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Hybrid Approach in Bed Planning and Scheduling Decisions: A Literature Review and Future Perspectives

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Proceedings of the 11th International Conference on Production Research – Americas (ICPR 2022)

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Abstract

Hospitals consist of service systems that need to be managed for the efficient use of their resources. Beds are one of the most critical features of these systems, and they are generally scarce due to the costs attributed to their operation. Thus, decisions related to the planning and scheduling of beds must be made using appropriate methods, promoting greater use of these. Due to the uncertainties and complexity inherent to hospital systems, hybrid approaches offer opportunities for application in this context. Based on this, we developed a systematic literature review, aiming to investigate the use of hybrid approaches, using simulation and optimization, applied to the planning and scheduling of beds, conducted using the PRISMA method. As a result, the study presents (i) a content analysis, which shows the techniques used and the predominance of research at the tactical decision level; (ii) the perspectives for future researches, which indicate opportunities for enriching simulation models, conducting research at the operational level, developing structures for the supply and analysis of data and the integration with Artificial Intelligence (AI) techniques.

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References

  1. Aghaabdellahian, Z., Bijari, M.: Bed management considering bed-blocking and elective patient admissions using simulation optimisation. Int. J. Simul. Process. Model. 15, 278–294 (2020)

    Article  Google Scholar 

  2. Booton, R.D., Powell, A.L., Turner, K.M., Wood, R.: Modelling the effect of COVID-19 mass vaccination on acute hospital admissions. Int. J. Qual. Health Care 34(2) (2022). https://doi.org/10.1093/intqhc/mzac031

  3. Banditori, C., Cappanera, P., Visintin, F.: A combined optimization-simulation approach to the master surgical scheduling problem. IMA J. Manag. Math. 24, 155–187 (2013)

    MathSciNet  Google Scholar 

  4. Cappanera, P., Visintin, F., Banditori, C.: Comparing resource balancing criteria in master surgical scheduling: a combined optimisation-simulation approach. Int. J. Prod. Econ. 158, 179–196 (2014)

    Article  Google Scholar 

  5. Proudlove, N.: The 85% bed occupancy fallacy: the use, misuse and insights of queuing theory. Health Serv. Manag. Res. 33(3), 110–121 (2020). https://doi.org/10.1177/0951484819870936

    Article  Google Scholar 

  6. de Souza, N.L.S., Mendes, L.G., Rovaris, E.S., Frazzon, E.M., Braghirolli, L.F.: Integrated production and maintenance planning: a systematic literature review. In: Rossit, D.A., Tohmé, F., Mejía Delgadillo, G. (eds.) Production Research. ICPR-Americas 2020. CCIS, vol. 1407, pp. 342–356. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-76307-7_26

  7. He, L., Madathil, S.C., Oberoi, A., et al.: A systematic review of research design and modeling techniques in inpatient bed management. Comput. Ind. Eng. 127, 451–466 (2019)

    Google Scholar 

  8. Holm, L.B., Lurås, H., Dahl, F.A.: Improving hospital bed utilisation through simulation and optimisation. With application to a 40% increase in patient volume in a Norwegian general hospital. Int. J. Med. Inform. 82, 80–89 (2013)

    Google Scholar 

  9. Jiang, Y., Yang, F., Tang, Z., Li, Q.-L.: Admission control of hospitalization with patient gender by using Markov decision process. Int. Trans. Oper. Res. 30(1), 70–98 (2023). Special Issue: Operations Research in Healthcare

    Google Scholar 

  10. Kuck, M., Broda, E., Freitag, M., et al.: Towards adaptive simulation-based optimization to select individual dispatching rules for production control. In: Proceedings - Winter Simulation Conference, pp. 3852–3863 (2017)

    Google Scholar 

  11. Landa, P., Tànfani, E., Testi, A.: Simulation and optimization for bed re-organization at a surgery department. In: SIMULTECH 2013 - Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications. Reykjavik, pp. 584–594 (2013)

    Google Scholar 

  12. Li, N., Pan, J., Xie, X.: Operational decision making for a referral coordination alliance- When should patients be referred and where should they be referred to? Omega (United Kingdom) 96 (2020)

    Google Scholar 

  13. Liao, Y., Deschamps, F., Loures, E., Ramos, L.F.P.: Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. Int. J. Prod. Res. 55, 3609–3629 (2017)

    Google Scholar 

  14. Lin, J.T., Chen, C.-M.: Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing. Simul. Model. Pract. Theory 51, 100–114 (2015)

    Article  Google Scholar 

  15. Luo, L., Li, J., Xu, X., Shen, W., Xiao, L.: A data-driven hybrid three-stage framework for hospital bed allocation: a case study in a large tertiary hospital in China. Comput. Math. Methods Med. 2019, 12 (2019). https://doi.org/10.1155/2019/7370231. Article ID 7370231

  16. Magazine, M., Murphy, M., Schauer, D., Wiggermann, N.: Determining the number of bariatric beds needed in a U.S. acute care hospital. Heal Environ. Res. Des. J. 14, 14–26. (2021)

    Google Scholar 

  17. Mallor, F., Azcárate, C., Barado, J.: Control problems and management policies in health systems: application to intensive care units. Flex Serv. Manuf. J. 28, 62–89 (2016)

    Google Scholar 

  18. Mohamed, I., Hussein, R.: A simulation optimisation approach for managing bed capacity in an intensive care unit. J. Inf. Knowl. Manag. 20(1), 2150001 (2021). https://doi.org/10.1142/S0219649221500015

  19. Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6, e1000097 (2009)

    Article  Google Scholar 

  20. Oakley, D., Onggo, B.S., Worthington, D.: Symbiotic simulation for the operational management of inpatient beds: model development and validation using Δ-method. Health Care Manag. Sci. 23(1), 153–169 (2019). https://doi.org/10.1007/s10729-019-09485-1

    Article  Google Scholar 

  21. Ordu, M., Demir, E., Davari, S.: A hybrid analytical model for an entire hospital resource optimisation. Soft. Comput. 25(17), 11673–11690 (2021). https://doi.org/10.1007/s00500-021-06072-x

    Article  Google Scholar 

  22. Pimentel, R., Santos, P., Carreirão Danielli A.M., et al.: Towards an Adaptive Simulation-Based Optimization Framework for the Production Scheduling of Digital Industries (2018)

    Google Scholar 

  23. Pires, M., Frazzon, E., Carreirão Danielli, A.M., et al.: Towards a simulation-based optimization approach to integrate supply chain planning and control. In: Procedia CIRP, pp. 520–525 (2018)

    Google Scholar 

  24. Prodel, M., Augusto, V., Xie, X.: Hospitalization admission control of emergency patients using Markovian decision processes and discrete event simulation. In: Tolk, A., Yilmaz, L., DSYRIO (ed.) Proceedings - Winter Simulation Conference. Institute of Electrical and Electronics Engineers Inc., pp. 1433–1444 (2015)

    Google Scholar 

  25. Saadouli, H., Jerbi, B., Dammak, A., et al.: A stochastic optimization and simulation approach for scheduling operating rooms and recovery beds in an orthopedic surgery department. Comput. Ind. Eng. 80, 72–79 (2015)

    Article  Google Scholar 

  26. Saremi, A., Jula, P., Elmekkawy, T., Wang, G.: Appointment scheduling of outpatient surgical services in a multistage operating room department. Int. J. Prod. Econ. 141, 646–658 (2013)

    Article  Google Scholar 

  27. Uhlmann, I.R., Frazzon, E.M.: Production rescheduling review: opportunities for industrial integration and practical applications. J. Manuf. Syst. 49, 186–193 (2018)

    Article  Google Scholar 

  28. Wang, X., Gong, X., Geng, N., et al.: Metamodel-based simulation optimisation for bed allocation. Int. J. Prod. Res. 58, 6315–6335 (2020)

    Article  Google Scholar 

  29. Wu, K., Zhu, X., Zhang, R., Liu, S.: Hospital bed planning in a single department based on Monte Carlo simulation and queuing theory. In: 2019 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE Computer Society, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, China, pp. 644–648 (2019a)

    Google Scholar 

  30. Ebinger, J., et al.: A machine learning algorithm predicts duration of hospitalization in COVID-19 patients. Intell.-Based Med. 5, 100035 (2021). https://doi.org/10.1016/j.ibmed.2021.100035

    Article  Google Scholar 

  31. Joy, M.P., Jones, S: Predicting bed demand in a hospital using neural networks and ARIMA models: a hybrid approach. In: ESANN, vol. 2005, p. 13th (2005)

    Google Scholar 

  32. Pendharkar, P.C., Khurana, H.: Machine learning techniques for predicting hospital length of stay in Pennsylvania federal and specialty hospitals. Int. J. Comput. Sci. Appl. 11(3) (2014)

    Google Scholar 

  33. Afrash, M.R., Kazemi-Arpanahi, H., Ranjbar, P., Nopour, R., Amraei, M., Saki, M., Shanbehzadeh, M.: Predictive modeling of hospital length of stay in COVID-19 patients using machine learning algorithms. J. Med. Chem. Sci. 4(5), 525–537 (2021). https://doi.org/10.26655/JMCHEMSCI.2021.5.15

  34. Tello, M., et al.: Machine learning based forecast for the prediction of inpatient bed demand. BMC Med. Inform. Decis. Mak. 22, 55 (2022). https://doi.org/10.1186/s12911-022-01787-9

    Article  Google Scholar 

  35. Bergmann, S., Feldkamp, N., Strassburger, S.: Emulation of control strategies through machine learning in manufacturing simulations. J. Simul. 11(1), 38–50 (2017). https://doi.org/10.1057/s41273-016-0006-0

    Article  Google Scholar 

  36. Takeda-Berger, S.L., Frazzon, E.M., Broda, E., Freitag, M.: Machine learning in production scheduling: an overview of the academic literature. In: Freitag, M., Haasis, H.-D., Kotzab, H., Pannek, J. (eds.) LDIC 2020. LNL, pp. 409–419. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44783-0_39

    Chapter  Google Scholar 

  37. Lucidi, S., Maurici, M., Paulon, L., Rinaldi, F., Roma, M.: A derivative-free approach for a simulation-based optimization problem in healthcare. Optim. Lett. 10(2), 219–235 (2015). https://doi.org/10.1007/s11590-015-0905-4

    Article  MathSciNet  Google Scholar 

  38. Zhou, L., Geng, N., Jiang, Z., Wang, X.: Multi-objective capacity allocation of hospital wards combining revenue and equity. Omega 81, 220–233 (2018). https://doi.org/10.1016/j.omega.2017.11.005

    Article  Google Scholar 

  39. Saadouli, H., Jerbi, B., Dammak, A., Masmoudi, L., Bouaziz, A.: A stochastic optimization and simulation approach for scheduling operating rooms and recovery beds in an orthopedic surgery department. Comput. Ind. Eng. 80, 72–79 (2015). https://doi.org/10.1016/j.cie.2014.11.021

    Article  Google Scholar 

  40. Campos, A., Gabriel, G., Torres, A., Santos, C., Montevechi, J.: Integrating computer simulation and the normalized normal constraint method to plan a temporary hospital for COVID-19 patients. J. Oper. Res. Soc. 1–12 (2022). https://doi.org/10.1080/01605682.2022.2083989

  41. Van den Broek d’Obrenan, A., Ridder, A., Roubos, D., Stougie, L.: Minimizing bed occupancy variance by scheduling patients under uncertainty. Eur. J. Oper. Res. 286(1), 336–349 (2020). https://doi.org/10.1016/j.ejor.2020.03.026

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Production Engineering Graduate Program, Federal University of Santa Catarina, Florianopolis, SC, 88040-900, Brazil

    Lúcio Galvão Mendes, Danilo Ribamar Sá Ribeiro & Enzo Morosini Frazzon

Authors
  1. Lúcio Galvão Mendes
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  2. Danilo Ribamar Sá Ribeiro
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  3. Enzo Morosini Frazzon
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Correspondence to Lúcio Galvão Mendes .

Editor information

Editors and Affiliations

  1. Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifícia Universidade Catolica do Parana, Curitiba, Brazil

    Fernando Deschamps

  2. Industrial and Systems Engineering Graduate Program (PPGEPS), Universidade Tecnologica Federal do Parana, Curitiba, Paraná, Brazil

    Edson Pinheiro de Lima

  3. Industrial and Systems Engineering Graduate Program (PPGEPS), Universidade Tecnologica Federal do Parana, Curitiba, Paraná, Brazil

    Sérgio E. Gouvêa da Costa

  4. Industrial and Systems Engineering Graduate Program (PPGEPS), Universidade Tecnologica Federal do Parana, Curitiba, Paraná, Brazil

    Marcelo G. Trentin

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Mendes, L.G., Sá Ribeiro, D.R., Frazzon, E.M. (2023). Hybrid Approach in Bed Planning and Scheduling Decisions: A Literature Review and Future Perspectives. In: Deschamps, F., Pinheiro de Lima, E., Gouvêa da Costa, S.E., G. Trentin, M. (eds) Proceedings of the 11th International Conference on Production Research – Americas. ICPR 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-36121-0_67

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  • DOI: https://doi.org/10.1007/978-3-031-36121-0_67

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