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Dynamic Repositioning of Indistinguishable Service Units on Transportation Networks

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  • Oded Berman

    (The University of Calgary, Calgary, Alberta)

Abstract

The repositioning of urban emergency units is examined analytically using the methodology of Markovian Decision Processes. In practice urban emergency vehicles (e.g., fire engines) are constantly subject to repositioning in order to assure a proper posture for responding quickly to future demands. The analysis assumes that there are q indistinguishable servers on the network located initially at q nodes---“The Home Locations.” Depending on the status of other server locations, any available server can be moved to any other vacant location in the network. The states of the system are defined according to the status of each location (occupied or vacant). The policy space consists of decisions on where and when to move service units for any possible state. The objective is to minimize the long term expected cost (in time units) of operating the system.

Suggested Citation

  • Oded Berman, 1981. "Dynamic Repositioning of Indistinguishable Service Units on Transportation Networks," Transportation Science, INFORMS, vol. 15(2), pages 115-136, May.
    • Handle: RePEc:inm:ortrsc:v:15:y:1981:i:2:p:115-136
    DOI: 10.1287/trsc.15.2.115
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    Cited by:

    1. van Barneveld, Thije & Jagtenberg, Caroline & Bhulai, Sandjai & van der Mei, Rob, 2018. "Real-time ambulance relocation: Assessing real-time redeployment strategies for ambulance relocation," Socio-Economic Planning Sciences, Elsevier, vol. 62(C), pages 129-142.
    2. Matthew S. Maxwell & Eric Cao Ni & Chaoxu Tong & Shane G. Henderson & Huseyin Topaloglu & Susan R. Hunter, 2014. "A Bound on the Performance of an Optimal Ambulance Redeployment Policy," Operations Research, INFORMS, vol. 62(5), pages 1014-1027, October.
    3. Amir Ali Nasrollahzadeh & Amin Khademi & Maria E. Mayorga, 2018. "Real-Time Ambulance Dispatching and Relocation," Manufacturing & Service Operations Management, INFORMS, vol. 20(3), pages 467-480, July.
    4. Sudtachat, Kanchala & Mayorga, Maria E. & Mclay, Laura A., 2016. "A nested-compliance table policy for emergency medical service systems under relocation," Omega, Elsevier, vol. 58(C), pages 154-168.
    5. Saif Benjaafar & Daniel Jiang & Xiang Li & Xiaobo Li, 2022. "Dynamic Inventory Repositioning in On-Demand Rental Networks," Management Science, INFORMS, vol. 68(11), pages 7861-7878, November.
    6. Kenneth C. Chong & Shane G. Henderson & Mark E. Lewis, 2016. "The Vehicle Mix Decision in Emergency Medical Service Systems," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 347-360, July.
    7. Matthew S. Maxwell & Mateo Restrepo & Shane G. Henderson & Huseyin Topaloglu, 2010. "Approximate Dynamic Programming for Ambulance Redeployment," INFORMS Journal on Computing, INFORMS, vol. 22(2), pages 266-281, May.
    8. Xueping Li & Zhaoxia Zhao & Xiaoyan Zhu & Tami Wyatt, 2011. "Covering models and optimization techniques for emergency response facility location and planning: a review," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 74(3), pages 281-310, December.
    9. Li, Mengyu & Carter, Alix & Goldstein, Judah & Hawco, Terence & Jensen, Jan & Vanberkel, Peter, 2021. "Determining ambulance destinations when facing offload delays using a Markov decision process," Omega, Elsevier, vol. 101(C).

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