Healthcare Districting Optimization Using Gray Wolf Optimizer and Ant Lion Optimizer Algorithms (case study: South Khorasan Healthcare System in Iran)

Journal of Optimization in Industrial Engineering

مقاله 11، دوره 12، شماره 1 - شماره پیاپی 25، خرداد 2019، صفحه 119-131   

نوع مقاله: Original Manuscript

شناسه دیجیتال (DOI): 10.22094/joie.2018.766.1489

نویسندگان

Hiwa Farughi^* ؛ Sobhan Mostafayi؛ Jamal Arkat

Department of Industrial Engineering, University of Kurdistan, Pasdaran Boulevard, Sanandaj, Iran

چکیده

In this paper, the problem of population districting in the health system of South Khorasan province has been investigated in the form of an optimization problem. Now that the districting problem is considered as a strategic matter, it is vital to obtain efficient solutions in order to implement in the system. Therefore in this study two meta-heuristic algorithms, Ant Lion Optimizer (ALO) and Grey Wolf Optimizer (GWO), have been applied to solve the problem in the dimensions of the real world. The objective function of the problem is to maximize the population balance in each district. Problem constraints include unique assignment as well as non-existent allocation of abnormalities. Abnormal allocation means compactness, lack of contiguous, and absence of holes in the districts. According to the obtained results, GWO has a higher level of performance than the ALO. The results of this problem can be applied as a useful scientific tool for districting in other organizations and fields of application.

کلیدواژه‌ها

Healthcare system؛ Districting problem؛ Ant Lion Optimizer؛ Grey Wolf Optimizer

مراجع

Benzarti, E., Sahin, E., & Dallery, Y. (2010). Modelling approaches for the home health care districting problem. In 8th International Conference of Modeling and Simulation-MOSIM (pp. 10-12).

Benzarti, E., Sahin, E., & Dallery, Y. (2013). Operations management applied to home care services: Analysis of the districting problem. Decision Support Systems, 55(2), 587-598.

Bergey, P. K., Ragsdale, C. T., & Hoskote, M. (2003). A simulated annealing genetic algorithm for the electrical power districting problem. Annals of Operations Research, 121(1-4), 33-55.

Blais, M., Lapierre, S. D., & Laporte, G. (2003). Solving a home-care districting problem in an urban setting. Journal of the Operational Research Society, 54(11), 1141-1147.

Bozkaya, B., Erkut, E., & Laporte, G. (2003). A tabu search heuristic and adaptive memory procedure for political districting. European Journal of Operational Research, 144(1), 12-26.

Contreras, I., Fernández, E., & Reinelt, G. (2012). Minimizing the maximum travel time in a combined model of facility location and network design. Omega, 40(6), 847-860.

D'Amico, S. J., Wang, S. J., Batta, R., & Rump, C. M. (2002). A simulated annealing approach to police district design. Computers & Operations Research, 29(6), 667-684.

Datta, D., Figueira, J. R., Gourtani, A. M., & Morton, A. (2013). Optimal administrative geographies: an algorithmic approach. Socio-Economic Planning Sciences, 47(3), 247-257.

Datta, D., Fonseca, C. M., & Deb, K. (2008). A multi-objective evolutionary algorithm to exploit the similarities of resource allocation problems. Journal of Scheduling, 11(6), 405.

De Assis, L. S., Franca, P. M., & Usberti, F. L. (2014). A redistricting problem applied to meter reading in power distribution networks. Computers & Operations Research, 41, 65-75.

Galvão, L. C., Novaes, A. G., De Cursi, J. S., & Souza, J. C. (2006). A multiplicatively-weighted Voronoi diagram approach to logistics districting. Computers & Operations Research, 33(1), 93-114.

Ghiggi, C., Puliafito, P. P., & Zoppoli, R. (1975, September). A combinatorial method for health-care districting. In IFIP Technical Conference on Optimization Techniques (pp. 116-130). Springer, Berlin, Heidelberg.

Grefenstette, J. J. (Ed.). (2013). Genetic algorithms and their applications: proceedings of the second international conference on genetic algorithms. Psychology Press.

Gutiérrez-Gutiérrez, E. V., & Vidal, C. J. (2015). A home health care districting problem in a rapid-growing city. Ingeniería y Universidad, 19(1), 87-113.

Kim, J., Hwang, I., Kim, Y. H., & Moon, B. R. (2011, July). Genetic approaches for graph partitioning: a survey. In Proceedings of the 13th annual conference on Genetic and evolutionary computation (pp. 473-480). ACM.

Knight, V. A., Harper, P. R., & Smith, L. (2012). Ambulance allocation for maximal survival with heterogeneous outcome measures. Omega, 40(6), 918-926.

Li, W., Church, R. L., & Goodchild, M. F. (2014). An extendable heuristic framework to solve the p-compact-regions problem for urban economic modeling. Computers, Environment and Urban Systems, 43, 1-13.

Lin, R. C., Sir, M. Y., & Pasupathy, K. S. (2013). Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services. Omega, 41(5), 881-892.

Liu, R., Xie, X., & Garaix, T. (2014). Hybridization of tabu search with feasible and infeasible local searches for periodic home health care logistics. Omega, 47, 17-32.

Minciardi, R., Puliafito, P. P., & Zoppoli, R. (1981). A districting procedure for social organizations. European Journal of Operational Research, 8(1), 47-57.

Muyldermans, L., Cattrysse, D., Van Oudheusden, D., & Lotan, T. (2002). Districting for salt spreading operations. European Journal of Operational Research, 139(3), 521-532.

Novaes, A. G., de Cursi, J. S., da Silva, A. C., & Souza, J. C. (2009). Solving continuous location–districting problems with Voronoi diagrams. Computers & Operations Research, 36(1), 40-59.

Pezzella, F., Bonanno, R., & Nicoletti, B. (1981). System Approach to the Optimal Health-Care Districting. EUROP. J. OPER. RES., 8(2), 139-146.

Ricca, F., & Simeone, B. (2008). Local search algorithms for political districting. European Journal of Operational Research, 189(3), 1409-1426.

Ríos-Mercado, R. Z., & López-Pérez, J. F. (2013). Commercial territory design planning with realignment and disjoint assignment requirements. Omega, 41(3), 525-535.

Salazar-Aguilar, M. A., Ríos-Mercado, R. Z., & González-Velarde, J. L. (2011). A bi-objective programming model for designing compact and balanced territories in commercial districting. Transportation Research Part C: Emerging Technologies, 19(5), 885-895.

Shirabe, T. (2012). Prescriptive modeling with map algebra for multi-zone allocation with size constraints. Computers, Environment and Urban Systems, 36(5), 456-469.

Steiner, M. T. A., Datta, D., Neto, P. J. S., Scarpin, C. T., & Figueira, J. R. (2015). Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil. Omega, 52, 53-64.

Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.

Tavares-Pereira, F., Figueira, J. R., Mousseau, V., & Roy, B. (2007). Multiple criteria districting problems. Annals of Operations Research, 154(1), 69-92.