Health Systems

, Volume 6, Issue 3, pp 187–208 | Cite as

Logistics for Emergency Medical Service systems

  • Melanie Reuter-OppermannEmail author
  • Pieter L. van den Berg
  • Julie L. Vile
Review Article


Emergency Medical Service (EMS) systems worldwide are complex systems, characterized by significant variation in service providers, care pathways, patient case-mix and quality care indicators. Analysing and improving them is therefore challenging. Since EMS systems differ between countries, it is difficult to provide generic rules and approaches for EMS planning. Nevertheless, the common goal for all service providers is to offer medical assistance to patients with serious injuries or illnesses as quickly as possible. This paper presents an overview of logistical problems arising for EMS providers, demonstrating how some of these problems are related and intertwined. For each individual planning problem, a description as well as a concise literature overview of solution approaches considered is given. A summary table classifies the literature according to the problems addressed and connects it to the proposed taxonomy.


EMS systems emergency and non-emergency services forecasting location problems 



This research was financed in part by EPSRC Grant EP/F033338/1 as part of the LANCS initiative and the Dutch Technology Foundation STW under contract 11986, which we gratefully acknowledge. The authors would like to thank the organizers of the EURO Summer Institute XXXI conference, at which the stimulus of this paper arose, for providing an excellent forum for the authors to discuss and debate new modelling and solution techniques to aid EMS decision makers. They would also like to express their gratitude to each of their University supervisors for their helpful comments and support, as well as each of the ambulance trusts that have actively provided data, comments and advice for each of their related doctoral projects.


  1. Aboueljinane L, Sahin E and Jemai Z (2013) A review on simulation models applied to emergency medical service operations. Computers and Industrial Engineering 66, 734–750.CrossRefGoogle Scholar
  2. Aboueljinane L, Sahin E, Jemai Z and Marty J (2014) A simulation study to improve the performance of an emergency medical service: Application to the French Val-de-Marne department. Simulation Modelling Practice and Theory 47, 46–59.CrossRefGoogle Scholar
  3. Ahmadi-Javid A, Seyedi P and Syam SS (2016) A survey of healthcare facility location. Computers & Operations Research.Google Scholar
  4. Alanis R, Ingolfsson A and Kolfal B (2013) A Markov Chain Model for an EMS System with Repositioning. Production and Operations Management 22(1), 216–231.CrossRefGoogle Scholar
  5. Ambulancezorg Nederland (2013) Ambulances in-zicht 2012. Technical report National Institute for Public Health and the Environment.Google Scholar
  6. Ambulancezorg Nederland (2014) Ambulances in-zicht 2013. Technical report National Institute for Public Health and the Environment.Google Scholar
  7. Andersson, T., & VäRBRAND, P. (2007). Decision support tools for ambulance dispatch and relocation. Journal of the Operational Research Society, 58, 195–201.CrossRefGoogle Scholar
  8. Andrews B and Cunningham S (1995) L. L. Bean improves call-center forecasting. Interfaces 25(6), 1–13.CrossRefGoogle Scholar
  9. Aringhieri R, Bruni M, Khodaparasti S and van Essen J (2017) Emergency medical services and beyond: Addressing new challenges through a wide literature review. Computers & Operations Research 78, 349–368.CrossRefGoogle Scholar
  10. Aringhieri R, Carello G, and Morale D (2007) Ambulance location through optimization and simulation: the case of milano urban area. In: The 38th annual conference of the Italian operations research society optimization and decision sciences, Università degli Studi di Milano, Polo didattico e di ricerca di Crema.Google Scholar
  11. Aringhieri R, Carello G, and Morale D (2013) Supporting decision making to improve the performance of an Italian Emergency Medical Service. Annals of Operations Research pp 1–18.Google Scholar
  12. Baker J and Fitzpatrick K (1986) Determination of an optimal forecast model for ambulance demand using goal programming. Journal of Operational Research Society 37(11), 1047–1059.CrossRefGoogle Scholar
  13. Bandara D, Mayorga EM and Mclay AL (2014). Priority dispatching strategies for ems systems. Journal of the Operational Research Society, 65(4), 572–587.Google Scholar
  14. Basar A, Çatay B and Ünlüyurt T (2012) A taxonomy for emergency service station location problem. Optimization Letters 6, 1147–1160.CrossRefGoogle Scholar
  15. Behrendt H and Schmiedel R (2002) Ermittlung der bedarfsgerechten fahrzeugvorhaltung im rettungsdienst. Notfall und Rettungsmedizin 5(3), 190–203.CrossRefGoogle Scholar
  16. Bell C and Allen D (1969) Optimal planning of an emergency ambulance service. Socio-Economic Planning Science 3, 95–101.CrossRefGoogle Scholar
  17. Beraldi P and Bruni M (2009) A probabilistic model applied to emergency service vehicle location. European Journal of Operational Research 196(1), 323–331.CrossRefGoogle Scholar
  18. Beraldi P, Bruni M and Conforti D (2004) Designing robust emergency medical service via stochastic programming. European Journal of Operational Research 158(1), 183–193.CrossRefGoogle Scholar
  19. Bianci L, Jarrett J and Hanumara C (1993) Forecasting incoming calls to telemarketing centers. Journal of Business Forecasting 12(2), 3–11.Google Scholar
  20. Bjarnason R, Tadepalli P, Fern A and Niedner C (2009) Simulation-based Optimization of Resource Placement and Emergency Response. In: Proceedings of the Twenty-First Innovative Application of Artificial Intelligence Conference pp 47–53.Google Scholar
  21. Bradbeer P, Findlay C and Fogarty TC (2000) An ambulance crew rostering system. In: Real-World Applications of Evolutionary Computing pp 267–279, Springer.Google Scholar
  22. Brotcorne L, Laporte G and Semet F (2003) Ambulance location and relocation models. European Journal of Operational Research 147(3), 451–463.CrossRefGoogle Scholar
  23. Brown LH, Lerner E B, Larmon B, Legassick T and Taigman M (2007) Are ems call volume predictions based on demand pattern analysis accurate? Prehospital Emergency Care 11(2), 199–203.Google Scholar
  24. Carnes TA, Henderson SG, Shmoys DB, Ahghari M, Russell D, Henderson SG, and Shmoys DB (2013) Air-Ambulance Routing at Ornge. Interfaces 43(3), 232–239.Google Scholar
  25. Carter AJ, Gould JB, Vanberkel P, Jensen JL, Cook J, Carrigan S, Wheatley MR and Travers AH (2015) Offload zones to mitigate emergency medical services (ems) offload delay in the emergency department: a process map and hazard analysis. CJEM.Google Scholar
  26. Carter GM, Chaiken JM, Ignall E and Jun NM (1972) Response Areas for Two Emergency Units. Operations Research 20(3), 571–594.Google Scholar
  27. Channouf N, L’Ecuyer P, Ingolfsson A and Avramidis A (2007) The application of forecasting techniques to modelling Emergency Medical System calls in Calgary. Alberta. Health Care Manage Science 10(1), 25–45.CrossRefGoogle Scholar
  28. Chen A, Lu T, Ma M and Sun W (2015) Demand forecast using data analytics for the pre-allocation of ambulances. IEEE Journal of Biomedical and Health Informatics.Google Scholar
  29. Cho SH, Jang H, Lee T, Turner J, and Cho SH (2014) Simultaneous Location of Trauma Centers and Helicopters for Emergency Medical Service Planning Simultaneous Location of Trauma Centers and Helicopters for Emergency Medical Service Planning. Operations Research 62(4), 751–771.Google Scholar
  30. Chong KC, Henderson SG, and Lewis ME (2015) The vehicle mix decision in Emergency Medical Service systems. Manufacturing & Service Operations Management.Google Scholar
  31. Church R andRevelle C (1974) The maximal covering location problem. Papers in Regional Science 32(1), 101–118.CrossRefGoogle Scholar
  32. Clarke O (2015) 40,000 hours of ambulance delays at Welsh hospital A&Es. BBC News Wales.Google Scholar
  33. Cordeau JF and Laporte G (2007) The dial-a-ride problem: models and algorithms. Annals of Operations Research 153(1), 29–46.Google Scholar
  34. Daskin MS (1983) A maximum expected covering location model: Formulation, properties and heuristic solution. Transportation Science 17(1), 48–70.CrossRefGoogle Scholar
  35. De La Mota IF, Garduño AV, and Pérez ES (2015) Simulation and Optimization of the Pre-hospital Care System of the National University of Mexico. In Applied Simulation and Optimization (Mujica Mota M, De La Mota IF and Guimarans Serrano D, Eds), pp 233–276, Springer International Publishing, Cham.Google Scholar
  36. Dean SF (2008) Why the Closest Ambulance Cannot be Dispatched in an Urban Emergency Medical Services System. Prehospital and Disaster Medicine 23(02), 161–165.CrossRefGoogle Scholar
  37. Dick WF (2003) Anglo-american vs. franco-german emergency medical services system. Prehospital and Disaster Medicine 18, 29–37.CrossRefGoogle Scholar
  38. Donabedian A (1980) Definition of Quality and Approaches to its Assessment. Health Administration: Explorations in Quality Assessment and Monitoring.Google Scholar
  39. Dwars RP (2013) Capacity planning of emergency call centers. Master’s thesis VU University Amsterdam.Google Scholar
  40. Dzator M and Dzator J (2013) An effective heuristic for the P-median problem with application to ambulance location. Opsearch 50(1), 60–74.CrossRefGoogle Scholar
  41. Erdemir ET, Batta R, Rogerson PA, Blatt A and Flanigan M (2010) Joint ground and air emergency medical services coverage models: A greedy heuristic solution approach. European Journal of Operational Research 207(2), 736–749.Google Scholar
  42. Erdoğan G, Erkut E, Ingolfsson A and Laporte G (2010) Scheduling ambulance crews for maximum coverage. Journal of the Operational Research Society 61(4), 543–550.CrossRefGoogle Scholar
  43. Erkut E, Ingolfsson A and Erdoğan G (2008) Ambulance location for maximum survival. Naval Research Logistics (NRL) 55(1), 42–58.CrossRefGoogle Scholar
  44. Erkut E, Ingolfsson A, Sim T, Erdoğan G (2009) Computational comparison of five maximal covering models for locating ambulances. Geographical Analyis 41, 43–65.CrossRefGoogle Scholar
  45. Fischer M, Kamp J, Riesgo LGC, Robertson-Steel I, Overton J, Ziemann A, Krafft T, Group E et al. (2011) Comparing emergency medical service systems - a project of the european emergency data (eed) project. Resuscitation 82(3), 285–293.Google Scholar
  46. Fujiwara O, Makjamroen T and Gupta KK (1987) Ambulance deployment analysis: A case study of bangkok. European Journal of Operational Research 31(1), 9–18.CrossRefGoogle Scholar
  47. Furuta T and Tanaka KI (2014) Maximal Covering Location Model for Doctor-Helicopter Systems with Two Types of Coverage Criteria. Urban and Regional Planning Review 1, 39–58.CrossRefGoogle Scholar
  48. Gendreau M, Laporte G and Semet F (1997) Solving an ambulance location model by tabu search. Location Science 5(2), 75–88.CrossRefGoogle Scholar
  49. Gendreau M, Laporte G and Semet F (2001) A dynamic model and parallel tabu search heuristic for real-time ambulance relocation. Parallel Computing 27(12), 1641–1653.CrossRefGoogle Scholar
  50. Gendreau M, Laporte G and Semet F (2006) The maximal expected coverage relocation problem for emergency vehicles. Journal of the Operational Research Society 57(1), 22–28.CrossRefGoogle Scholar
  51. Goldberg J, Dietrich R, Chen JM, Mitwasi M, Valenzuela T and Criss E (1990) A simulation model for evaluating a set of emergency vehicle base locations: Development, validation, and usage. Socio-Economic Planning Sciences 24(2), 125–141.Google Scholar
  52. Haghani A, Tian Q and Hu H (2003) A simulation model for real-time emergency vehicle dispatching and routing. In: CD-ROM, Presented at the 82nd Annual Meeting of the Transportation Research Board, Washington, DC.Google Scholar
  53. Harewood S (2002) Emergency ambulance deployment in Barbados: A multiobjective approach. Journal of the Operational Research Society 53(2), 185–192.CrossRefGoogle Scholar
  54. Henderson S and Mason A (2004) Ambulance service planning: Simulation and data visualization. In Handbook of Operations Research and Health Care Methods and Applications. International series in Operations Research and Management Science (Sainfort F, Brandeau ML, Pierskalla WP, Eds.), 70:77–102.Google Scholar
  55. Hogan K and Revelle C (1986) Concepts and applications of backup coverage. Management Science 32, 1434–1444.Google Scholar
  56. Holcomb J and Sharpe N (2007) Forecasting police calls during peak times for the city of Cleveland. CS-BIGS 1(1), 47–53.Google Scholar
  57. Hoogeveen M (2010) Ambulance care in Europe. Technical report Ambulancezorg Nederland.Google Scholar
  58. Hughes O (2009) Crews waste 30,000 hours at A&E. Wales p: Daily Post. 8.Google Scholar
  59. Ibri S, Nourelfath M and Drias H (2012) A multi-agent approach for integrated emergency vehicle dispatching and covering problem. Engineering Applications of Artificial Intelligence 25(3), 554–565.CrossRefGoogle Scholar
  60. Ingolfsson A (2013) Operations Research and Health Care Policy. Springer New York: EMS Planning and Management.Google Scholar
  61. Ingolfsson A, Budge S and Erkut E (2008) Optimal ambulance location with random delays and travel times. Health Care Management Science 11(3), 262–274.CrossRefGoogle Scholar
  62. Inoue H, Yanagisawa S and Kamae I (2006) Computer-simulated assessment of methods of transporting severely injured individuals in disaster - case study of an airport accident. Computer Methods Programs in Biomedicine 81(3), 256–265.CrossRefGoogle Scholar
  63. Jagtenberg C, Bhulai S and van der Mei R (2015) An efficient heuristic for real-time ambulance redeployment. Operations Research for Health Care 4, 27–35.CrossRefGoogle Scholar
  64. Jain S and Mclean C (2003) In: Chick S, Sánchez P, Ferrin D, and Morrice D (Eds). Proceedings of the 2003 Winter Simulation Conference. pp 1068–1076.Google Scholar
  65. Jasim H (2002) Relief staff rostering for the st john ambulance service.Google Scholar
  66. Jones A (2011) Slow ambulance turnarounds cost NHS more than £10m. BBC News Wales.Google Scholar
  67. Kamentzky R, Shuman L and Wolfe H (1982) Estimating need and demand for prehospital care. Operations Research 30(6), 1148–1167.CrossRefGoogle Scholar
  68. Kergosien Y, Bélanger V, Soriano P, Gendreau M and Ruiz A (2015) A generic and flexible simulation-based analysis tool for ems management. International Journal of Production Research 53(24), 7299–7316.CrossRefGoogle Scholar
  69. Kergosien Y, Lenté C, Piton D and Billaut JC (2011) A tabu search heuristic for the dynamic transportation of patients between care units. European Journal of Operational Research 214(2), 442–452.CrossRefGoogle Scholar
  70. Knight V, Harper P and Smith L (2012) Ambulance allocation for maximal survival with heterogeneous outcome measures. Omega 40, 918–926.CrossRefGoogle Scholar
  71. Knight VA and Harper PR (2012) Modelling Emergency Medical Services with phase-type distributions. Health Systems 1, 58–68.Google Scholar
  72. Koole G and Mandelbaum A (2002) Queueing models of call centers: an introduction. Annals of Operations Research 113, 41–59.CrossRefGoogle Scholar
  73. Kozan E and Mesken N (2005) A Simulation Model for Emergency Centres. In: Zerger A and Argent R (Eds). Proceedings of the International Congress on Modelling and Simulation. Advances and Applications for Management and Decision Making. pp 2602–2608.Google Scholar
  74. Krafft T, Garcia-Castrillo Riesgo L, Fischer M, Lippert F, Overton J, and Robertson-Steel I (2006) Health monitoring and benchmarking of european ems systems: Components, indicators, recommendations. Technical report.Google Scholar
  75. Larsen MP, Eisenberg MS, Cummins RO and Hallstrom AP (1993). Predicting survival from out-of-hospital cardiac arrest: a graphic model. Annals of emergency medicine 22(11), 1652–1658.Google Scholar
  76. Larson RC (1974) A hypercube queuing model for facility location and redistricting in urban emergency services. Computers and Operations Research 1(1), 67–95.CrossRefGoogle Scholar
  77. Lee S (2011) The role of preparedness in ambulance dispatching. Journal of the Operational Research Society 62(10), 1888–1897.CrossRefGoogle Scholar
  78. Lee S (2012a) Ambulance-initiated dispatching by centrality principle in emergency medical service. In: IIE Annual Conference. Proceedings, Institute of Industrial Engineers-Publisher.Google Scholar
  79. Lee S (2012b) The role of centrality in ambulance dispatching. Decision Support Systems 54(1), 282–291.CrossRefGoogle Scholar
  80. Lee S (2013) Centrality-based ambulance dispatching for demanding emergency situations. Journal of the Operational Research Society 64(4), 611–618.CrossRefGoogle Scholar
  81. Lee, S (2014) Role of parallelism in ambulance dispatching. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(8), 1113–1122.CrossRefGoogle Scholar
  82. Lee T, Jang H, Cho SH, and Turner JG (2012) A simulation-based iterative method for trauma center - air ambulance location problem. In: Proceedings of the 2012 Winter Simulation Conference pp 955–966.Google Scholar
  83. Li X, Zhao Z, Zhu X and Wyatt T (2011) Covering models and optimization techniques for emergency response facility location and planning: a review. Mathematical Methods of Operations Research 74(3), 281–310.CrossRefGoogle Scholar
  84. Li Y and Kozan E (2009) Rostering ambulance services. Industrial engineering and management society pp 795–801.Google Scholar
  85. Solutions, Lightfoot (2009) Time to make a difference: Transforming ambulance services in Wales. Technical report: A modernisation plan for ambulance services and NHS Direct Wales.Google Scholar
  86. Lim CS, Mamat R and Braunl T (2011) Impact of ambulance dispatch policies on performance of emergency medical services. IEEE Transactions on Intelligent Transportation Systems 12(2), 624–632.Google Scholar
  87. Lowthian J, Cameron P, Stoelwinder J, Curtis A, Currell A, Cooke M, et al (2011a) Increasing utilisation of emergency ambulances. Australian Health Review 35(1), 63–69.CrossRefGoogle Scholar
  88. Lowthian J, Jolley D, Curtis A, Currell A, Cameron P, Stoelwinder J, et al (2011b) The challenges of population ageing: accelerating demand for emergency ambulance services by older patients, 1995–2015. The Medical Journal of Australia 194(11), 574–578.Google Scholar
  89. Mason AJ (2013) Simulation and Real-Time Optimised Relocation for Improving Ambulance Operations. pp 289–317 Springer New York New York, NY.Google Scholar
  90. Matteson D, McLean M, Woodard D and Henderson S (2011) Forecasting emergency medical service call arrival rates. The Annals of Applied Statistics 5(2B), 1379–1406.CrossRefGoogle Scholar
  91. Maxwell M, Restrepo M, Henderson S and Topaloglu H (2010) Approximate dynamic programming for ambulance redeployment. INFORMS Journal on Computing 22(2), 266–281.CrossRefGoogle Scholar
  92. McCormack R and Coates G (2015) A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival. European Journal of Operational Research, 247, 294–309.CrossRefGoogle Scholar
  93. McLay LA. and Mayorga ME (2013a) A dispatching model for server-to-customer systems that balances efficiency and equity. Manufacturing & Service Operations Management 15(2), 205–220.Google Scholar
  94. McLay LA and Mayorga ME (2013b) A model for optimally dispatching ambulances to emergency calls with classification errors in patient priorities. IIE Transactions 45(1), 1–24.Google Scholar
  95. NHS Choices (2014) Emergency and urgent care services. Technical report.Google Scholar
  96. NHS Scotland (2014) Annual Reports and Accounts 2013/14. Technical report.Google Scholar
  97. Nickel S, Reuter-Oppermann M and Saldanha-Da Gama F (2015) Ambulance location under stochastic demand: A sampling approach. Operations Research for Health Care.Google Scholar
  98. O’cathain A, Knowles E, Turner J, Hirst E, Goodacre S and Nicholl J (2015) Variation in avoidable emergency admissions: multiple case studies of emergency and urgent care systems. Journal of Health Services Research and Policy.Google Scholar
  99. Parragh S (2009) Ambulance routing problems with rich constraints and multiple objectives. Dissertation, Fakultaet fuer Wirtschaftswissenschaften Universitaet Wien, Vienna.Google Scholar
  100. Parragh SN and Doerner KF, Hartl RF and Gandiblex X (2009) A heuristic two-phase solution approach for the multi-objective dial-a-ride problem. Networks 54(4), 227–242.Google Scholar
  101. Rajagopalan HK and Saydam C, Setzler H, and Sharer E (2011) Ambulance deployment and shift scheduling: An integrated approach. Journal of Service Science and Management 4(01), 66.Google Scholar
  102. Repede J and Bernardo J (1994) Developing and validating a decision support system for locating emergency medical vehicles in louisville, kentucky. European Journal of Operational Research 75(3), 567–581.CrossRefGoogle Scholar
  103. Reuter-Oppermann M, Kunze Von Bischhoffshausen J and Hottum P (2015) Towards an it-based coordination platform for the german emergency medical service system. In Exploring Services Science (Novoa H and Dragoicea M, Eds), volume 201 of Lecture Notes in Business Information Processing pp 253–263, Springer International Publishing.Google Scholar
  104. Revelle C and Hogan K (1988) A reliability-constrained siting model with local estimates of busy fractions. Environment and Planning B: Planning and Design 15(2), 143–152.CrossRefGoogle Scholar
  105. Revelle C and Swain R (1970) Central facilities location. Geographical Analysis 2(1), 30–42.CrossRefGoogle Scholar
  106. Ritzinger U and Puchinger J (2012) Real-world patient transportation. In 19th ITS World Congress.Google Scholar
  107. Sacco W, Navin D, Fiedler K, Waddell R, Long W and Buckman R (2005) Precise formulation and evidence-based application of resourceconstrained triage. Academic Emergency Medicine 12(8), 759–770.CrossRefGoogle Scholar
  108. Schilde M, Doerner KF and Hartl RF (2011) Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports. Computers & operations research 38(12), 1719–1730.Google Scholar
  109. Schlechtriemen T, Burghofer K, Lackner CK and Altemeyer KH (2005a) Validation of the naca score based on objectifiable parameters: Analysis of 104,962 primary air rescue missions in 1999–2003. Notfall & Rettungsmedizin 8(2), 96–108.Google Scholar
  110. Schlechtriemen T, Burghofer K, Stolpe E, Altemeyer KH and Lackner CK (2005b) The munich naca score: Modification of the naca score for preclinical emergency medicine. Notfall & Rettungsmedizin 8(2), 109–111.Google Scholar
  111. Schmid V (2012) Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. European Journal of Operational Research 219(3), 611–621.CrossRefGoogle Scholar
  112. Setzler H, Park S and Saydam C (2009) EMS call volume predictions: A comparative study. Computers & Operations Research 36, 1843–1851.CrossRefGoogle Scholar
  113. Snooks H, Kingston M, Anthony R, and Russell I (2013) New models of emergency prehospital care that avoid unnecessary conveyance to Emergency Department: Translation of research evidence into practice? The Scientific World Jounral.Google Scholar
  114. Sudtachat K, Mayorga ME and Mclay LA (2014) Recommendations for dispatching emergency vehicles under multitiered response via simulation. International Transactions in Operational Research 21(4), 581–617.Google Scholar
  115. Takeda RA and Widmer JA and Morabito R (2007) Analysis of ambulance decentralization in an urban emergency medical service using the hypercube queueing model. Computers and Operations Research 34(3), 727–741.Google Scholar
  116. Takedaa R, Widmera J and Morabitob R (2007) Analysis of ambulance decentralization in an urban emergency medical service using the hypercube queueing model. Computers & Operations Research 34, 727–741.CrossRefGoogle Scholar
  117. Toregas C, Swain R, Revelle C and Bergman L (1971) The location of emergency service facilities. Operations Research 19(6), 1363–1373.CrossRefGoogle Scholar
  118. Van Buuren M, Van Der Mei R, Aardal K and Post H (2012) Evaluating dynamic dispatch strategies for emergency medical services: Tifar simulation tool. In Proceedings of the Winter Simulation Conference (Laroque C, Himmelspach J, Pasupathy R and Uhrmacher AM, Eds), p 46.Google Scholar
  119. van den Berg PL and Aardal K (2015) Time-dependent MEXCLP with start-up and relocation cost. European Journal of Operational Research 242(2), 383–389.Google Scholar
  120. Van Den Berg PL, Kommer GJ and Zuzáková B (2015) Linear formulation for the maximum expected coverage location model with fractional coverage. Operations Research for Health Care.Google Scholar
  121. Van Den Berg PL, Van Essen JT and Harderwijk EJ (2016) Comparison of static ambulance location models. In: International Conference on Logistics and Operations Management (GOL).Google Scholar
  122. van den Bergh J, Beliën J, de Bruecker P, Demeulemeester E and de Boeck L (2013) Personnel scheduling: A literature review. European Journal of Operational Research 226(3), 367–385.CrossRefGoogle Scholar
  123. Van Essen JT, Hurink JL, Nickel S and Reuter M (2013) Models for ambulance planning on the strategic and tactical level. Technical report Beta Research School for Operations Management and Logistics.Google Scholar
  124. Vile J, Gillard J, Harper P and Knight V (2012) Predicting ambulance demand using singular spectrum analysis. Journal of the Operational Research Society 63(11), 1556–1565.CrossRefGoogle Scholar
  125. Vile J, Gillard J, Harper P and Knight V (2016) Time-dependent stochastic methods for managing and scheduling emergency medical services. Operations Research for Health Care.Google Scholar
  126. Wang Y, Luangkesorn K and Shuman L (2012) Modeling emergency medical response to a mass casualty incident using agent based simulation. Socio-Economic Planning Sciences, 46(4), 281–290.CrossRefGoogle Scholar
  127. Wong HT and Lai PC (2014) Weather factors in the short-term forecasting of daily ambulance calls. International journal of biometeorology, 58(5), 669–678.Google Scholar
  128. Workforce, Facilities Team H and Centre S C I (2014) Ambulance Services, England 2013-14. Technical report.Google Scholar
  129. Yue Y, Marla L, Krishnan R, Heinz HJ, and College I I I (2012) An Efficient Simulation-based Approach to Ambulance Fleet Allocation and Dynamic Redeployment. In: AAAI Conference on Artificial Intelligence.Google Scholar
  130. Zarkeshzadeh M, Zare H, Heshmati Z and Teimouri M (2016) A novel hybrid method for improving ambulance dispatching response time through a simulation study. Simulation Modelling Practice and Theory 60, 170–184.CrossRefGoogle Scholar
  131. Zhang O, Mason A, and Philpott A (2008) Simulation and optimisation for ambulance logistics and relocation. In: Presentation at the INFORMS 2008 Conference.Google Scholar
  132. Zhen L, Wang K, Hu H and Chang D (2014) A simulation optimization framework for ambulance deployment and relocation problems. Computers and Industrial Engineering 72, 12–23.CrossRefGoogle Scholar

Copyright information

© The OR Society 2017

Authors and Affiliations

  • Melanie Reuter-Oppermann
    • 1
    • 2
    Email author
  • Pieter L. van den Berg
    • 3
    • 4
    • 5
  • Julie L. Vile
    • 6
    • 7
  1. 1.Institute of Operations ResearchKarlsruhe Institute of Technology (KIT)KarlsruheGermany
  2. 2.Karlsruhe Service Research Institute (KSRI)Karlsruhe Institute of Technology (KIT)KarlsruheGermany
  3. 3.Rotterdam School of ManagementErasmus UniversityRotterdamThe Netherlands
  4. 4.Delft Institute of Applied MathematicsDelft University of TechnologyDelftThe Netherlands
  5. 5.Centrum Wiskunde and InformaticaAmsterdamThe Netherlands
  6. 6.School of MathematicsCardiff UniversityCardiffUK
  7. 7.NHS Delivery UnitBridgendUK

Personalised recommendations