214
Views
1
CrossRef citations to date
0
Altmetric
Research Article

A mixed-integer programming model for identifying intuitive ambulance dispatching policies

Pages 2300-2311 | Received 25 Jan 2022, Accepted 13 Oct 2022, Published online: 04 Nov 2022

References

  • Alanis, R., Ingolfsson, A., & Kolfal, B. (2013). A Markov chain model for an EMS system with repositioning. Production and Operations Management, 22(1), 216–231. https://doi.org/10.1111/j.1937-5956.2012.01362.x
  • Altman, E. (1999). Constrained Markov decision processes. Chapman & Hall/CRC.
  • Ansari, S., McLay, L. A., & Mayorga, M. E. (2017). A maximum expected covering problem for district design. Transportation Science, 51(1), 376–390. https://doi.org/10.1287/trsc.2015.0610
  • Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible ai. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
  • Bandara, D., Mayorga, M. E., & McLay, L. A. (2014). Priority dispatching strategies for ems systems. Journal of the Operational Research Society, 65(4), 572–587. https://doi.org/10.1057/jors.2013.95
  • Bélanger, V., Lanzarone, E., Nicoletta, V., Ruiz, A., & Soriano, P. (2020). A recursive simulation-optimization framework for the ambulance location and dispatching problem. European Journal of Operational Research, 286(2), 713–725. https://doi.org/10.1016/j.ejor.2020.03.041
  • Budge, S., Ingolfsson, A., & Erkut, E. (2009). Approximating vehicle dispatch probabilities for emergency service systems with location-specific service times and multiple units per location. Operations Research, 57(1), 251–255. https://doi.org/10.1287/opre.1080.0591
  • Dimitrov, N. B., Michalopoulos, D. P., Morton, D. P., Nehme, M. V., Pan, F., Popova, E., Schneider, E. A., & Thoreson, G. G. (2011). Physics-based detection probability calculations. Annals of Operations Research, 187(1), 207–228. https://doi.org/10.1007/s10479-009-0677-2
  • Dimitrov, N. B., & Morton, D. P. (2009). Combinatorial design of a stochastic Markov decision process. In J. W. Chinneck, B. Kristjansson, & M. J. Saltzman, M.J. (eds.), Operations research and cyber-infrastructure (pp. 167–193). Springer.
  • DuBois, E., & Albert, L. A. (2021). Dispatching policies during prolonged mass casualty incidents. Journal of the Operational Research Society, 1–15. https://doi.org/10.1080/01605682.2021.1999181
  • Erkut, E., Ingolfsson, A., & Erdoğan, G. (2008). Ambulance location for maximum survival. Naval Research Logistics (NRL), 55(1), 42–58. https://doi.org/10.1002/nav.20267
  • Gendreau, M., Laporte, G., & Semet, F. (2006). The maximal expected coverage relocation problem for emergency vehicles. Journal of the Operational Research Society, 57(1), 22–28. https://doi.org/10.1057/palgrave.jors.2601991
  • Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine, 38(3), 50–57. https://doi.org/10.1609/aimag.v38i3.2741
  • Jagtenberg, C. J., Bhulai, S., & van der Mei, R. D. (2017). Dynamic ambulance dispatching: Is the closest-idle policy always optimal? Health Care Management Science, 20(4), 517–531. https://doi.org/10.1007/s10729-016-9368-0
  • Jarvis, J. P. (1985). Approximating the equilibrium behavior of multi-server loss systems. Management Science, 31(2), 235–239. https://doi.org/10.1287/mnsc.31.2.235
  • Jenkins, P. R., Robbins, M. J., & Lunday, B. J. (2021). Approximate dynamic programming for military medical evacuation dispatching policies. INFORMS Journal on Computing, 33(1), 2–26. https://doi.org/10.1287/ijoc.2019.0930
  • Larson, R. C. (1974). A hypercube queuing model for facility location and redistricting in urban emergency services. Computers & Operations Research, 1(1), 67–95. https://doi.org/10.1016/0305-0548(74)90076-8
  • Levine, E. S., Tisch, J., Tasso, A., & Joy, M. (2017). The New York City police department’s domain awareness system. Interfaces, 47(1), 70–84. https://doi.org/10.1287/inte.2016.0860
  • McLay, L. A., & Mayorga, M. E. (2013a). A model for optimally dispatching ambulances to emergency calls with classification errors in patient priorities. IIE Transactions, 45(1), 1–24. https://doi.org/10.1080/0740817X.2012.665200
  • McLay, L. A., & Mayorga, M. E. (2013b). A dispatching model for server-to-customer systems that balances efficiency and equity. Manufacturing & Service Operations Management, 15(2), 205–220. https://doi.org/10.1287/msom.1120.0411
  • Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences of the United States of America, 116(44), 22071–22080. https://doi.org/10.1073/pnas.1900654116
  • Puterman, M. L. (1994). Markov decision processes: Discrete stochastic dynamic programming. John Wiley & Sons, Inc.
  • Rautenstrauss, M., Martin, L., & Minner, S. (2023). Ambulance dispatching during a pandemic: Tradeoffs of categorizing patients and allocating ambulances. European Journal of Operational Research, to Appear, 304(1), 239–254. https://doi.org/10.1016/j.ejor.2021.11.051
  • Robbins, M. J., Jenkins, P. R., Bastian, N. D., & Lunday, B. J. (2020). Approximate dynamic programming for the aeromedical evacuation dispatching problem: Value function approximation utilizing multiple level aggregation. Omega, 91(102020), 102020. https://doi.org/10.1016/j.omega.2018.12.009
  • Schmid, V. (2012). Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. European Journal of Operational Research, 219(3), 611–621. https://doi.org/10.1016/j.ejor.2011.10.043
  • Silva, F., & Serra, D. (2008). Locating emergency services with different priorities: The priority queuing covering location problem. Journal of the Operational Research Society, 59(9), 1229–1238. https://doi.org/10.1057/palgrave.jors.2602473
  • Toro-Díaz, H., Mayorga, M. E., Chanta, S., & McLay, L. A. (2013). Joint location and dispatching decisions for emergency medical services. Computers & Industrial Engineering, 64(4), 917–928. https://doi.org/10.1016/j.cie.2013.01.002
  • Toro-Díaz, H., Mayorga, M. E., McLay, L. A., Rajagopalan, H. K., & Saydam, C. (2015). Reducing disparities in large-scale emergency medical service systems. Journal of the Operational Research Society, 66(7), 1169–1181. https://doi.org/10.1057/jors.2014.83
  • Van Barneveld, T. C., van der Mei, R. D., & Bhulai, S. (2017). Compliance tables for an ems system with two types of medical response units. Computers & Operations Research, 80, 68–81. https://doi.org/10.1016/j.cor.2016.11.013
  • Yoon, S., & Albert, L. A. (2018). An expected coverage model with a cutoff priority queue. Health Care Management Science, 21(4), 517–533. https://doi.org/10.1007/s10729-017-9409-3
  • Yoon, S., & Albert, L. A. (2021). Dynamic dispatch policies for emergency response with multiple types of vehicles. Transportation Research Part E: Logistics and Transportation Review, 152, 102405. https://doi.org/10.1016/j.tre.2021.102405

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.