Abstract
We study the problem of optimal location of the 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 workday of the emergency medical service (EMS). This model takes into account the stochastic nature of the problem and the changes in road network loading. To solve the problem, we develop an algorithm of genetic local search with the four types of neighborhoods. The simulation model in this algorithm is used to compute the value of the objective function. We study the influence of neighborhoods on the accuracy of the obtained solution. Computer simulation is performed on the example of the EMS of Vladivostok city. We show that it is possible to reduce the average waiting time by \(1.5 \) times. The estimates are obtained of the impact of traffic congestion on the average waiting time.
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REFERENCES
M. Reuter-Oppermann, P. L. van den Berg, and J. L. Vile, “Logistics for Emergency Medical Service Systems,” Health Systems 6, 187–208 (2017).
L. Brotcorne, G. Laporte, and F. Semet, “Ambulance Location and Relocation Models,” European J. Oper. Res. 147, 451–463 (2003).
J. Goldberg, “Operations Research Models for the Deployment of Emergency Services Vehicle,” EMS Management J. 1, 20–39 (2004).
X. Li, Z. Zhao, X. Zhu, and T. Wyatt, “Covering Models and Optimization Techniques for Emergency Response Facility Location and Planning: A Review,” Math. Meth. Oper. Res. 74 (3), 281–310 (2011).
R. Aringhieri, M. E. Bruni, S. Khodaparasti, and J. T. van Essen, “Emergency Medical Services and Beyond: Addressing New Challenges through a Wide Literature Review,” Comput. Oper. Res. 78, 349–368 (2017).
V. Bélanger, A. Ruiz,and P. Sorianoa, “Recent Optimization Models and Trends in Location, Relocation, and Dispatching of Emergency Medical Vehicles,” European J. Oper. Res. 272, 1–23 (2019).
N. Andersson and P. Varbrand, “Decision Support Tools for Ambulance Dispatch and Relocation,” J. Oper. Res. Soc. 58 (2), 195–201 (2007).
V. Schmid, “Solving the Dynamic Ambulance Relocation and Dispatching Problem Using Approximate Dynamic Programming,” European J. Operational Res. 219 (3), 611–621 (2012).
J. A. Fitzsimmons and B. N. Srikar, “Emergency Ambulance Location Using the Contiguous Zone Search Routine,” J. Oper. Management 2 (4), 225–237 (1982).
M. A. Zaffar, H. K. Rajagopalan, C. Saydam, M. Mayorga, and E. Sharer, “Coverage, Survivability or Response Time: A Comparative Study of Performance Statistics Used in Ambulance Location Models via Simulation-Optimization,” Oper. Res. Health Care 11 (111), 1–12 (2016).
S. G. Henderson and A. J. Mason, “Ambulance Service Planning: Simulation and Data Visualization,” Handbook of Operations Research and Health Care Methods and Applications 70, 77–102 (2004).
R. McCormack and G. Coates, “A Simulation Model to Enable the Optimization of Ambulance Fleet Allocation and Base Station Location for Increased Patient Survival,” European J. Oper. Res. 247, 294–309 (2015).
L. Aboueljinane, E. Sahin and Z. Jemai, “A Review of Simulation Models Applied to Emergency Medical Service Operations,” Comput. Ind. Eng. 66, 734–750 (2013).
L. Zhen, K. Wang, H. Hu, and D. Chang, “A Simulation Optimization Framework for Ambulance Deployment and Relocation Problems,” Comput. Ind. Eng. 72, 12–23 (2014).
R. Garcia and A. Marin, “Network Equilibrium Models with Combined Modes: Models and Solution Algorithms,” Transp. Res. B, 39, 223–254 (2005).
N. B. Shamray, “The General Multimodal Network Equilibrium Problem with Elastic Balanced Demand,” in Discrete Optimization and Operations Research (Proceedings of 9th International Conference DOOR-2016, Vladivostok, Russia, September 19-23, 2016) (RWTH Aachen Univ., Aachen, 2017), pp. 404–414.
PTV Group (PTV Planung Transport Verkehr, Karlsruhe, 2021). Available at https://www.ptvgroup.com (accessed January 20, 2021).
AIMSUN (TSS-Transport Simulation Systems, Barcelona, 2021). Available at https://www.aimsun.com (accessed January 20, 2021).
TRANSIMS Open-Source. Available at https://code.google.com/archive/p/transims (accessed July 8, 2020).
M. Beckmann, C. B. McGuire, and C. B. Winsten, Studies in the Economics of Transportation (Yale University Press, New Haven, 1956).
Traffic Assignment Manual: for Application with a Large, High Speed Computer (U.S. Department of Commerce, Bureau of Public Roads, Washington, D.C., 1964).
A. G. Wilson, Entropy in Urban and Regional Modelling (Pion, London, 1970; Nauka, Moscow, 1978).
S.-C. Fang, J. R. Rajasekera, and H.-S. J. Tsao, Entropy Optimization and Mathematical Programming (Kluwer Academic, Boston, 1997).
M. Patriksson, The Traffic Assignment Problem. Models and Methods (VSP, Utrecht, Netherlands, 1994).
L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence through Simulated Evolution (Wiley, Chichester, UK, 1966).
J. H. Holland, Adaptation in Natural and Artificial Systems (University of Michigan Press, Ann Arbor, 1975).
A. G. Ivakhnenko, Heuristic Self-Organization Systems in Engineering Cybernetics (Tekhnika, Kiev, 1971) [in Russian].
L. A. Rastrigin, Statistical Search Methods (Nauka, Moscow, 1968) [in Russian].
Funding
The first author was supported by the State Task to the Sobolev Institute of Mathematics (project no. 0314–2019–0014). The second author was supported by the Russian Foundation for Basic Research (Project 18–29–03071).
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Translated by L.B. Vertgeim
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Kochetov, Y.A., Shamray, N.B. Optimization of the Ambulance Fleet Location and Relocation. J. Appl. Ind. Math. 15, 234–252 (2021). https://doi.org/10.1134/S1990478921020058
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DOI: https://doi.org/10.1134/S1990478921020058