Gizem MullaoğluGorkem SariyerSariyer, GorkemMullaoglu, Gizem2025-10-06201921681015, 216810232168-10152168-102310.1080/21681015.2019.17011072-s2.0-85076877835https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076877835&doi=10.1080%2F21681015.2019.1701107&partnerID=40&md5=1eb578549d23cac0cdaabee6a445614ehttps://gcris.yasar.edu.tr/handle/123456789/9343https://doi.org/10.1080/21681015.2019.1701107In Emergency Medical Services (EMS) ability to respond to emergency situations rapidly is very crucial. One of the main decisions that need to be made by the EMS provider is to locate available ambulances in an efficient way that all possible demand can be covered at a reasonable amount of time. For the ambulance location problem we propose three new optimization models employing Double Standard Model as a base. We make an elaborate analysis to decide on the minimum number of ambulances required in each location. Using real-life data from İzmir Turkey proposed optimization models are solved for both the population and the call volume data. Consequently this study mainly showed that in solving ambulance location problem the use of call volume data and preparing periodical plans are more efficient than using either population data or preparing plans based on daily call volume data. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessAmbulance Location, Double-coverage Models, Emergency Medical Services, Mathematical Modeling, Ambulances, Emergency Services, Location, Mathematical Models, Optimization, Coverage Models, Emergency Medical Services, Emergency Situation, Location Modeling, Location Problems, Optimization Models, Population Data, Standard Model, Population StatisticsAmbulances, Emergency services, Location, Mathematical models, Optimization, Coverage models, Emergency medical services, Emergency situation, Location modeling, Location problems, Optimization models, Population data, Standard model, Population statisticsEmergency Medical ServicesMathematical ModelingAmbulance LocationDouble-Coverage ModelsVariations of Double Coverage Ambulance Location Model Using Call Volume and Population DataArticle