It’s National Emergency Medical Services Week #EMSweek2019. Check out my papers and presentations about EMS systems.

This week is National Emergency Medical Services Week. I’ve published and spoken extensively about my research on emergency medical services.

Some blog posts about EMS include:

• My keynote at the 4th International Workshop on Planning of Emergency Services in Delft
• Punk Rock OR goes to Oberlin College
• a journey to the German OR Society Conference
• Resilience Analytics at the University of Oklahoma

Mike Trick wrote a post about my semi-plenary talk at the 2014 German OR Society conference entitled “Using analytics for emergency response”

Papers include:

1. McLay, L.A., A Maximum Expected Covering Location Model with Two Types of Servers, IIE Transactions 41(8), 730 – 741.
2. McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance Measures. Health Care Management Science 13(2), 124 – 136.
3. McLay, L.A., Mayorga, M.E., 2011. Evaluating the Impact of Performance Goals on Dispatching Decisions in Emergency Medical Service. IIE Transactions on Healthcare Service Engineering 1, 185 – 196.
4. Chanta, S., Mayorga, M.E., Kurz, M.E., McLay, L.A., 2011. The minimum p-envy location problem: A new model for equitable distribution of emergency resources. IIE Transactions on Healthcare Systems Engineering 1(2), 101 – 115.
5. McLay, L.A., Moore, H. 2012. Hanover County Improves Its Response to Emergency Medical 911 Calls. Interfaces 42(4), 380-394.
6. Bandar, D., Mayorga, M.E., McLay, L.A., 2012. Optimal Dispatching Strategies for Emergency Vehicles to Increase Patient Survivability. International Journal of Operational Research.
7. McLay, L.A., Brooks, J.P., Boone, E.L., 2012. Analyzing the Volume and Nature of Emergency Medical Calls during Severe Weather Events using Regression Methodologies. Socio-Economic Planning Sciences 46, 55 – 66.
8. Kunkel, A., McLay, L.A. 2013. Determining minimum staffing levels during snowstorms using an integrated simulation, regression, and reliability model. Health Care Management Science 16(1), 14 – 26.
9. McLay, L.A., Mayorga, M.E., 2013. A model for optimally dispatching ambulances to emergency calls with classification errors in patient priorities. IIE Transactions 45(1), 1—24. This paper was selected as a Best Paper Award for IIE Transactions Focused Issue on Scheduling and Logistics.
10. Mayorga, M.E., Bandara, D., McLay, L.A., 2013. Districting and dispatching policies for emergency medical service systems to improve patient survival, IIE Transactions on Healthcare Systems Engineering 3(1), 39 – 56.
11. Toro-Diaz, 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.
12. Dreiding, R.A., McLay, L.A., An Integrated Screening Model for Screening Cargo Containers for Nuclear Weapons. European Journal of Operational Research 230, 181 – 189.
13. Chanta, S., Mayorga, M. E., McLay, L. A., 2014. Improving Rural Emergency Services without Sacrificing Coverage: A Bi-Objective Covering Location Model for EMS Systems. Annals of Operations Research 221(1), 133 – 159.
14. Sudtachat, K., Mayorga, M.E., McLay, L.A. 2014. Recommendations for Dispatching Emergency Vehicles under Multi-tiered Response via Simulation. International Transactions in Operational Research 21(4), 581-617.
15. Chanta, S., Mayorga, M.E., McLay, L.A., 2014. The minimum p-envy problem with requirement on minimum survival rate. Computers & Industrial Engineering 74, 228 – 239.
16. Bandara, D., Mayorga, M.E., McLay, L.A., 2014. Priority Dispatching Strategies for EMS Systems, The Journal of the Operational Research Society 65, 572 – 587.
17. Grannan, B.C., Bastian, N., McLay, L.A. A Maximum Expected Covering Problem for Locating and Dispatching Two Classes of Military Medical Evacuation Air Assets. Operations Research Letters 9, 1511-1531.
18. Toro-Diaz, H., Mayorga, M.E., McLay, L.A., Rajagopalan, H., Saydem, C., Reducing disparities in large scale emergency medical service systems, Journal of the Operational Research Society 66, 1169-1181. doi:10.1057/jors.2014.83
19. McLay, L.A., Mayorga, M.E., 2013. A dispatching model for server-to-customer systems that balances efficiency and equity. Manufacturing & Service Operations Management 15(2), 205 – 200.
20. Ansari, S., McLay, L.A., Mayorga, M.E., 2015. A Maximum Expected Covering Problem for District Design, Transportation Science 51(1), 376 – 390.
21. Sudtachat, K., Mayorga, M.E., McLay, L.A., 2016. A Nested-Compliance Table Policy for Emergency Medical Service Systems under Relocation. OMEGA 58, 154 – 168.
22. Ansari, S., Yoon, S., Albert, L. 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. 103, 143 – 157. DOI: 1016/j.tre.2017.04.013.
23. Yoon, S., Albert, L. An Expected Coverage Model with a Cutoff Priority Queue. Health Care Management Science 21(4), 517 – 533. DOI: https://doi.org/10.1007/s10729-017-9409-3.
24. Enayati, S., Mayorga, M., Toro-Diaz, H., Albert, L. 2018. Identifying trade-offs in equity and efficiency for simultaneously optimizing location and multi-priority dispatch of ambulances. International Transactions in Operational Research 26, 415 – 438. DOI:1111/itor.12590
25. Yoon, S. and Albert, L.A., 2018. Dynamic Resource Assignment for Emergency Response with Multiple Types of Vehicles, Submitted to Operations Research, October 2018.
26. Yoon, S., and Albert, L.A. A dynamic ambulance routing model with multiple response. Submitted to Transportation Research Part E: Logistics and Transportation Review, January 2019.