A two-stage stochastic programming model for phlebotomist scheduling in hospital laboratories

  • Laquanda LeavenEmail author
  • Xiuli Qu
Original Article


This paper introduces a two-stage stochastic integer linear programming model to improve phlebotomist scheduling in laboratory facilities of healthcare delivery systems. The model developed enables laboratory management to determine optimal scheduling policies that minimize work overload. The stochastic programming model considers the uncertainty associated with the blood collection demand in laboratory environments when optimizing phlebotomist scheduling. The paper presents an application of the model to a hospital laboratory in urban North Carolina as a case study discussing the implications for hospital laboratory management.


Scheduling Laboratory Work overload Stochastic programming 


  1. Bagheri, M., A.G. Devin, and A. Izanloo. 2016. An application of stochastic programming method for nurse scheduling problem in real word hospital. Computers & Industrial Engineering 96: 192–200.CrossRefGoogle Scholar
  2. Bayraksan, G. 2005. Monte Carlo sampling-based methods in stochastic programming. Dissertation Presented to the Faculty of the Graduate School of The University of Texas at Austin.Google Scholar
  3. Burke, E.K., P. De Causmaecker, G.V. Berghe, and H. Van Landeghem. 2004. The state of the art of nurse rostering. Journal of Scheduling 7 (6): 441–499.CrossRefGoogle Scholar
  4. Campbell, G. 2011. A two-stage stochastic program for scheduling and allocating cross-trained workers. Journal of the Operational Research Society 62 (6): 1038–1047.CrossRefGoogle Scholar
  5. Da Rin, G. 2009. Pre-analytical workstations: a tool for reducing laboratory errors. Clinica Chimica Acta 404 (1): 68–74.CrossRefGoogle Scholar
  6. Dantzig, G.B., and G. Infanger. 1993. Multi-stage stochastic linear programs for portfolio optimization. Annals of Operations Research 45 (1): 59–76.CrossRefGoogle Scholar
  7. Dempster, M.A.H., M.L. Fisher, L. Jansen, B.J. Lageweg, J.K. Lenstra, and A.H. Rinnooy Kan. 1983. Analysis of heuristics for stochastic programming: results for hierarchical scheduling problems. Mathematics of Operations Research 8 (4): 525–537.CrossRefGoogle Scholar
  8. Fanjiang, G., J.H. Grossman, W.D. Compton, and P.P. Reid (eds.). 2005. Building a better delivery system: a new engineering/health care partnership. Washington: National Academies Press.Google Scholar
  9. Hawkins, R. 2012. Managing the pre- and post-analytical phases of the total testing process. Annals of Laboratory Medicine 32 (1): 5–16. doi: 10.3343/alm.2012.32.1.5.CrossRefGoogle Scholar
  10. Karuppiah, R., M. Martín, and I.E. Grossmann. 2010. A simple heuristic for reducing the number of scenarios in two-stage stochastic programming. Computers & Chemical Engineering 34 (8): 1246–1255.CrossRefGoogle Scholar
  11. Kim, K., and S. Mehrotra. 2015. A two-stage stochastic integer programming approach to integrated staffing and scheduling with application to nurse management. Operations Research 63 (6): 1431–1451.CrossRefGoogle Scholar
  12. Leaven, L. 2014. Improving laboratory performance in healthcare delivery systems through optimal stage selection: The analytic network process approach. Management Science and Engineering 8 (3): 35–40.Google Scholar
  13. Leaven, L., and Q. Xiuli. 2014. Applying scenario reduction heuristics in stochastic programming for phlebotomist scheduling. Management Science and Engineering 8 (3): 1–4.Google Scholar
  14. Plebani, M. 1999. The clinical importance of laboratory reasoning. Clinica Chimica Acta 280 (1): 35–45.CrossRefGoogle Scholar
  15. Punnakitikashem, P., J.M. Rosenberger, and D.B. Behan. 2008. Stochastic programming for nurse assignment. Computational Optimization and Applications 40 (3): 321–349.CrossRefGoogle Scholar
  16. Reader, T.W., and A. Gillespie. 2013. Patient neglect in healthcare institutions: A systematic review and conceptual model. BMC Health Services Research. doi: 10.1186/1472-6963-13-156.Google Scholar
  17. Risser, D.T., M.M. Rice, M.L. Salisbury, R. Simon, G.D. Jay, S.D. Berns, and MedTeams Research Consortium. 1999. The potential for improved teamwork to reduce medical errors in the emergency department. Annals of Emergency Medicine 34 (3): 373–383.CrossRefGoogle Scholar
  18. Shapiro, A. 2003. Monte Carlo sampling approach to stochastic programming. Paper presented at the ESAIM: Proceedings.Google Scholar

Copyright information

© The OR Society 2017

Authors and Affiliations

  1. 1.Department of Marketing, Transportation and Supply Chain, College of Business and EconomicsNorth Carolina A&T State UniversityGreensboroUSA
  2. 2.Department of Industrial and Systems Engineering, College of EngineeringNorth Carolina A&T State UniversityGreensboroUSA

Personalised recommendations