Development of a discrete locating model for the healthcare facilities considering efficiency and readiness

Mohammadreza Alikhasi, Mohammadreza Vasili


The locating of facilities is one of the main issues in the field of operation research, and has always been a concern for many societies due to the very important role in controlling costs, quality and access to commodity and services. The use of modern scientific tools for locating in developed countries is very common and is considered as  a solution to avoid mistakes in the organization of services and production. In developing countries, these tools are also very effective in improving the ability of communities. The result of the application of modern science in locating, anywhere around the world, is well established in the quality of service and the satisfaction of suppliers and applicants. In the implementation of locating, the demand of individuals in the communities is considered as dynamic and static. In the static facilities area due to the high relative dimensions and, in principle, the impossibility of low cost relocation, locating decisions assign high importance and precision. Given influence of the efficiency and readiness of a healthcare center such as a hospital in choosing new services and locations for establishing or maintaining a service, can be very helpful and will prevent future mistakes and adjust previous preferences. The most important question that has been answered in this study is the impact of the effectioncy and services in locating and allocating services and re-examining hospital capacities. By measuring the efficiency and readiness indexes through data envelopment analysis models and ranking the provided services, a more precise decision can be made and a review of the applied policies can be considered. Therefore, in this paper, a model is proposed with consideration of different services for a hospital and quality for each service, in which it seeks to minimize operational and maintenance costs and, as well as, maximize the quality and efficiency. The goal of this model is to provide an approach for optimal location of the centers, which reduces investment and operational costs. The proposed model is solved by the modified Epsilon Limit Approach. The results indicate the proper functioning of the system after the implementation of the proposed model.


Facilities locating - Healthcare facilities - Efficiency - Readiness - Data Envelopment Analysis

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 Akkihal, A. (2006). Inventory pre-positioning for humanitarian logistics. MS thesis, Massachusetts Institute of Technology.

 Altay, N., and Green, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475–493.

 Nedjati, A, Izbirak, G, and Arkat, J. (2017). Bi-objective covering tour location routing problem with replenishment at intermediate depots: Formulation and Meta-heuristics. Computers & Industrial Engineering, 110, 191-206.

 Murali,P. Ordóñez,F. and Dessouky, M. M. M. (2012). Facility location under demand uncertainty: Response to a large-scale bio-terror attack. Socioecon. Plann. Sci., vol. 46, no. 1, 78–87.

 YinP. and Mu,L.(2012). Modular capacitated maximal covering location problem for the optimal siting of emergency vehicles. Appl. Geogr., vol. 34, 247–254.

 Wei,L. Li, W. Li,K. Liu,H. and Cheng, L.(2012). Decision Support for Urban Shelter Locations Based on Covering Model. Procedia Eng., vol. 43, pp. 59–64.

 Schiffer, M, Walther, G. (2017). The electric location routing problem with time windows and partial recharging, European Journal of Operational Research.

 Berman O. , Krass D., and M. B. C. Menezes (2013). Location and reliability problems on a line: Impact of objectives and correlated failures on optimal location patterns. Omega, vol. 41, no. 4, 766–779.

 Ghaderi A. and Jabalameli, M. M. S. Modeling the budget-constrained dynamic uncapacitated facility location–network design problem and solving it via two efficient heuristics: A case study of health care. (2013). Math. Comput. Model., vol. 57, no. 3–4, 382–400.

 Q. Li, B. Zeng, and A. Savachkin, “Reliable facility location design under disruptions,” Comput. Oper. Res., vol. 40, no. 4, pp. 901–909, Apr. 2017.

 C.-C. Lu and J.-B. Sheu, “Robust vertex p-center model for locating urgent relief distribution centers,” Comput. Oper. Res., vol. 40, no. 8, pp. 2128–2137, Aug. 2017.

 H. Toro-díaz, M. E. Mayorga, S. Chanta, and L. a. Mclay, “Joint location and dispatching decisions for Emergency Medical Services,” Comput. Ind. Eng., vol. 64, no. 4, pp. 917–928, Apr. 2017.

 P. Mitropoulos, I. Mitropoulos, and I. Giannikos, “Computers & Operations Research Combining DEA with location analysis for the effective consolidation of services in the health sector,” Comput. Oper. Res., vol. 40, no. 9, pp. 2241–2250, 2017.

 Klimberg R. and Ratick,S. (2008).Modeling data envelopment analysis (DEA) efficient location/allocation decisions. Comput. Oper. Res.

 Boonmee C,Arimura M, Asada T. Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction 2017; 24: 485-498.

 Seyed Jafar Sadjad,iMehdi Heidari,Amin Alinezhad Esboei,Augmented ε-constraint method in multiobjective staff scheduling problem: a case study, The International Journal of Advanced Manufacturing Technology 2014, Volume 70, Issue 5–8, pp 1505–1514

 Hui Li, Jingxiao Zhang,Chao Wang,Yujie Wang,Vaughan Coffey, An evaluation of the impact of environmental regulation on the efficiency of technology innovation using the combined DEA model: A case study of Xi’an, China, Sustainable Cities and Society, Volume 42, October 2018, Pages 355-369

 Zhao Xin-gang,We iZhen, The technical efficiency of China's wind power list enterprises: An estimation based on DEA method and micro-data, Renewable Energy, Volume 133, April 2019, Pages 470-479

 Alireza Tajbakhsh, AzamSham, A facility location problem for sustainability-conscious power generation decision makers, Journal of Environmental Management Volume 230, 15 January 2019, Pages 319-334

 Hong, X. Lejeune, MA. and Noyan, N. (2015). “Stochastic network design for disaster preparedness. IIE Transactions, 47(4), 329–357.

 Nagurney, A. Alvarez Flores, a Soylu. C. (2016). A Generalized Nash Equilibrium network model for post-disaster humanitarian relief, Transportation research part E: logistics and transportation review, VOL95, 1-18



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