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”
- McLay, L.A., A Maximum Expected Covering Location Model with Two Types of Servers, IIE Transactions 41(8), 730 – 741.
- McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical Service Performance Measures. Health Care Management Science 13(2), 124 – 136.
- 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.
- 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.
- McLay, L.A., Moore, H. 2012. Hanover County Improves Its Response to Emergency Medical 911 Calls. Interfaces 42(4), 380-394.
- Bandar, D., Mayorga, M.E., McLay, L.A., 2012. Optimal Dispatching Strategies for Emergency Vehicles to Increase Patient Survivability. International Journal of Operational Research.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- Ansari, S., McLay, L.A., Mayorga, M.E., 2015. A Maximum Expected Covering Problem for District Design, Transportation Science 51(1), 376 – 390.
- 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.
- 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.
- 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.
- 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
- Yoon, S. and Albert, L.A., 2018. Dynamic Resource Assignment for Emergency Response with Multiple Types of Vehicles, Submitted to Operations Research, October 2018.
- 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.