Abstract
The hypercube is a well-known descriptive model for planning server-to-customer systems. In the present study we adapt this model to analyze Emergency Medical Systems on highways involving partial backup and multiple dispatching of ambulances. The modified model is then embedded into a genetic algorithm to optimize the configuration and operation of the system. By embedding the hypercube into a genetic algorithm, we can support decisions, such as, determining the optimal districts for the system in order to optimize the mean performance measures. Computational results are analyzed applying the approach to the case study of an EMS operating on Brazilian highways.
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Arroyo, J. C., & Armentano, V. A. (2005). Genetic local search for multi-objective flowshop scheduling. European Journal of Operational Research, 167, 717–738.
Atkinson, J. B., Kovalenko, I. N., Kuznetsov, N., & Mykhalevych, K. V. (2005, to appear). Heuristic solution methods for a hypercube queueing model of the deployment of emergency systems. Cybernetics and Systems Analysis.
Banks, J. (1998). Handbook of simulation (pp. 3–389). Atlanta: Wiley.
Batta, R., Dolan, J. M., & Krishnamurthy, N. N. (1989). The maximal expected covering location problem: revisited. Transportation Science, 23, 277–287.
Beasley, J. E. (2002). Population heuristics. In P. M. Pardalos & M. G. C. Resende (Eds.), Handbook of applied optimization (pp. 138–157). Oxford: University Press.
Brandeau, M., & Larson, R. C. (1986). Extending and applying the hypercube queueing model to deploy ambulances in Boston. In A. J. Swersey & E. J. Ingnall (Eds.), TIMS studies in the management science : Vol. 22. Delivery of urban services (pp. 121–153). Amsterdam: Elsevier.
Brotcorne, L., Laporte, G., & Semet, F. (2003). Ambulance location and relocation models. European Journal of Operational research, 147, 451–463.
Burwell, T. H., Jarvis, J. P., & Mcknew, M. A. (1993). Modeling co-located servers and dispatch ties in the hypercube model. Computers & Operations Research, 20(2), 113–119.
Chelst, K., & Barlach, Z. (1981). Multiple unit dispatches in emergency services: models to estimate system performance. Management Science, 27(12), 1390–1409.
Chiyoshi, F., Galvão, R. D., & Morabito, R. (2003). A note on solutions to the maximal expected covering location problem. Computers & Operations Research, 30(1), 87–96.
Eaton, D. J., Daskin, M. S., Simmons, D., Bulloch, B., & Jansma, G. (1985). Determining emergency medical service vehicle deployment in Austin, Texas. Interfaces, 15, 96–108.
Fujiwara, O., Makjamroen, T., & Gupta, K. K. (1987). Ambulance deployment analysis: a case study of Bangkok. European Journal of Operational Research, 31, 9–18.
Galvão, R. D., Chiyoshi, F., & Morabito, R. (2005). Towards unified formulations and extensions of two classical probabilistic location models. Computers & Operations Research, 32, 15–33.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. New York: Addison–Wesley.
Goldberg, J., & Paz, L. (1991). Location of emergency vehicle bases when service times depends on call location. Transportation Science, 25, 264–280.
Harewood, S. I. (2002). Emergency ambulance deployment in Barbados: a multi-objective approach. Journal of the Operational Research Society, 53, 185–192.
Hertz, A., & Kobler, D. (2000). A framework for the description of evolutionary algorithms. European Journal of Operational Research, 126, 1–12.
Holland, J. H. (1975). Adaptation in natural and artificial systems. Cambridge: MIT Press.
Iannoni, A., & Morabito, R. (2006). A discrete simulation analysis of a logistics supply system. Transportation Research Part E, 42, 191–210.
Iannoni, A. P., & Morabito, R. (2007) A multiple dispatch and partial backup hypercube queuing model to analyze emergency medical systems on highways. Transportation Research E, doi:10.1016/j.tre.2006.05.005
Jarvis, J. P. (1985). Approximating the equilibrium behavior of multi-server loss systems. Management Science, 31, 235–239.
Jaszkiewicz, A. (2002). Genetic local search for multi-objective combinatorial optimization. European Journal of Operational Research, 137, 50–71.
Kelton, W. D., Sadowski, R. P., & Sadowski, D. (2002). A simulation with arena (2nd ed.) New York: McGraw–Hill.
Larson, R. C. (1974). Hypercube queueing model for facility location and redistricting in urban emergency services. Computers & Operations Research, 1, 67–95.
Larson, R. C. (1975). Approximating the performance of urban emergency service systems. Operations Research, 23, 845–868.
Larson, R. C. (2004). OR models for homeland security. OR/MS Today, 31, 22–29.
Larson, R. C., & Odoni, A. R. (1981). Urban operations research. New Jersey: Prentice Hall.
Mendonça, F. C., & Morabito, R. (2001). Analyzing emergency service ambulance deployment on a Brazilian highway using the hypercube model. Journal of the Operation Research Society, 52, 261–268.
Michalewicz, Z. (1996). Genetic algorithms + data structures = evolution programs (3rd ed.) Berlin: Springer.
Owen, S. H., & Daskin, M. S. (1998). Strategic facility location: a review. European Journal of Operational Research, 111, 423–447.
Pegden, C. D., Shannon, R. E., & Sadowski, R. P. (1995). Introduction to simulation using SIMAN (2nd ed.) New York: McGraw–Hill.
Rajagopalan, H. K., Saydam, C., & Xiao, J. (2006, to appear) A multiperiod set covering location model for a dynamic redeployment of ambulances. Computers & Operations Research.
Sacks, S. R., & Grief, S. (1994) Orlando Police Department uses OR/MS methodology, new software to design patrol districts. OR/MS Today, Baltimore, 30–32.
Saydam, C., & Aytug, H. (2003). Accurate estimation of expected coverage: revisited. Socio-Economic Planning Sciences, 37, 69–80.
Swersey, A. J. (1994). Handbooks in OR/MS (Vol. 6, pp. 151–200). Amsterdam: Elsevier.
Takeda, R. A., Widmer, J. A., & Morabito, R. (2007). Analysis of ambulance decentralization in an urban medical emergency service using the hypercube queueing model. Computers & Operations Research, 34(3), 727–741.
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Iannoni, A.P., Morabito, R. & Saydam, C. A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways. Ann Oper Res 157, 207–224 (2008). https://doi.org/10.1007/s10479-007-0195-z
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DOI: https://doi.org/10.1007/s10479-007-0195-z