Optimization of~the~ambulance fleet location~and~relocation

Yu. A. Kochetov ; N. B. Shamray

Diskretnyj analiz i issledovanie operacij, Tome 28 (2021) no. 2, pp. 5-34.

Résumé

We consider the problem of optimal location of an ambulance fleet at the base stations. The objective is to minimize the average waiting time for ambulance arrival. We elaborate a simulation model that describes a day of work of the Emergency Medical Service (EMS). This model takes into account the stochastic nature of the problem and the changing busyness of the city’s transport network. To solve the problem, a genetic local search algorithm was developed with 4 types of neighborhoods. The simulation model in this algorithm is used to compute the value of the objective function. We investigate the influence of neighborhoods on the accuracy of the obtained solutions and conduct computer simulations on the example of the Vladivostok EMS. We show that it is possible to reduce the average waiting time by 1.5 times. Some estimates on the impact of traffic congestion on the average waiting time are obtained. Tab. 4, illustr. 11, bibliogr. 28.

Détail

Keywords: optimization, emergency medical service, simulation model, genetic algorithm, local search.

Bibliographie

[1] Reuter-Oppermann M., van den Berg P. L., Vile J. L., “Logistics for emergency medical service systems”, Health Syst., 6 (2017), 187–208 | DOI

[2] Brotcorne L., Laporte G., Semet F., “Ambulance location and relocation models”, Eur. J. Oper. Res., 147 (2003), 451–463 | DOI | MR | Zbl

[3] Goldberg J., “Operations research models for the deployment of emergency services vehicle”, EMS Manage. J., 1 (2004), 20–39

[4] Li X., Zhao Z., Zhu X., Wyatt T., “Covering models and optimization techniques for emergency response facility location and planning: a review”, Math. Methods Oper. Res., 74:3 (2011), 281–310 | DOI | MR | Zbl

[5] Aringhieri R., Bruni M. E., Khodaparasti S., van Essen J. T., “Emergency medical services and beyond: Addressing new challenges through a wide literature review”, Comput. Oper. Res., 78 (2017), 349–368 | DOI | MR | Zbl

[6] Bélanger V., Ruiz A., Sorianoa P., “Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles”, Eur. J. Oper. Res., 272 (2019), 1–23 | DOI | MR | Zbl

[7] Andersson N., Värbrand P., “Decision support tools for ambulance dispatch and relocation”, J. Oper. Res. Soc., 58:2 (2007), 195–201 | DOI | Zbl

[8] Schmid V., “Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming”, Eur. J. Oper. Res., 219:3 (2012), 611–621 | DOI | MR | Zbl

[9] Fitzsimmons J. A., Srikar B. N., “Emergency ambulance location using the contiguous zone search routine”, J. Oper. Manage, 2:4 (1982), 225–237 | DOI

[10] Zaffar M. A, Rajagopalan H. K, Saydam C., Mayorga M., Sharer E., “Coverage, survivability or response time: a comparative study of performance statistics used in ambulance location models via simulation-optimization”, Oper. Res. Health Care, 11 (2016), 1–12 | DOI

[11] Henderson S. G., Mason A. J., “Ambulance service planning: simulation and data visualization”, Oper. Res. Health Care, 70 (2004), 77–102 | DOI

[12] McCormack R., Coates G., “A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival”, Eur. J. Oper. Res., 247 (2015), 294–309 | DOI | Zbl

[13] Aboueljinane L., Sahin E., Jemai Z., “A review of simulation models applied to emergency medical service operations”, Comput. Ind. Eng., 66 (2013), 734–750 | DOI

[14] Zhen L., Wang K., Hu H., Chang D., “A simulation optimization framework for ambulance deployment and relocation problems”, Comput. Ind. Eng., 72 (2014), 12–23 | DOI

[15] Garcia R., Marin A., “Network equilibrium models with combined modes: models and solution algorithms”, Transp. Res. B, 39 (2005), 223–254 | DOI

[16] Shamray N. B., “The general multimodal network equilibrium problem with elastic balanced demand”, Discrete Optimization and Operations Research. Suppl. Proc. 9th Int. Conf. DOOR (Vladivostok, Russia, Sept. 19–23, 2016), CEUR Workshop Proc., 1623, RWTH Aachen Univ., Aachen, 2017, 404–414 (accessed Jan. 20, 2021) http://ceur-ws.org/Vol-1623

[17] PTV Group, , PTV Planung Transport Verkehr, Karlsruhe, 2021 (accessed Jan. 20, 2021) http://www.ptvgroup.com

[18] Aimsun, , TSS-Transport Simulation Systems, Barcelona, 2021 (accessed Jan. 20, 2021) http://www.aimsun.com

[19] TRANSIMS Open-Source, (accessed Jan. 20, 2021) http://code.google.com/archive/p/transims

[20] Beckmann M., McGuire C. B., Winsten C. B., Studies in the economics of transportation, Yale Univ. Press, New Haven, 1956

[21] Traffic Assignment Manual, Bureau of Public Roads, Washington D.C., 1964

[22] A. G. Wilson, Entropy in Urban and Regional Modelling, Pion, London, 1970

[23] Fang S.-C., Rajasekera J. R., Tsao H.-S. J., Entropy optimization and mathematical programming, Kluwer Acad. Publ., Boston, 1997 | MR | Zbl

[24] Patriksson M., The traffic assignment problem — Models and methods, VSP, Utrecht, 1994

[25] Fogel L. J., Owens A. J., Walsh M. J., Artificial intelligence through simulated evolution, Wiley, Chichester, UK, 1966 | Zbl

[26] Holland J. H., Adaptation in natural and artificial systems, Univ. Michigan Press, Ann Arbor, 1975 | MR

[27] A. G. Ivakhnenko, Heuristic Self-Organization Systems in Engineering Cybernetics, Tekhnika, Kyiv, 1971 (Russian)

[28] L. A. Rastrigin, Statistical Search Methods, Nauka, M., 1968 (Russian)