Enhanced Greedy Optimization Algorithm with Data Warehousing for Automated Nurse Scheduling System


Current Nurse scheduling process has many challenges like work plan creation and working hour allocation for employees at specific planning horizon. Hospitals in most of the developing countries use manual methods to create nurse scheduling systems. With current existing manual nurse scheduling systems, most of the hospitals especially in developing countries don’t have efficient work plan allocation. Moreover, patients need nursing care throughout the day. Hence, current manual nurse scheduling approach with simple statistical functions is not efficient especially for highly populated countries. Our proposed automated nurse scheduling approach has carried out in two stages. Firstly, we propose an efficient data warehouse system based on online analytical method for hospital information system. Subsequently, Enhanced Greedy Optimization algorithm is implemented to optimize the nurse roster and compared with other optimization algorithms (Simulated Annealing and Genetic Algorithm). Experimental results (MYSQL, JAVA, OLAP) with proposed optimization algorithm outperforms compared with existing optimization solutions.

Share and Cite:

R. Ratnayaka, Z. Wang, S. Anamalamudi and S. Cheng, "Enhanced Greedy Optimization Algorithm with Data Warehousing for Automated Nurse Scheduling System," E-Health Telecommunication Systems and Networks, Vol. 1 No. 4, 2012, pp. 43-48. doi: 10.4236/etsn.2012.14007.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] S. Kundu, M. Mahato, B. Mahanty and S. Acharyya, “Comparative Performance of Simulated Annealing and Genetic Algorithm in Solving Nurse Scheduling Problem,” Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong, 19-21 March 2008, p. 96.
[2] G. Baskaran, A. Bargiela and R. Qu, “Hierarchical Method for Nurse Rostering Based on Granular Pre-Processing of Constraints,” The 23rd EUROPEAN Conference on Modelling and Simulation, Madrid, 9-12 June 2009, pp. 855-861.
[3] Q. Y. Guo, F. D. Hao, X. L. Duan, X. Q. Xie and W. Liao, “Multi Personal Computer Storage System: Solution of Sea Capacity PACS Storage,” Chinese Medical Journal, Vol. 116, No. 5, 2003, pp. 650-653.
[4] R. Paul and A. S. M. L. Hoque, “A Storage & Search Efficient Representation of Medical Data,” 2010 International Conference on Bioinformatics and Biomedical Technology, Chengdu, 16-18 April 2010, pp. 418-422.
[5] P. Li, T. Wu, M. Chen, B. Zhou and W.-G. Xu, “A Study on Building Data Warehouse of Hospital Information System,” Chinese Medical Journal, Vol. 124, No. 15, 2011, pp. 2372-2377.
[6] P. Villiers, “Clinical Data Warehouse Functionality,” SAS Institute Inc., New Caledonia, 1998.
[7] M. Silver, T. Sakuta, H.-C. Su, S. B. Dolins and M. J. Oshea, “Case Study: How to Apply Data Mining Technigues in a Healthcare Data Warehouse,” Journal of Hethcare Information Management, Vol. 15, No. 2, 2001, pp. 155-164.
[8] A. H. W. Chun, S. H. C. Chan, G. P. S. Lam, F. M. F. Tsang, J. Wong and D. W. M. Yeung, “Nurse Rostering at the Hospital Authority of Hong Kong,” Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, Austin, 30 July-3 August 2000, pp. 951-956.
[9] M. F. Wisniewski, P. Kieszkowski, B. M. Zagorski, W. E. Trick, M. Sommers and R. A. Weinstein, “Development of a Clinical Data Warehouse for Hospital Infection Control,” Journal of the American Medical Informatics Association, Vol. 10, No. 5, 2003, pp. 454-462. doi:10.1197/jamia.M1299
[10] T. B. Pederson and C. S. Jensen, “Research Issues in Clinical Data Warehousing,” 10th International Conference on Scientific and Statistical Database Management, Capri, 1-3 July 1998, pp. 43-52.
[11] C. A. Goble, R. Stevens, G. Ng, S. Bechhofer, N. W. Paton and P. G. Baker, et al., “Transparent Access to Multiple Bioinformatics Information Sources,” IBM Systems Journal, Vol. 40, No. 2, 2001, pp. 532-551. doi:10.1147/sj.402.0532
[12] B. A. Eckman, C. A. Bennett, J. H. Kaufman and J. W. Tenner, “Varieties of Interoperability in the Transformation of the Health-Care Information Infrastructure,” IBM Systems Journal, Vol. 46, No. 1, 2007, pp. 19-41. doi:10.1147/sj.461.0019
[13] X. Z. Zhou, S. B. Chen, B. Y. Liu, R. S. Zhang, Y. H. Wang and P. Li, et al., “Development of Traditional Chinese Medicine Clinical Data Warehouse for Medical Knowledge Discovery and Decision Support,” Artificial Intelligence in Medicine, Vol. 48, No. 2-3, pp. 139-152.

Copyright © 2021 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.