Abstract
In this paper, we investigate a novel physician scheduling problem in the Mobile Cabin Hospitals (MCH) which are constructed in Wuhan, China during the outbreak of the Covid-19 pandemic. The shortage of physicians and the surge of patients brought great challenges for physicians scheduling in MCH. The purpose of the studied problem is to get an approximately optimal schedule that reaches the minimum workload for physicians on the premise of satisfying the service requirements of patients as much as possible. We propose a novel hybrid algorithm integrating particle swarm optimization (PSO) and variable neighborhood descent (VND) (named as PSO-VND) to find the approximate global optimal solution. A self-adaptive mechanism is developed to choose the updating operators dynamically during the procedures. Based on the special features of the problem, three neighborhood structures are designed and searched in VND to improve the solution. The experimental comparisons show that the proposed PSO-VND has a significant performance increase than the other competitors.
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World Health Organization, Coronavirus disease 2019 (COVID-19) Situation Report-97. https://www.who.int/docs/default-source/528coronaviruse/situation-reports/20200426-sitrep-97-covid-19.pdf. Accessed 26 April 2020
Yuki, K., Fujiogi, M., Koutsogiannaki, S.: COVID-19 pathophysiology: A review. Clin. Immunol. 215, 108427 (2020)
Ma, X., Zhao, X., Guo, P.: Cope with the COVID-19 pandemic: Dynamic bed allocation and patient subsidization in a public healthcare system. Int. J. Prod. Econ. 243, 108320 (2022)
Zhang, J., Wang, M., Zhao, M., Guo, S., Xu, Y., Ye, J., Ding, W., Wang, Z., Ye, D., Pan, W., Liu, M., Li, D., Luo, Z., Liu, J., Wan, J.: The Clinical Characteristics and Prognosis Factors of Mild-Moderate Patients With COVID-19 in a Mobile Cabin Hospital: A Retrospective, Single-Center Study. Front. Public Health. 8, 1–11 (2020)
Wang, B., Wang, Z., Zhao, J., Zeng, X., Wu, M., Wang, S., Wang, T.: Epidemiological and clinical course of 483 patients with COVID-19 in Wuhan, China: a single-center, retrospective study from the mobile cabin hospital. Eur. J. Clin. Microbiol. Infect. Dis. 39, 2309–2315 (2020)
Liu, P., Zhang, H., Long, X., Wang, W., Zhan, D., Meng, X., Li, D., Wang, L., Chen, R.: Management of COVID-19 patients in Fangcang shelter hospital: clinical practice and effectiveness analysis. Clin. Respir. J. 15, 280–286 (2021)
Zhang, X., Huang, D.S., Guan, P.: Nursing scheduling mode and experience from the medical teams in aiding hubei province during the covid-19 outbreak: A systematic scoping review of 17 studies. Risk Manag. Healthc. Policy. 14, 1805–1813 (2021)
Lan, S., Fan, W., Shao, K., Yang, S., Pardalos, P.M.: Medical Staff Scheduling Problem in Chinese Mobile Cabin Hospitals During Covid-19 Outbreak. In: Simos, D.E., Pardalos, P.M., and Kotsireas, I.S. (eds) Learning and Intelligent Optimization. LION 2021. Lecture Notes in Computer Science, 12931, 211–218. Springer International Publishing, Cham (2021)
Faes, C., Abrams, S., Van Beckhoven, D., Meyfroidt, G., Vlieghe, E., Hens, N.: Time between Symptom Onset, Hospitalisation and Recovery or Death: Statistical Analysis of Belgian COVID-19 Patients. Int. J. Environ. Res. Public Health. 17, 7560 (2020)
Vassilacopoulos, G.: Allocating Doctors to Shifts in an Accident and Emergency Department. J. Oper. Res. Soc. 36, 517–523 (1985)
Erhard, M., Schoenfelder, J., Fügener, A., Brunner, J.O.: State of the art in physician scheduling. Eur. J. Oper. Res. 265, 1–18 (2018)
Mansini, R., Zanotti, R.: Optimizing the physician scheduling problem in a large hospital ward. J. Sched. 23, 337–361 (2020)
EL-Rifai, O., Garaix, T., Augusto, V., Xie, X.: A stochastic optimization model for shift scheduling in emergency departments. Health Care Manag. Sci. 18, 289–302 (2015)
Tohidi, M., Kazemi Zanjani, M., Contreras, I.: Integrated physician and clinic scheduling in ambulatory polyclinics. J. Oper. Res. Soc. 70, 177–191 (2019)
Van Huele, C., Vanhoucke, M.: Analysis of the integration of the physician rostering problem and the surgery scheduling problem topical collection on systems-level quality improvement. J. Med. Syst. 38 (2014)
Bard, J.F., Shu, Z., Morrice, D.J., Leykum, L.K.: Annual block scheduling for internal medicine residents with 4+1 templates. J. Oper. Res. Soc. 67, 911–927 (2016)
Franz, L.S., Miller, J.L.: Scheduling Medical Residents to Rotations: Solving the Large-Scale Multiperiod Staff Assignment Problem. Oper. Res. 41, 269–279 (1993)
Kraul, S.: Annual scheduling for anesthesiology medicine residents in task-related programs with a focus on continuity of care. Flex. Serv. Manuf. J. 32, 181–212 (2020)
Kraul, S., Fügener, A., Brunner, J.O., Blobner, M.: A robust framework for task-related resident scheduling. Eur. J. Oper. Res. 276, 656–675 (2019)
Patrick, J., Montazeri, A., Michalowski, W., Banerjee, D.: Automated Pathologist Scheduling at The Ottawa Hospital. INFORMS J. Appl. Anal. 49, 93–103 (2019)
Wickert, T.I., Kummer Neto, A.F., Boniatti, M.M., Buriol, L.S.: An integer programming approach for the physician rostering problem. Ann. Oper. Res. 302, 363–390 (2021)
Güler, M.G., Geçici, E.: A decision support system for scheduling the shifts of physicians during COVID-19 pandemic. Comput. Ind. Eng. 150, 106874 (2020)
Liu, R., Fan, X., Wu, Z., Pang, B., Xie, X.: The Physician Scheduling of Fever Clinic in the Covid-19 Pandemic. IEEE Trans. Autom. Sci. Eng. 1–15 (2021)
Zucchi, G., Iori, M., Subramanian, A.: Personnel scheduling during Covid-19 pandemic. Optim. Lett. 15, 1385–1396 (2020)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks. pp. 1942–1948. IEEE (1995)
Tharwat, A., Schenck, W.: A conceptual and practical comparison of PSO-style optimization algorithms. Expert Syst. Appl. 167, 114430 (2021)
Tasgetiren, M.F., Liang, Y.C., Sevkli, M., Gencyilmaz, G.: A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. Eur. J. Oper. Res. 177, 1930–1947 (2007)
Wu, T.H., Yeh, J.Y., Lee, Y.M.: A particle swarm optimization approach with refinement procedure for nurse rostering problem. Comput. Oper. Res. 54, 52–63 (2015)
Bozorgi-Amiri, A., Jabalameli, M.S., Alinaghian, M., Heydari, M.: A modified particle swarm optimization for disaster relief logistics under uncertain environment. Int. J. Adv. Manuf. Technol. 60, 357–371 (2012)
Hidayati, M., Wibowo, A.: Multi-Objective Physician Scheduling using Native Binary Particle Swarm Optimization and Its Variance. Int. J. Recent Technol. Eng. 8, 5230–5243 (2019)
Caporossi, G., Hansen, P., Mladenović, N., Hansen, P.: Variable Neighborhood Search. In: Siarry, P. (ed.) Computers & Operations Research, pp. 1097–1100. Springer International Publishing, Cham (1997)
Hansen, P., Mladenović, N., Moreno Pérez, J.A.: Variable neighbourhood search: methods and applications. Ann. Oper. Res. 175, 367–407 (2010)
Mladenović, N., Alkandari, A., Pei, J., Todosijević, R., Pardalos, P.M.: Less is more approach: basic variable neighborhood search for the obnoxious p -median problem. Int. Trans. Oper. Res. 27, 480–493 (2020)
Pei, J., Dražić, Z., Dražić, M., Mladenović, N., Pardalos, P.M.: Continuous Variable Neighborhood Search (C-VNS) for Solving Systems of Nonlinear Equations. INFORMS J. Comput. 31, 235–250 (2019)
Pěnička, R., Faigl, J., Saska, M.: Variable Neighborhood Search for the Set Orienteering Problem and its application to other Orienteering Problem variants. Eur. J. Oper. Res. 276, 816–825 (2019)
Pei, J., Mladenović, N., Urošević, D., Brimberg, J., Liu, X.: Solving the traveling repairman problem with profits: A Novel variable neighborhood search approach. Inf. Sci. (Ny). 507, 108–123 (2020)
Ranjbar, M., Saber, R.G.: A variable neighborhood search algorithm for transshipment scheduling of multi products at a single station. Appl. Soft Comput. 98, 106736 (2021)
Wilson, D.T., Hawe, G.I., Coates, G., Crouch, R.S.: A multi-objective combinatorial model of casualty processing in major incident response. Eur. J. Oper. Res. 230, 643–655 (2013)
Molenbruch, Y., Braekers, K., Caris, A., Vanden Berghe, G.: Multi-directional local search for a bi-objective dial-a-ride problem in patient transportation. Comput. Oper. Res. 77, 58–71 (2017)
Christopher, Y., Wahyuningsih, S., Satyananda, D.: Study of variable neighborhood descent and tabu search algorithm in VRPSDP. J. Phys. Conf. Ser. 1872 (2021)
Pan, Q.K., Fatih Tasgetiren, M., Liang, Y.C.: A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem. Comput. Oper. Res. 35, 2807–2839 (2008)
Tasgetiren, M.F., Suganthan, P.N., Pan, Q.-Q.: A discrete particle swarm optimization algorithm for the generalized traveling salesman problem. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO’07. p. 158. ACM Press, New York, New York, USA (2007)
Chen, Y.Y., Cheng, C.Y., Wang, L.C., Chen, T.L.: A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems - A case study for solar cell industry. Int. J. Prod. Econ. 141, 66–78 (2013)
Kadlec, P., Sedenka, V., Stumpf, M., Marek, M.: Solution of an inverse scattering problem using optimization with a variable number of dimensions. Proc. 2017 19th Int. Conf. Electromagn. Adv. Appl. ICEAA 2017. 976–979 (2017)
Mota, F.O., Wanner, E.F., Luz, E.J.S., Moreira, G.J.P.: VND-based Local Search Operator for Equality Constraint Problems in PSO Algorithm. Electron Notes Discrete Math. 66, 111–118 (2018)
Goksal, F.P., Karaoglan, I., Altiparmak, F.: A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery. Comput. Ind. Eng. 65, 39–53 (2013)
Wang, S., Li, Y., Zhang, Y.: A hybrid PSO algorithm for vehicle routing problem with simultaneous delivery and pickup. Adv. Mater. Res. 655–657, 2326–2330 (2013)
Kiran, M.S.: Particle swarm optimization with a new update mechanism. Appl. Soft Comput. J. 60, 670–678 (2017)
Lin, S.W., Cheng, C.Y., Pourhejazy, P., Ying, K.C.: Multi-temperature simulated annealing for optimizing mixed-blocking permutation flowshop scheduling problems. Expert Syst. Appl. 165, 113837 (2021)
Mirjalili, S.M., Mirjalili, S.M., Hatamlou, A.: Multi-Verse Optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Applic. 27, 495–513 (2016)
Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A.A., Al-qaness, M.A.A., Gandomi, A.H.: Aquila Optimizer: A novel meta-heuristic optimization algorithm. Comput. Ind. Eng. 157, 107250 (2021)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (Nos. 72071057, 71922009, 71871080, and 72188101).
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Lan, S., Fan, W., Yang, S. et al. Physician scheduling problem in Mobile Cabin Hospitals of China during Covid-19 outbreak. Ann Math Artif Intell 91, 349–372 (2023). https://doi.org/10.1007/s10472-023-09834-5
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DOI: https://doi.org/10.1007/s10472-023-09834-5