A tensor based hyper-heuristic for nurse rostering

Asta, Shahriar; �zcan, Ender; Curtois, Tim

Abstract

Nurse rostering is a well-known highly constrained scheduling problem requiring assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to produce high quality solutions, hence (meta)heuristics are commonly preferred as solution methods which are often designed and tuned for specific (group of) problem instances. Hyper-heuristics have emerged as general search methodologies that mix and manage a predefined set of low level heuristics while solving computationally hard problems. In this study, we describe an online learning hyper-heuristic employing a data science technique which is capable of self-improvement via tensor analysis for nurse rostering. The proposed approach is evaluated on a well-known nurse rostering benchmark consisting of a diverse collection of instances obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four of the instances.

Citation

Asta, S., Özcan, E., & Curtois, T. (2016). A tensor based hyper-heuristic for nurse rostering. Knowledge-Based Systems, 98, https://doi.org/10.1016/j.knosys.2016.01.031

• Journal Article Type: Article
• Acceptance Date: Jan 23, 2016
• Online Publication Date: Feb 3, 2016
• Publication Date: Apr 15, 2016
• Deposit Date: Mar 10, 2016
• Publicly Available Date: Mar 10, 2016
• Journal: Knowledge-Based Systems
• Print ISSN: 0950-7051
• Electronic ISSN: 1872-7409
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 98
• DOI: https://doi.org/10.1016/j.knosys.2016.01.031
• Keywords: Nurse rostering; Personnel scheduling; Data science; Tensor factorization; Hyper-heuristics
• Public URL: https://nottingham-repository.worktribe.com/output/784976
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0950705116000514