Skip to main content
SpringerLink
Log in

Staff scheduling in blood collection problems

  • S.I. : Scalable Optimization and Decision Making in OR
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

To improve the blood collection volume and reduce the staff costs, this study investigates the staff scheduling problem faced by blood donation center under variant situations derived from reality. Staff scheduling for blood donation center is a complex task due to the stochastic arriving of blood donors. In this study, we propose the deterministic demand model and stochastic demand model of blood donors according to the number of donors arriving at donation sites. Then, based on the deterministic demand and the stochastic demand models, we consider the number of staff in the blood donation center and group blood donation. Five blood collection scenarios are proposed using the combination of donors and staff. To solve the proposed models, linear transformation and lexicographic order optimal method are applied. To verify the effectiveness of the proposed models, both large-scale and small-scale numerical experiments are conducted and its the stability is also validated using the 10 times of random numerical experiments. All of the experimental results show that the proposed models need few staff when facing adequate staff scenarios, and reduce the number of donors when facing inadequate staff scenarios.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Abkowitz, M. D., & Cheng, P. (1988). Developing a risk/cost framework for routing truck movements of hazardous materials. Accident Analysis and Prevention, 20(1), 39–51.

    Article  Google Scholar 

  • Avramidis, A. N., Chan, W., Gendreau, M., L’Ecuyer, P., & Pisacane, O. (2010). Optimizing daily agent scheduling in a multiskill call center. European Journal of Operational Research, 200(3), 822–832.

    Article  Google Scholar 

  • Bard, J. F., Binici, C., & Desilva, A. H. (2003). Staff scheduling at the United States postal service. Computers & Operations Research, 30(5), 745–771.

    Article  Google Scholar 

  • Bergh, V. D., Beliën, J., Bruecker, D. P., Demeulemeester, E., & Boeck, D. L. (2013). Personnel scheduling: A literature review. European Journal of Operational Research, 226(3), 367–385.

    Article  Google Scholar 

  • Bilgin, B., Causmaecker, P. D., Rossie, B., & Berghe, G. V. (2012). Local search neighbourhoods for dealing with a novel nurse rostering model. Annals of Operations Research, 194(1), 33–57.

    Article  Google Scholar 

  • Brucker, P., & Kampmeyer, T. (2008). Cyclic job shop scheduling problems with blocking. Annals of Operations Research, 159(1), 161–181.

    Article  Google Scholar 

  • Bruni, R., & Detti, P. (2014). A flexible discrete optimization approach to the physician scheduling problem. Operations Research for Health Care, 3(4), 191–199.

    Article  Google Scholar 

  • Castillo-Salazar, J. A., Landa-Silva, D., & Qu, R. (2016). Workforce scheduling and routing problems: Literature survey and computational study. Annals of Operations Research, 239(1), 39–67.

    Article  Google Scholar 

  • Case, T. T., Colorni, A., Dorigo, M., & Maniezzo, V. (1990). Genetic algorithms and highly constrained problems: The time-table case[C] Workshop on parallel problem solving from nature. Springer.

  • Chao, I. M., Golden, B. L., & Wasil, E. A. (2007). The team orienteering problem. European Journal of Operational Research, 88(3), 464–474.

    Article  Google Scholar 

  • Davey, R. J. (2004). Recruiting blood donors: Challenges and opportunities. Transfusion, 44(4), 597–600.

    Article  Google Scholar 

  • Diamant, A., Milner, J., & Quereshy, F. (2018). Dynamic patient scheduling for multi-appointment health care programs. Production & Operations Management, 27(1), 58–79.

    Article  Google Scholar 

  • Doerner, K. F., Gronalt, M., Hartl, R. F., Kiechle, G., & Reimann, M. (2008). Exact and heuristic algorithms for the vehicle routing problem with multiple interdependent time windows. Computers & Operations Research, 35(9), 3034–3048.

    Article  Google Scholar 

  • Erhard, M., Schoenfelder, J., Fügener, A., & Brunner, J. O. (2017). State of the art in physician scheduling. European Journal of Operational Research, 265, 1–18.

    Article  Google Scholar 

  • Ernst, A. T., Jiang, H., Krishnamoorthy, M., & Sier, D. (2004). Staff scheduling and rostering: A review of applications, methods and models. European Journal of Operational Research, 153(1), 3–27.

    Article  Google Scholar 

  • Glass, C. A., & Knight, R. A. (2010). The nurse rostering problem: A critical appraisal of the problem structure. European Journal of Operational Research, 202(2), 379–389.

    Article  Google Scholar 

  • Gunpinar, S., & Centeno, G. (2016). An integer programming approach to the bloodmobile routing problem. Transportation Research Part E: Logistics and Transportation Review, 86, 94–115.

    Article  Google Scholar 

  • Guyon, O., Lemaire, P., Pinson, é, & Rivreau, D. (2010). Cut generation for an integrated employee timetabling and production scheduling problem. European Journal of Operational Research, 201(2), 557–567.

    Article  Google Scholar 

  • Hemmelmayr, V., Doerner, K. F., Hartl, R. F., & Savelsbergh, M. W. P. (2009). Delivery strategies for blood products supplies. OR Spectrum, 31(4), 707–725.

    Article  Google Scholar 

  • http://www.nhc.gov.cn/yzygj/xxmtbd/201408/66ed235c370f494f9030488e0f50716a.shtml.

  • Li, Y., & Liu, Y. (2019). A risk-averse multi-item inventory problem with uncertain demand. Journal of Data, Information and Management, 1, 77–90.

    Article  Google Scholar 

  • Mason, A. J., & Panton, R. D. M. (1998). Integrated simulation, heuristic and optimisation approaches to staff scheduling. Operations Research, 46(2), 161–175.

    Article  Google Scholar 

  • Mostafa, P., Abbas, K., Mohammad-Ali, J. F., Sima, Z., Omid, K. G. S., & Mehdi, S. (2018). Mobile blood collection sites and their roles in providing safe and adequate supply: A six-year experience. Frontiers in Biology, 13(3), 226–234.

    Article  Google Scholar 

  • Özener, O. Ö., & Ali, E. (2018). Managing platelet supply through improved routing of blood collection vehicles. Computers & Operations Research, 98, 113–126.

    Article  Google Scholar 

  • Prastacos, G. P. (1984). Blood inventory management: an overview of theory and practice. Management Science, 30(7), 777–800.

    Article  Google Scholar 

  • Puente, J., Alberto, G., Isabel, F., & Priore, P. (2009). Medical doctor rostering problem in a hospital emergency department by means of genetic algorithms. Computers & Industrial Engineering, 56(4), 1232–1242.

    Article  Google Scholar 

  • Şahinyazan, F. G., Kara, B. Y., & Taner, M. R. (2015). Selective vehicle routing for a mobile blood donation system. European Journal of Operational Research, 245(1), 22–34.

    Article  Google Scholar 

  • Seda, B., Giuliana, C., Ettore, L., & Semih, Y. (2018). An appointment scheduling framework to balance the production of blood units from donation. European Journal of Operational Research, 265(3), 1124–1143.

    Article  Google Scholar 

  • Smalley, H. K., & Keskinocak, P. (2016). Automated medical resident rotation and shift scheduling to ensure quality resident education and patient care. Health Care Management Science, 19(1), 66–88.

    Article  Google Scholar 

  • Subhash, C. S., Hanif, D. S., & Seon, K. K. (2016). A hospital staff scheduling problem under stochastic operation times. International Journal of Planning and Scheduling, 2(3), 210–232.

    Article  Google Scholar 

  • Topaloglu, S., & Ozkarahan, I. (2011). A constraint programming-based solution approach for medical resident scheduling problems. Computers & Operations Research, 38(1), 246–255.

    Article  Google Scholar 

  • Van Brummelen, S. P. J., De Kort, W. L., & Van Dijk, N. M. (2018). Queue length computation of time-dependent queueing networks and its application to blood collection. Operations Research for Health Care, 17, 4–15.

    Article  Google Scholar 

  • Vermuyten, H., Rosa, J. N., Marques, I., Beliën, J., & Barbosa, A. (2018). Integrated staff scheduling at a medical emergency service: An optimisation approach. Expert Systems with Applications, 112, 62–76.

    Article  Google Scholar 

  • Wong, T. C., Xu, M., & Chin, K. S. (2014). A two-stage heuristic approach for nurse scheduling problem: A case study in an emergency department. Computers & Operations Research, 51, 99–110.

    Article  Google Scholar 

  • Yi, J. (2003). Vehicle routing with time windows and time-dependent rewards: A problem from the american red cross. Manufacturing & Service Operations Management, 5(1), 74–77.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 71722007, 71931001, 71901014), the Funds for First-class Discipline Construction (XK18025) and the Fundamental Research Funds for the Central Universities (buctrc201926).

Author information

Authors and Affiliations

  1. College of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China

    Xiang Li, Haoyue Fan & Jiaming Liu

  2. Academy of Mathematics and Systems Science, Chinese Academy of Science, University of Chinese Academy of Science, Beijing, 100190, China

    Qifeng Xun

Authors
  1. Xiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haoyue Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiaming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qifeng Xun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiaming Liu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables 16, 17, 18, 19, 20, 21, 22, 23.

Table 16 The distribution of donors (\(25*6\))
Full size table
Table 17 The distribution of donors in situation \(h_1\) (\(50*8\))
Full size table
Table 18 The distribution of donors in situation \(h_2\) (\(50*8\))
Full size table
Table 19 Optimal staff scheduling solutions of D-A (\(50*8\))
Full size table
Table 20 Optimal staff scheduling solutions of S-A (\(50*8\))
Full size table
Table 21 Optimal staff scheduling solutions of D-IA (\(50*8\))
Full size table
Table 22 Optimal staff scheduling solutions of S-IA (\(50*8\))
Full size table
Table 23 Optimal staff scheduling solutions of S-IA-T (\(50*8\))
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Fan, H., Liu, J. et al. Staff scheduling in blood collection problems. Ann Oper Res 316, 365–400 (2022). https://doi.org/10.1007/s10479-020-03688-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-020-03688-4

Keywords

Search

Navigation