Dynamic programming with approximation function for nurse scheduling

Shi, Peng; Landa-Silva, Dario

Abstract

Although dynamic programming could ideally solve any combinatorial optimization problem, the curse of dimensionality of the search space seriously limits its application to large optimization problems. For example, only few papers in the literature have reported the application of dynamic programming to workforce scheduling problems. This paper investigates approximate dynamic programming to tackle nurse scheduling problems of size that dynamic programming cannot tackle in practice. Nurse scheduling is one of the problems within workforce scheduling that has been tackled with a considerable number of algorithms particularly meta-heuristics. Experimental results indicate that approximate dynamic programming is a suitable method to solve this problem effectively.

Citation

Shi, P., & Landa-Silva, D. (2016). Dynamic programming with approximation function for nurse scheduling. Lecture Notes in Artificial Intelligence, 10122, 269-280. https://doi.org/10.1007/978-3-319-51469-7_23

• Journal Article Type: Article
• Conference Name: 2nd International Workshop on Machine Learning, Optimization and Big Data (MOD 2016)
• End Date: Aug 29, 2016
• Acceptance Date: Jun 28, 2016
• Publication Date: Dec 25, 2016
• Deposit Date: Mar 24, 2017
• Publicly Available Date: Mar 24, 2017
• Journal: Lecture Notes in Computer Science
• Electronic ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 10122
• Pages: 269-280
• DOI: https://doi.org/10.1007/978-3-319-51469-7_23
• Keywords: Markov Decision Process, Approximate Dynamic Programming,; Nurse Scheduling Problem
• Public URL: https://nottingham-repository.worktribe.com/output/832256
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-319-51469-7_23
• Additional Information: The final publication is available at link.springer.com.; ; 2nd International Workshop on Machine Learning, Optimization and Big Data (MOD 2016), Volterra, Italy, 26-29 August 2016.
• Contract Date: Mar 24, 2017