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Scenario-based approach for the ambulance location problem with stochastic call arrivals under a dispatching policy

An Erratum to this article was published on 13 February 2017

This article has been updated

Abstract

This paper proposes a scenario-based ambulance location model which explicitly computes the availability of ambulances with stochastic call arrivals under a dispatching policy. The model utilizes two-stage stochastic programming to represent the temporal variations in call arrivals as a set of call arrival sequences. Constraints are embedded in the model to ensure that available ambulances are assigned to incoming calls following a dispatching policy. A logic-based Benders decomposition algorithm is presented to solve the model. The advantage of using our algorithm is demonstrated by comparing its performance with those of other location models.

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Change history

  • 13 February 2017

    An erratum to this article has been published.

Notes

  1. 1.

    In a strict sense, the model of Carson and Batta (1990) is not a scenario planning model because the location decisions for each scenario are not linked.

  2. 2.

    Note that unlike a probabilistic location model our model does not utilize the busy fraction to compute a location solution. They are only implicit in the model.

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Acknowledgements

This research was supported by a Grant “Research and development of modeling and simulating the rescues, the transfer, and the treatment of disaster victims” [MPSS-SD-2013-36] through the Disaster and Safety Management Institute funded by Ministry of Public Safety and Security of Korean government.

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Correspondence to Taesik Lee.

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The original version of this article was revised: Algorithm 1 had been missing in the article. This has been corrected in this version.

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Sung, I., Lee, T. Scenario-based approach for the ambulance location problem with stochastic call arrivals under a dispatching policy. Flex Serv Manuf J 30, 153–170 (2018). https://doi.org/10.1007/s10696-016-9271-5

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Keywords

  • Ambulance location problem
  • Stochastic programming
  • Stochastic call arrivals
  • Dispatching policy
  • Logic-based Benders decomposition