Skip to main content
SpringerLink
Log in

Scheduling of Patients in Emergency Departments with a Variable Neighborhood Search

  • Conference paper
  • First Online:
Variable Neighborhood Search (ICVNS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12559))

Included in the following conference series:

Abstract

The dynamic scheduling of patients to doctors in an emergency department environment is tackled in this work. We consider the case in which patients arrive dynamically during the working hours, and the objective is to minimize the weighted tardiness. We propose a greedy heuristic based on priority queues and a general variable neighborhood search (GVNS). In the greedy heuristic, patients are scheduled by observing their urgency, while in the GVNS, the schedule is optimized every time a patient arrives. The GVNS uses six neighborhood structures and a variable neighborhood descent to perform the local search. The GVNS also handles the static problem whose solution can be used as a reference for the dynamic one. Computational results on 80 instances show that using the GVNS better approximates the static problem, besides giving an overall reduction of 66.8% points over the greedy heuristic.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Baptiste, P., Jouglet, A., Savourey, D.: Lower bounds for parallel machine scheduling problems. Int. J. Oper. Res. 3(6), 643–664 (2008)

    Article  MathSciNet  Google Scholar 

  2. Bulhões, T., Sadykov, R., Subramanian, A., Uchoa, E.: On the exact solution of a large class of parallel machine scheduling problems. J. Sched. 23(4), 411–429 (2020). https://doi.org/10.1007/s10951-020-00640-z

    Article  MathSciNet  MATH  Google Scholar 

  3. Burke, E.K., Li, J., Qu, R.: A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems. Eur. J. Oper. Res. 203(2), 484–493 (2010)

    Article  MathSciNet  Google Scholar 

  4. Dellaert, N., Jeunet, J.: A variable neighborhood search algorithm for the surgery tactical planning problem. Comput. Oper. Res. 84, 216–225 (2017)

    Article  MathSciNet  Google Scholar 

  5. Derlet, R.W., Richards, J.R.: Overcrowding in the nation’s emergency departments: complex causes and disturbing effects. Ann. Emerg. Med. 35(1), 63–68 (2000)

    Article  Google Scholar 

  6. Di Somma, S., Paladino, L., Vaughan, L., Lalle, I., Magrini, L., Magnanti, M.: Overcrowding in emergency department: an international issue. Intern. Emerg. Med. 10(2), 171–175 (2014). https://doi.org/10.1007/s11739-014-1154-8

    Article  Google Scholar 

  7. Ding, Y., Park, E., Nagarajan, M., Grafstein, E.: Patient prioritization in emergency department triage systems: an empirical study of the canadian triage and acuity scale (CTAS). Manuf. Serv. Oper. Manag. 21(4), 723–741 (2019)

    Article  Google Scholar 

  8. Fernandes, C.M., et al.: Five-level triage: a report from the ACEP/ENA five-level triage task force. J. Emerg. Nurs. 31(1), 39–50 (2005)

    Article  Google Scholar 

  9. Fleszar, K., Hindi, K.S.: Solving the resource-constrained project scheduling problem by a variable neighbourhood search. Eur. J. Oper. Res. 155(2), 402–413 (2004)

    Article  MathSciNet  Google Scholar 

  10. Frifita, S., Masmoudi, M., Euchi, J.: General variable neighborhood search for home healthcare routing and scheduling problem with time windows and synchronized visits. Electron. Notes Discret. Math. 58, 63–70 (2017)

    Article  MathSciNet  Google Scholar 

  11. Graham, R., Lawler, E., Lenstra, J., Kan, A.: Optimization and approximation in deterministic sequencing and scheduling: a survey. In: Hammer, P.L., Johnson, E.L., Korte, B. (eds.) Discrete Optimization II Proceedings of the Advanced Research Institute on Discrete Optimization and Systems Applications, Annals of Discrete Mathematics, vol. 5, pp. 287–326 (1979)

    Google Scholar 

  12. Green, L.V., Soares, J., Giglio, J.F., Green, R.A.: Using queueing theory to increase the effectiveness of emergency department provider staffing. Acad. Emerg. Med. 13(1), 61–68 (2006)

    Article  Google Scholar 

  13. Hansen, P., Mladenović, N.: Variable neighborhood search. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies. Springer, Boston (2005). https://doi.org/10.1007/0-387-28356-0_8

  14. Jouglet, A., Savourey, D.: Dominance rules for the parallel machine total weighted tardiness scheduling problem with release dates. Comput. Oper. Res. 38(9), 1259–1266 (2011)

    Article  MathSciNet  Google Scholar 

  15. Kramer, A., Subramanian, A.: A unified heuristic and an annotated bibliography for a large class of earliness-tardiness scheduling problems. J. Sched. 22, 21–57 (2019)

    Article  MathSciNet  Google Scholar 

  16. Kramer, A., Dell’Amico, M., Iori, M.: Enhanced arc-flow formulations to minimize weighted completion time on identical parallel machines. Eur. J. Oper. Res. 275(1), 67–79 (2019)

    Article  MathSciNet  Google Scholar 

  17. Kramer, A., Dell’Amico, M., Feillet, D., Iori, M.: Scheduling jobs with release dates on identical parallel machines by minimizing the total weighted completion time. Comput. Oper. Res. 123, 105018 (2020)

    Article  MathSciNet  Google Scholar 

  18. Kuo, Y.H.: Integrating simulation with simulated annealing for scheduling physicians in an understaffed emergency department. HKIE Trans. 21(4), 253–261 (2014)

    Article  Google Scholar 

  19. Kuo, Y.H., Leung, J.M., Graham, C.A., Tsoi, K.K., Meng, H.M.: Using simulation to assess the impacts of the adoption of a fast-track system for hospital emergency services. J. Adv. Mech. Des. Syst. Manuf. 12(3), 17-00637:1–17-00637:11 (2018)

    Article  Google Scholar 

  20. Kuo, Y.H., Rado, O., Lupia, B., Leung, J.M.Y., Graham, C.A.: Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions. Flex. Serv. Manuf. J. 2, 120–147 (2014). https://doi.org/10.1007/s10696-014-9198-7

    Article  Google Scholar 

  21. Lenstra, J., Kan, A.R., Brucker, P.: Complexity of machine scheduling problems. In: Hammer, P., Johnson, E., Korte, B., Nemhauser, G. (eds.) Studies in Integer Programming, Annals of Discrete Mathematics, vol. 1, pp. 343–362. Elsevier (1977)

    Google Scholar 

  22. Li, J., Pan, Q., Wang, F.: A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem. Appl. Soft Comput. 24, 63–77 (2014)

    Article  Google Scholar 

  23. Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)

    Article  MathSciNet  Google Scholar 

  24. Nessah, R., Yalaoui, F., Chu, C.: A branch-and-bound algorithm to minimize total weighted completion time on identical parallel machines with job release dates. Comput. Oper. Res. 35(4), 1176–1190 (2008)

    Article  MathSciNet  Google Scholar 

  25. Queiroz, T.A., Mundim, L.R.: Multiobjective pseudo-variable neighborhood descent for a bicriteria parallel machine scheduling problem with setup time. Int. Trans. Oper. Res. 27(3), 1478–1500 (2020)

    Article  MathSciNet  Google Scholar 

  26. Richardson, D.B.: Increase in patient mortality at 10 days associated with emergency department overcrowding. Med. J. Aust. 184(5), 213–216 (2006)

    Article  Google Scholar 

  27. Saghafian, S., Hopp, W.J., Van Oyen, M.P., Desmond, J.S., Kronick, S.L.: Complexity-augmented triage: a tool for improving patient safety and operational efficiency. Manuf. Serv. Oper. Manag. 16(3), 329–345 (2014)

    Article  Google Scholar 

  28. Shih, F.Y., et al.: ED overcrowding in Taiwan: facts and strategies. Am. J. Emerg. Med. 17(2), 198–202 (1999)

    Article  Google Scholar 

  29. Wuerz, R.C., Milne, L.W., Eitel, D.R., Travers, D., Gilboy, N.: Reliability and validity of a new five-level triage instrument. Acad. Emerg. Med. 7(3), 236–242 (2000)

    Article  Google Scholar 

Download references

Acknowledgments

This research was partially funded by Health and Medical Research Fund, Food and Health Bureau, the Hong Kong SAR Government (grant 14151771), the Early Career Scheme (ECS), Research Grants Council (RGC) of Hong Kong (grant 27200419), the National Council for Scientific and Technological Development (CNPq - grants 234814/2014-4 and 308312/2016-3), the State of Goiás Research Foundation (FAPEG), and the University of Modena and Reggio Emilia (grant FAR 2018 - Analysis and optimization of health-care and pharmaceutical logistic processes).

Author information

Authors and Affiliations

  1. Institute of Mathematics and Technology, Federal University of Catalão, Catalão, GO, 75704-020, Brazil

    Thiago Alves de Queiroz

  2. Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, 42122, Reggio Emilia, Italy

    Manuel Iori

  3. Department of Production Engineering, Federal University of Rio Grande do Norte, Natal, RN, 59077-080, Brazil

    Arthur Kramer

  4. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Pok Fu Lam, Hong Kong

    Yong-Hong Kuo

Authors
  1. Thiago Alves de Queiroz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel Iori
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arthur Kramer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong-Hong Kuo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thiago Alves de Queiroz .

Editor information

Editors and Affiliations

  1. Khalifa University, Abu Dhabi, United Arab Emirates

    Nenad Mladenovic

  2. Khalifa University, Abu Dhabi, United Arab Emirates

    Andrei Sleptchenko

  3. University of Macedonia, Thessaloniki, Greece

    Angelo Sifaleras

  4. Khalifa University, Abu Dhabi, United Arab Emirates

    Mohammed Omar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alves de Queiroz, T., Iori, M., Kramer, A., Kuo, YH. (2021). Scheduling of Patients in Emergency Departments with a Variable Neighborhood Search. In: Mladenovic, N., Sleptchenko, A., Sifaleras, A., Omar, M. (eds) Variable Neighborhood Search. ICVNS 2021. Lecture Notes in Computer Science(), vol 12559. Springer, Cham. https://doi.org/10.1007/978-3-030-69625-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-69625-2_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69624-5

  • Online ISBN: 978-3-030-69625-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation