A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival

European Journal of Operational Research - Tập 247 - Trang 294-309 - 2015
Richard McCormack1, Graham Coates1
1School of Engineering and Computing Sciences, Durham University, South Road, Durham DH1 3LE, UK

Tài liệu tham khảo

Aytug, 2002, Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study, European Journal of Operational Research, 141, 480, 10.1016/S0377-2217(01)00260-0 Bandara, 2012, Optimal dispatching strategies for emergency vehicles to increase patient survivability, International Journal of Operational Research, 15, 195, 10.1504/IJOR.2012.048867 Beraldi, 2009, A probabilistic model applied to emergency service vehicle location, European Journal of Operational Research, 196, 323, 10.1016/j.ejor.2008.02.027 Bianchi, 1988, A hybrid fleet model for emergency medical service system design, Social Science & Medicine, 26, 163, 10.1016/0277-9536(88)90055-X Brotcorne, 2003, Ambulance location and relocation models, European Journal of Operational Research, 147, 451, 10.1016/S0377-2217(02)00364-8 Chiyoshi, 2003, A note on solutions to the maximal expected covering location problem, Computers & Operations Research, 30, 87, 10.1016/S0305-0548(01)00083-1 Daskin, 1983, A maximum expected covering location model: Formulation, properties and heuristic solution, Transportation Science, 17, 48, 10.1287/trsc.17.1.48 Erkut, 2008, Ambulance location for maximum survival, Naval Research Logistics (NRL), 55, 42, 10.1002/nav.20267 Farahani, 2012, Covering problems in facility location: A review, Computers & Industrial Engineering, 62, 368, 10.1016/j.cie.2011.08.020 Fitzsimmons, 1982, Emergency ambulance location using the contiguous zone search routine, Journal of Operations Management, 2, 225, 10.1016/0272-6963(82)90011-0 Galvao, 2005, Towards unified formulations and extensions of two classical probabilistic location models, Computers & Operations Research, 32, 15, 10.1016/S0305-0548(03)00200-4 Galvao, 2008, Emergency service systems: The use of the hypercube queueing model in the solution of probabilistic location problems, International Transactions in Operational Research, 15, 525, 10.1111/j.1475-3995.2008.00654.x Gendreau, 1997, Solving an ambulance location model by tabu search, Location Science, 5, 75, 10.1016/S0966-8349(97)00015-6 Gendreau, 2001, A dynamic model and parallel tabu search heuristic for real-time ambulance relocation, Parallel Computing, 27, 1641, 10.1016/S0167-8191(01)00103-X Gendreau, 2006, The maximal expected coverage relocation problem for emergency vehicles, The Journal of the Operational Research Society, 57, 22, 10.1057/palgrave.jors.2601991 Geroliminis, 2011, A hybrid hypercube – genetic algorithm approach for deploying many emergency response mobile units in an urban network, European Journal of Operational Research, 210, 287, 10.1016/j.ejor.2010.08.031 Goldberg, 1989, Genetic algorithms in search, optimization, and machine learning Goldberg, 1990, A simulation model for evaluating a set of emergency vehicle base locations: Development, validation, and usage, Socio-Economic Planning Sciences, 24, 125, 10.1016/0038-0121(90)90017-2 Goldberg, 2004, Operations research models for the deployment of emergency services vehicles, EMS Management Journal, 1, 20 Haupt, 2004 Hausner, 1975, Determining the travel characteristics of emergency service vehicles Henderson, 2004, Ambulance service planning: simulation and data visualization, Handbook of Operations Research and Health Care Methods and Applications, 70, 77, 10.1007/1-4020-8066-2_4 Holland, 1975 Houck, 1996, Comparison of genetic algorithms, random restart and two-opt switching for solving large location-allocation problems, Computers & Operations Research, 23, 587, 10.1016/0305-0548(95)00063-1 Iannoni, 2008, A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways, Annals of Operations Research, 157, 207, 10.1007/s10479-007-0195-z Iannoni, 2009, An optimization approach for ambulance location and the districting of the response segments on highways, European Journal of Operational Research, 195, 528, 10.1016/j.ejor.2008.02.003 Ingolfsson, 2008, Optimal ambulance location with random delays and travel times, Health Care Management Science, 11, 262, 10.1007/s10729-007-9048-1 Jarvis, 1985, Approximating the equilibrium behavior of multi-server loss systems, Management Science, 31, 235, 10.1287/mnsc.31.2.235 Knight, 2012, Ambulance allocation for maximal survival with heterogeneous outcome measures, Omega, 40, 918, 10.1016/j.omega.2012.02.003 Larson, 1975, Approximating the performance of urban emergency service systems, Operations Research, 23, 845, 10.1287/opre.23.5.845 Li, 2011, Covering models and optimization techniques for emergency response facility location and planning: A review, Mathematical Methods of Operations Research, 74, 281, 10.1007/s00186-011-0363-4 Man, 1999 Marianov, 1995, Siting emergency services, 199 Marianov, 1996, The queueing maximal availability location problem: A model for the siting of emergency vehicles, European Journal of Operational Research, 93, 110, 10.1016/0377-2217(95)00182-4 Maxwell, 2010, Approximate dynamic programming for ambulance redeployment, INFORMS Journal on Computing, 22, 266, 10.1287/ijoc.1090.0345 McLay, 2010, Evaluating emergency medical service performance metrics, Health Care Management Science, 13, 124, 10.1007/s10729-009-9115-x McLay, 2011, Evaluating the impact of performance goals on dispatching decisions in emergency medical service, IIE Transactions on Healthcare Systems Engineering, 1, 185, 10.1080/19488300.2011.618820 Naoum-Sawaya, 2013, A stochastic optimization model for real-time ambulance redeployment, Computers & Operations Research, 40, 1972, 10.1016/j.cor.2013.02.006 Rajagopalan, 2008, A multiperiod set covering location model for dynamic redeployment of ambulances, Computers & Operations Research, 35, 814, 10.1016/j.cor.2006.04.003 Repede, 1994, Developing and validating a decision support system for locating emergency medical vehicles in Louisville, Kentucky, European Journal of Operational Research, 75, 567, 10.1016/0377-2217(94)90297-6 ReVelle, 1989, Review, extension and prediction in emergency service siting models, European Journal of Operational Research, 40, 58, 10.1016/0377-2217(89)90272-5 ReVelle, 2005, Location analysis: A synthesis and survey, European Journal of Operational Research, 165, 1, 10.1016/j.ejor.2003.11.032 Sasaki, 2010, Using genetic algorithms to optimise current and future health planning – the example of ambulance locations, International Journal of Health Geographics, 9, 10.1186/1476-072X-9-4 Saydam, 2003, Accurate estimation of expected coverage: revisited, Socio-Economic Planning Sciences, 37, 69, 10.1016/S0038-0121(02)00004-6 Saydam, 1994, Accurate estimation of expected coverage: A comparative study, Socio-Economic Planning Sciences, 28, 113, 10.1016/0038-0121(94)90010-8 Schmid, 2012, Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming, European Journal of Operational Research, 219, 611, 10.1016/j.ejor.2011.10.043 Toregas, 1971, The location of emergency service facilities, Operations Research, 19, 1363, 10.1287/opre.19.6.1363 Toro-Diaz, 2013, Joint location and dispatching decisions for emergency medical services, Computers & Industrial Engineering, 64, 917, 10.1016/j.cie.2013.01.002 Toro-Diaz, 2014, Reducing disparities in large-scale emergency medical service systems, Journal of the Operational Research Society, 65, 1 Valenzuela, 1997, Estimating effectiveness of cardiac arrest interventions: A logistic regression survival model, Circulation, 96, 3308, 10.1161/01.CIR.96.10.3308 Yue, 2012, An efficient simulation-based approach to ambulance fleet allocation and dynamic redeployment Zarandi, 2013, The large-scale dynamic maximal covering location problem, Mathematical and Computer Modelling, 57, 710, 10.1016/j.mcm.2012.07.028