A tensor based hyper-heuristic for nurse rostering

Asta, Shahriar and Özcan, Ender and Curtois, Tim (2016) A tensor based hyper-heuristic for nurse rostering. Knowledge-Based Systems, 98 . pp. 185-199. ISSN 1872-7409

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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.

Item Type: Article
Keywords: Nurse rostering; Personnel scheduling; Data science; Tensor factorization; Hyper-heuristics
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.knosys.2016.01.031
Depositing User: Ozcan, Dr Ender
Date Deposited: 10 Mar 2016 09:54
Last Modified: 15 Sep 2016 13:15
URI: http://eprints.nottingham.ac.uk/id/eprint/32190

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