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A multiple dispatch and partial backup hypercube queuing model to analyze emergency medical systems on highways

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  • Iannoni, Ana Paula
  • Morabito, Reinaldo

Abstract

The hypercube is a spatially distributed queuing model based on Markovian analysis approximations, used to analyze the configuration and operation of server-to-customer emergency systems. In the present study we adapted the model to analyze emergency medical systems (EMS) on highways, which operate within particular dispatching policies. The study takes into consideration that: the emergency calls are of different types; the servers are distinct (e.g., rescue ambulances, medical vehicles); only certain servers in the system can service calls in a given region (partial backup); and, depending on the type of call, one or more identical or distinct servers are immediately dispatched to service such calls (multiple dispatch). We also consider that the arriving calls take place either along the highway or at the home location of a server - in which case the server does not need to travel to the call location. Finally, we analyzed the computational results of applying such an approach to the case study of an EMS operating on Brazilian highways.

Suggested Citation

  • Iannoni, Ana Paula & Morabito, Reinaldo, 2007. "A multiple dispatch and partial backup hypercube queuing model to analyze emergency medical systems on highways," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(6), pages 755-771, November.
    • Handle: RePEc:eee:transe:v:43:y:2007:i:6:p:755-771
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    Citations

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    Cited by:

    1. Iannoni, Ana Paula & Chiyoshi, Fernando & Morabito, Reinaldo, 2015. "A spatially distributed queuing model considering dispatching policies with server reservation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 49-66.
    2. Liu, Han & Hua, Cheng & Lei, Chao, 2021. "Planning for time-varying volunteer firefighter systems under probabilistic service disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    3. Zhang, Sizhe & Cardin, Michel-Alexandre, 2017. "Flexibility and real options analysis in emergency medical services systems using decision rules and multi-stage stochastic programming," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 107(C), pages 120-140.
    4. Junhu Ruan & Felix T. S. Chan & Xiaofeng Zhao, 2018. "Re-Planning the Intermodal Transportation of Emergency Medical Supplies with Updated Transfer Centers," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    5. Boyacı, Burak & Geroliminis, Nikolas, 2015. "Approximation methods for large-scale spatial queueing systems," Transportation Research Part B: Methodological, Elsevier, vol. 74(C), pages 151-181.
    6. Iannoni, Ana Paula & Morabito, Reinaldo & Saydam, Cem, 2011. "Optimizing large-scale emergency medical system operations on highways using the hypercube queuing model," Socio-Economic Planning Sciences, Elsevier, vol. 45(3), pages 105-117, September.
    7. Ana Iannoni & Reinaldo Morabito & Cem Saydam, 2008. "A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways," Annals of Operations Research, Springer, vol. 157(1), pages 207-224, January.
    8. Iannoni, Ana P. & Morabito, Reinaldo, 2023. "A review on hypercube queuing model's extensions for practical applications," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    9. Akbar Karimi & Michel Gendreau & Vedat Verter, 2018. "Performance Approximation of Emergency Service Systems with Priorities and Partial Backups," Transportation Science, INFORMS, vol. 52(5), pages 1235-1252, October.
    10. 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.
    11. Atkinson, J.B. & Kovalenko, I.N. & Kuznetsov, N. & Mykhalevych, K.V., 2008. "A hypercube queueing loss model with customer-dependent service rates," European Journal of Operational Research, Elsevier, vol. 191(1), pages 223-239, November.
    12. Soovin Yoon & Laura A. Albert, 2018. "An expected coverage model with a cutoff priority queue," Health Care Management Science, Springer, vol. 21(4), pages 517-533, December.
    13. Iannoni, Ana Paula & Morabito, Reinaldo & Saydam, Cem, 2009. "An optimization approach for ambulance location and the districting of the response segments on highways," European Journal of Operational Research, Elsevier, vol. 195(2), pages 528-542, June.
    14. Erdemir, Elif Tokar & Batta, Rajan & Rogerson, Peter A. & Blatt, Alan & Flanigan, Marie, 2010. "Joint ground and air emergency medical services coverage models: A greedy heuristic solution approach," European Journal of Operational Research, Elsevier, vol. 207(2), pages 736-749, December.
    15. Ansari, Sardar & Yoon, Soovin & Albert, Laura A., 2017. "An approximate hypercube model for public service systems with co-located servers and multiple response," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 143-157.
    16. N C Simpson & P G Hancock, 2009. "Fifty years of operational research and emergency response," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 126-139, May.
    17. Geroliminis, Nikolas & Kepaptsoglou, Konstantinos & Karlaftis, Matthew G., 2011. "A hybrid hypercube - Genetic algorithm approach for deploying many emergency response mobile units in an urban network," European Journal of Operational Research, Elsevier, vol. 210(2), pages 287-300, April.
    18. Cheng, Yung-Hsiang & Liang, Zheng-Xian, 2014. "A strategic planning model for the railway system accident rescue problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 75-96.
    19. Kundu, Tanmoy & Sheu, Jiuh-Biing & Kuo, Hsin-Tsz, 2022. "Emergency logistics management—Review and propositions for future research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    20. Shariat-Mohaymany, Afshin & Babaei, Mohsen & Moadi, Saeed & Amiripour, Sayyed Mahdi, 2012. "Linear upper-bound unavailability set covering models for locating ambulances: Application to Tehran rural roads," European Journal of Operational Research, Elsevier, vol. 221(1), pages 263-272.
    21. de Souza, Regiane Máximo & Morabito, Reinaldo & Chiyoshi, Fernando Y. & Iannoni, Ana Paula, 2015. "Incorporating priorities for waiting customers in the hypercube queuing model with application to an emergency medical service system in Brazil," European Journal of Operational Research, Elsevier, vol. 242(1), pages 274-285.
    22. Sardar Ansari & Laura Albert McLay & Maria E. Mayorga, 2017. "A Maximum Expected Covering Problem for District Design," Transportation Science, INFORMS, vol. 51(1), pages 376-390, February.
    23. Su, Qiang & Luo, Qinyi & Huang, Samuel H., 2015. "Cost-effective analyses for emergency medical services deployment: A case study in Shanghai," International Journal of Production Economics, Elsevier, vol. 163(C), pages 112-123.
    24. Doan, Xuan Vinh & Shaw, Duncan, 2019. "Resource allocation when planning for simultaneous disasters," European Journal of Operational Research, Elsevier, vol. 274(2), pages 687-709.
    25. Caio Vitor Beojone & Regiane Máximo de Souza & Ana Paula Iannoni, 2021. "An Efficient Exact Hypercube Model with Fully Dedicated Servers," Transportation Science, INFORMS, vol. 55(1), pages 222-237, 1-2.

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