References
- Bagheri, M., Devin, A. G., & Izanloo, A. (2016). An application of stochastic programming method for nurse scheduling problem in real word hospital. Computers & Industrial Engineering, 96, 192–200.
- 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.
- Burke, E. K., De Causmaecker, P., Berghe, G. V., & Van Landeghem, H. (2004). The state of the art of nurse rostering. Journal of Scheduling, 7(6), 441–499.
- 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.
- Da Rin, G. (2009). Pre-analytical workstations: A tool for reducing laboratory errors. Clinica Chimica Acta, 404(1), 68–74.
- Dantzig, G. B., & Infanger, G. (1993). Multi-stage stochastic linear programs for portfolio optimization. Annals of Operations Research, 45(1), 59–76.
- Dempster, M. A. H., Fisher, M. L., Jansen, L., Lageweg, B. J., Lenstra, J. K., & Rinnooy Kan, A. H. (1983). Analysis of heuristics for stochastic programming: Results for hierarchical scheduling problems. Mathematics of Operations Research, 8(4), 525–537.
- Fanjiang, G., Grossman, J. H., Compton, W. D., & Reid, P. P. (Eds.). (2005). Building a better delivery system: A new engineeringl health care partnership. Washington, DC: National Academies Press.
- Hawkins, R. (2012). Managing the pre-and post-analytical phases of the total testing process. Annals of Laboratory Medicine, 32(1), 516. doi:10.3343/alm.2012.32.1.5.
- Karuppiah, R., Martń, M., & Grossmann, I. E. (2010). A simple heuristic for reducing the number of scenarios in two-stage stochastic programming. Computers & Chemical Engineering, 34(8), 1246–1255.
- Kim, K., & Mehrotra, S. (2015). A two-stage stochastic integer programming approach to integrated staffing and scheduling with application to nurse management. Operations Research, 63(6), 1431–1451.
- 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.
- Leaven, L. & Xiuli, Q. (2014). Applying scenario reduction heuristics in stochastic programming for phlebotomist scheduling. Management Science and Engineering, 8(3), 1–4.
- Plebani, M. (1999). The clinical importance of laboratory reasoning. Clinica Chimica Acta, 280(1), 35–45.
- Punnakitikashem, P., Rosenberger, J. M., & Behan, D. B. (2008). Stochastic programming for nurse assignment. Computational Optimization and Applications, 40(3), 321–349.
- Reader, T. W., & Gillespie, A. (2013). Patient neglect in healthcare institutions: A systematic review and conceptual model. BMC Health Services Research. doi:10.1186/1472-6963-13-156.
- Risser, D. T., Rice, M. M., Salisbury, M. L., Simon, R., Jay, G. D., Berns, S. D., & 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.
- Shapiro, A. (2003). Monte Carlo sampling approach to stochastic programming. Paper presented at the ESAIM: Proceedings.