Fair facility allocation in emergency service system

    Jaroslav Janáček Affiliation
    ; Lýdia Gábrišová Affiliation
    ; Miroslav Plevný   Affiliation


The request of equal accessibility must be respected to some extent when dealing with problems of designing or rebuilding of emergency service systems. Not only the disutility of the average user but also the disutility of the worst situated user must be taken into consideration. Respecting this principle is called fairness of system design. Unfairness can be mitigated to a certain extent by an appropriate fair allocation of additional facilities among the centres. In this article, two criteria of collective fairness are defined in the connection with the facility allocation problem. To solve the problems, we suggest a series of fast algorithms for solving of the allocation problem. This article extends the family of the original solving techniques based on branch-and-bound principle by newly suggested techniques, which exploit either dynamic programming principle or convexity and monotony of decreasing nonlinearities in objective functions. The resulting algorithms were tested and compared performing numerical experiments with real-sized problem instances. The new proposed algorithms outperform the original approach. The suggested methods are able to solve general min-sum and min-max problems, in which a limited number of facilities should be assigned to individual members from a finite set of providers.

Keyword : emergency system design, collective fairness, equal accessibility, allocation problem, dynamic programming, polynomial approach

How to Cite
Janáček, J., Gábrišová, L., & Plevný, M. (2020). Fair facility allocation in emergency service system. Journal of Business Economics and Management, 21(4), 1058-1071.
Published in Issue
Jun 2, 2020
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Aringhieri, R., Carello, G., & Morale, D. (2016). Supporting decision making to improve the performance of an Italian Emergency Medical Service. Annals of Operations Research, 236, 131–148.

Boujemaa, R., Jebali, A., Hammami, S., Ruiz, A., & Bouchriha, H. (2018). A stochastic approach for designing two-tiered emergency medical service systems. Flexible Services and Manufacturing Journal, 30, 123–152.

Brotcorne, L., Laporte, G., & Semet, F. (2003). Ambulance location and relocation models. European Journal of Operational Research, 147, 451–463.

Carmen, R., Van Nieuwenhuyse, I., & Van Houdt, B. (2018). Inpatient boarding in emergency departments: Impact on patient delays and system capacity. European Journal of Operational Research, 271(3), 953–967.

Chanta S., Mayorga, M. E., & McLay, L. A. (2014). Improving emergency service in rural areas: a biobjective covering location model for EMS systems. Annals of Operations Research, 221(1), 133–159.

Cyganska, M. (2017). Analysis of high cost outliers in a Polish reference hospital. E&M Ekonomie a Management, 20(4), 59–69.

Davis, R. (2005, May 20). The price of just a few seconds lost: People die. USA Today. Retrieved February 12, 2019, from

Garner, A. A., & van den Berg, P. L. (2017). Locating helicopter emergency medical service bases to optimise population coverage versus average response time. BMC Emergency Medicine, 17, 31.

Ingolfsson, A., Budge, S., & Erkut, E. (2008). Optimal ambulance location with random delays and travel times. Health Care Management Science, 11(3), 262–274.

Janáček, J., & Gábrišová, L. (2009). A two-phase method for the capacitated facility problem of compact customer sub-sets. Transport, 24(4), 274–282.

Janáček, J., & Gábrišová, L. (2017). Collective fairness in emergency system designing. In SOR´17: Proceedings of the 14th International Symposium on Operational Research (pp. 135–140). Bled, Slovenia.

Jánošíková, Ľ. (2007). Emergency medical service planning. Komunikácie, 9(2), 64–68.

Jánošíková, Ľ., Jankovič, P., & Kvet, M. (2017). Improving emergency system using simulation and optimization. In SOR´17: Proceedings of the 14th International Symposium on Operational Research (pp. 269–274). Bled, Slovenia.

Koval, O., Nabareseh, S., & Chromjaková, F. (2019). Standardization in services: Assessing the impact on customer satisfaction. E&M Ekonomie a Management, 22(3), 186–203.

Kvet, M. (2014). Computational study of radial approach to public service system design with generalized utility. In Digital Technologies 2014: the 10th International IEEE Conference. Zilina, Slovakia. IEEE.

Kvet, M. (2015). Exact and heuristic radial approach to fair public service system design. In 2015 International Conference on Information and Digital Technologies. Zilina, Slovakia. IEEE.

Lau, H., Dadich, A., Nakandala, D. Evans, H., & Zhao, L. (2018). Development of a cost-optimization model to reduce bottlenecks: A health service case study. Expert Systems, 35(6), e12294.

Liu, Y., Li, Z. Z., Liu, J. X., & Patel, H. (2016). A double standard model for allocating limited emergency medical service vehicle resources ensuring service reliability. Transportation Research Part C: Emerging Technologies, 69, 120–133.

Marianov, V., & Serra, D. (2002). Location problems in the public sector. In Z. Drezner, & H. Hambacher (Eds.), Facility location: Applications and theory (pp. 119–150). Springer.

Marsh, I., Crowley, K., Grube, D., & Eccleston, R. (2017). Delivering public services: locality, learning and reciprocity in place based practice. Australian Journal of Public Administration, 76(4), 443–456.

Nogueira, L. C., Pinto, L. R., & Silva, P. M. S. (2016). Reducing Emergency Medical Service response time via the reallocation of ambulance bases. Health Care Management Science, 19(1), 31–42.

Stanimirovic, Z., Miskovic, S., Trifunovic, D., & Veljovic, V. (2017). A two-phase optimization method for solving the multi-type maximal covering location problem in emergency service networks. Information Technology and Control, 46(1), 100–117.

Staňková, P., Papadaki, Š., & Dvorský, J. (2018). Comparative analysis of the perception of the advantages and disadvantages of hospital horizontal integration. E&M Ekonomie a Management, 21(1), 101–115.

Toro-Diaz, H., Mayorga, M. E., McLay, L. A., Rajagopalan, H. K., & Saydam, C. (2015). Reducing disparities in large-scale emergency medical service systems. Journal of the Operational Research Society, 66(7), 1169–1181.

Vermuyten, H., Rosa, J. N., Marques, I., Belien, J., & Barbosa-Povoa, A. (2018). Integrated staff scheduling at a medical emergency service: An optimisation approach. Expert Systems with Applications, 112, 62–76.