming 8, pp. 91-103 (1975).CrossRefGoogle Scholar
  49. Savas, E.S., Simulation and Cost-Effectiveness Analysis of New York’s Emergency Ambulance Service. Management Science, Vol. 15, No. 12, pp. 608-627 (1969).Google Scholar
  50. Schilling, D. A., D. Elzinga, J. Cohon, R. Church and C. Revelle, The TEAM/FLEET Mmodels for Simultaneous Facility and Equipment Siting, Transportation Science, 13(2), pp. 163-175 (1979).Google Scholar
  51. Schilling, D. A., J. Vaidyanathan and R. Barkhi, A Review of Covering Problems in Facility Location, Location Science, Vol. 1, pp. 25-55 (1993).Google Scholar
  52. Shantikumar, J.G., and R.G. Sargent, A Unifying View of Hhybrid Simulation/Analytic Models and Modeling, Operations Research, Vol. 31, No. 6, pp. 1030-1052 (1983).Google Scholar
  53. Smith, B., Demetsky, M., Short-Term Traffic Flow Prediction: Neural Network Approach, Transportation Research Record, No. 1453, pp. 98-104 (1995).Google Scholar
  54. Stephanedes, Y. J., P.G. Michalopoulos, and R.A. Plum, Improved Estimation of Traffice Flow for Real-Time Control, Transportation Research Record, Vol. 795, pp. 28-39 (1981).Google Scholar
  55. Toregas, C., Swain, R., ReVelle, and C. Bergman, L., The Location of Emergency Service Facilities, Operations Research, Vol. 19-6, pp. 1363-1373 (1971).Google Scholar
  56. Toregas, C., Swain, R., ReVelle, C. and Bergman, L., Reply to Rao’s Note on the Location of Emergency Service Facilities, Operations Research, Vol. 22-6, pp. 1262-1267 (1974).Google Scholar
  57. Trick, M. A., A Linear Relaxation Heuristic for the Generalized Assignment Problem, Naval Research Logistics, Vol. 39, pp. 137-152 (1992).CrossRefGoogle Scholar
  58. Yang, Saini, Masoud Hamedi and Ali Haghani, An On-line Emergency Vehicle Dispatching and Routing Model with Area Coverage Constraints, Transportation Research Record, No. 1923, pp. 1-9 (2006).Google Scholar
  59. Ziliaskopoulos, A., and H. Mahmassani, Time Dependent, Shortest-Path Algorithm for Real-Time Intelligent Vehicle Highway System Applications, Transportation Research Record 1408, pp. 94-100 (1993).Google Scholar
  60. Zografos, K., Douligeris, C. and C. Lin, A Model for the Optimum Deployment of Emergency Repair Trucks: An Application in the Electric Utility Industry, Transportation Research Record, 1358, pp. 88-94 (1992).Google Scholar
  61. Zografos, K., Douligeris, C., and C. Lin, A Simulation Model for Evaluating the Performance of an Emergency Response Fleet, Transportation Research Record, 1452, pp. 27-34 (1994)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Ali Haghani
    • 1
  • Saini Yang
    • 1
  1. 1.Department of Civil and Environmental EngineeringUniversity of MarylandCollege ParkMaryland

Personalised recommendations