Dynamic location model for designated COVID-19 hospitals in China

Submitted: 9 May 2024
Accepted: 20 September 2024
Published: 29 October 2024
Abstract Views: 468
PDF: 131
Supplementary Materials: 79
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In order to effectively cope with the situation caused by the COVID-19 pandemic, cases should be concentrated in designated medical institutions with full capability to deal with patients infected by this virus. We studied the location of such hospitals dividing the patients into two categories: ordinary and severe. Genetic algorithms were constructed to achieve a three-phase dynamic approach for the location of hospitals designated to receive and treat COVID-19 cases based on the goal of minimizing the cost of construction and operation isolation wards as well as the transportation costs involved. A dynamic location model was established with the decision variables of the corresponding ‘chromosome’ of the genetic algorithms designed so that this goal could be reached. In the static location model, 15 hospitals were required throughout the treatment cycle, whereas the dynamic location model found a requirement of only 11 hospitals. It further showed that hospital construction costs can be reduced by approximately 13.7% and operational costs by approximately 26.7%. A comparison of the genetic algorithm and the Gurobi optimizer gave the genetic algorithm several advantages, such as great convergence and high operational efficiency.

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Alizadeh R, Nishi T, 2020. Hybrid set covering and dynamic modular covering location problem: application to an emergency humanitarian logistics problem. Appl Sci 10:7110. DOI: https://doi.org/10.3390/app10207110
Allman A, Zhang Q, 2020. Dynamic location of modular manufacturing facilities with relocation of individual modules. Eur J Oper Res 286:494-507. DOI: https://doi.org/10.1016/j.ejor.2020.03.045
Amine K, 2019. Multiobjective simulated annealing: Principles and algorithm variants. Adv Oper Res 2019:1-13. DOI: https://doi.org/10.1155/2019/8134674
Aydin N, Seker S, 2021. Determining the location of isolation hospitals for COVID‐19 via Delphi‐based MCDM method. Int J Intell Syst 36:3011-34. DOI: https://doi.org/10.1002/int.22410
Barron AR, Cohen A, Dahmen W, DeVore RA, 2008. Approximation and learning by greedy algorithms. Proj Euclid 36:64-94. DOI: https://doi.org/10.1214/009053607000000631
Beheshti Z, Shamsuddin SMH, 2013. A review of population-based meta-heuristic algorithms. Int J Adv Soft Comput Appl 5:1-35.
Bhattacharjee AK, Mukhopadhyay A, 2023. An improved genetic algorithm with local refinement for solving hierarchical single-allocation hub median facility location problem. Soft Comput 27:1493-509. DOI: https://doi.org/10.1007/s00500-022-07448-3
Chakraborty S, Darbhe K, Sarmah S, 2021. Attended home delivery in Indian public distribution system: an iterated local search approach. J Model Manag 16:1116-37. DOI: https://doi.org/10.1108/JM2-06-2020-0148
Cover T, Hart P, 1967. Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21-7. DOI: https://doi.org/10.1109/TIT.1967.1053964
Cunningham K, Schrage L, 2004. The LINGO algebraic modeling language. In: Kallrath, J. (eds) Modeling Languages in Mathematical Optimization. Applied Optimization 88:159-71. DOI: https://doi.org/10.1007/978-1-4613-0215-5_9
De Armas J, Melián-Batista B, 2015. Variable neighborhood search for a dynamic rich vehicle routing problem with time windows. Comput Ind Eng 85:120-31. DOI: https://doi.org/10.1016/j.cie.2015.03.006
Desale S, Rasool A, Andhale S, Rane P, 2015. Heuristic and meta-heuristic algorithms and their relevance to the real world: a survey. Int J Comput Eng Res Trends 351:296-304.
de Souza EAG, Nagano MS, Rolim GA, 2022. Dynamic Programming algorithms and their applications in machine scheduling: A review. Expert Syst Appl 190:116180. DOI: https://doi.org/10.1016/j.eswa.2021.116180
Dong D, Cui N, 2021. Emergency supply facility location design under disaster conditions: emerging modeling techniques and algorithms. J Phys Conf Ser 1827:012196. DOI: https://doi.org/10.1088/1742-6596/1827/1/012196
Doungpan S, Moryadee S, U-tapao C, Laokhongthavorn Z, 2018. Analysis of Three Emergency Medical Location Models: A Case Study of Thailand. May 2018. Conference: 2018 International Conference on System Science and Engineering (ICSSE) 1-6. DOI: https://doi.org/10.1109/ICSSE.2018.8520208
Fergani C, Idrissi AEBE, Marcotte S, Hajjaji A, 2022. Dynamic location of modular mobile facilities in a hybrid network of hyperconnected and dedicated localisations: application in the chemical industry. Int J Shipp Transp Logist 15:407-34. DOI: https://doi.org/10.1504/IJSTL.2022.126946
Fernandes DR, Rocha C, Aloise D, Ribeiro GM, Santos EM, Silva A, 2014. A simple and effective genetic algorithm for the two-stage capacitated facility location problem. Comput Ind Eng 75:200-8. DOI: https://doi.org/10.1016/j.cie.2014.05.023
Gu J, Zhou Y, Das A, Lee GM, 2018. Medical relief shelter location problem with patient severity under a limited relief budget. Comput Ind Eng 125:720-8. DOI: https://doi.org/10.1016/j.cie.2018.03.027
Hashemi A, Gholami H, Venkatadri U, Karganroudi SS, Khouri S, Wojciechowski A, 2021. A new direct coefficient-based heuristic algorithm for set covering problems. Int J Fuzzy Syst 2:1131-47. DOI: https://doi.org/10.1007/s40815-021-01208-5
Jaramillo JH, BhaduryJ, Batta R, 2002. On the use of genetic algorithms to solve location problems. Comput Oper Res 29:761-79. DOI: https://doi.org/10.1016/S0305-0548(01)00021-1
Katoch S, Chauhan S S, Kumar V, 2021. A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80:8091-126. DOI: https://doi.org/10.1007/s11042-020-10139-6
Liu J, Cao L, Zhang D, Chen Z, Lian X, Li Y, Zhang Y, 2022. Optimization of site selection for emergency medical facilities considering the SEIR model. Comput Intell Neurosci 2022:1912272. DOI: https://doi.org/10.1155/2022/1912272
Luo C, Xing W, Cai S, Hu C, 2022. NuSC: an effective local search algorithm for solving the set covering problem. IEEE Trans Cybern 54:1403-16. DOI: https://doi.org/10.1109/TCYB.2022.3199147
Mladenović M, Hansen P, 1997.Variable neighborhood search. Comput Oper Res 24:1097-100. DOI: https://doi.org/10.1016/S0305-0548(97)00031-2
Ma D, Chu J, Wang Z, Sun T, 2016. Study on disaster shelter complement location problem after disaster shelter failure. World Earthq Eng 32:117-23.
Maleki N, Zeinali Y, Niaki S T A, 2021. A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection. Expert Syst Appl1 64:113981. DOI: https://doi.org/10.1016/j.eswa.2020.113981
Marques MC, Dias JM, 2018. Dynamic location problem under uncertainty with a regret‐based measure of robustness. Int Trans Oper Res 25:1361-81. DOI: https://doi.org/10.1111/itor.12183
Mirjalili S, 2019. Genetic algorithm. In: Evolutionary Algorithms and Neural Networks. Studies in Computational Intelligence 780:43-55. DOI: https://doi.org/10.1007/978-3-319-93025-1_4
Murad A, Faruque F, Naji A, Tiwari A, 2021. Using the location-allocation P-median model for optimising locations for health care centres in the city of Jeddah City, Saudi Arabia. Geospat Health 16:1002. DOI: https://doi.org/10.4081/gh.2021.1002
Nasiri MM, Mahmoodian V, Rahbari A, Farahmand S, 2018. A modified genetic algorithm for the capacitated competitive facility location problem with the partial demand satisfaction. Comput Ind Eng 124:435-48. DOI: https://doi.org/10.1016/j.cie.2018.07.045
Ng KKH, Lee CKM, Chan FTS, Chen C-H, Qin Y, 2020. A two-stage robust optimisation for terminal traffic flow problem. Appl Soft Comput 89:106048. DOI: https://doi.org/10.1016/j.asoc.2019.106048
Oksuz MK, Satoglu SI, 2020. A two-stage stochastic model for location planning of temporary medical centers for disaster response. Int J Disaster Risk Reduct 44:101426. DOI: https://doi.org/10.1016/j.ijdrr.2019.101426
Pourghader Chobar A, Adibi MA, Kazemi A, 2021. A novel multi-objective model for hub location problem considering dynamic demand and environmental issues. J Ind Eng Manag 8:1-31.
Shanmugasundaram N, Sushita K, Kumar SP, Ganesh EN, 2019. Genetic algorithm-based road network design for optimising the vehicle travel distance. Int J Vehicle Inf Commun Syst 4:344-54. DOI: https://doi.org/10.1504/IJVICS.2019.103931
Sitepu R, Puspita FM, Ariani I S, Indrawati I, Yuliza E, Octarina S, 2023. Robust set cover problem in determining the optimal location of emergency units in Palembang city with unknown distance. AIP Conf Proc, 2913(1). doi:10.1063/5.0175708. DOI: https://doi.org/10.1063/5.0175708
Sitepu R, Puspita F M, Romelda S, Fikri A, Susanto B, Kaban H, 2019. Set covering models in optimizing the emergency unit location of health facility in Palembang. J Phys Conf Ser 1282:012008. DOI: https://doi.org/10.1088/1742-6596/1282/1/012008
Xi M, Ye F, Yao Z, Zhao Q, 2013. A modified-median model for the emergency facilities location problem and its variable neighbourhood search-based algorithm. J Appl Math 2013:375657. DOI: https://doi.org/10.1155/2013/375657
Yan L, Grifoll M, Feng H, Zheng P, Zhou C, 2022. Optimization of Urban Distribution Centres: A Multi-Stage Dynamic Location Approach. Sustainability 14;4135. DOI: https://doi.org/10.3390/su14074135
Yu H, Sun X, Solvang WD, Zhao X, 2020. Reverse logistics network design for effective management of medical waste in epidemic outbreaks: Insights from the coronavirus disease 2019 (COVID-19) outbreak in Wuhan (China). Int J Environ Res Public Health 17:1770. DOI: https://doi.org/10.3390/ijerph17051770
Zhang M, Zhang Y, Qiu Z, Wu H, 2019. Two-Stage Covering Location Model for Air–Ground Medical Rescue System. Sustainability 11:3242. DOI: https://doi.org/10.3390/su11123242
Zhou Z, LiF, Zhu H, Xie H, Abawajy JH, Chowdhury MU, 2020. An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments. Neural Comput Appl 32:1531-41. DOI: https://doi.org/10.1007/s00521-019-04119-7

How to Cite

Fei, W., Linghong, Y., Weigang, Z., & Ruihan, Z. (2024). Dynamic location model for designated COVID-19 hospitals in China. Geospatial Health, 19(2). https://doi.org/10.4081/gh.2024.1310