Home > Research > Publications & Outputs > A hyper-heuristic approach based upon a hidden ...

Electronic data

  • CAOR2020 (1)

    Rights statement: This is the author’s version of a work that was accepted for publication in Computers and Operations Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers and Operations Research, 130, 2021 DOI: 10.1016/j.cor.2021.105221

    Accepted author manuscript, 419 KB, PDF document

    Embargo ends: 9/07/22

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

A hyper-heuristic approach based upon a hidden Markov model for the multi-stage nurse rostering problem

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
Close
Article number105221
<mark>Journal publication date</mark>1/06/2021
<mark>Journal</mark>Computers and Operations Research
Volume130
Number of pages14
Publication StatusE-pub ahead of print
Early online date9/01/21
<mark>Original language</mark>English

Abstract

The nurse rostering problem is a very important problem to address. Due to the importance of nurses’ jobs, it is vital that all the nurses in a hospital are assigned to the most appropriate shifts and days so as to meet the demands of the hospital as well as to satisfy the requirements and requests of the nurses as much as possible. Nurse rostering is a computationally hard and challenging combinatorial optimisation problem. To solve it, general and efficient methodologies such as selection hyper-heuristics have emerged. To address the multi-stage nurse rostering formulation, posed by the second international nurse rostering competition’s problem, a sequence-based selection hyper-heuristic that utilises a statistical Markov model is developed. The proposed algorithm incorporates a dedicated algorithm for building feasible initial solutions and a series of low-level heuristics with different dynamics that respect the characteristics of the competition’s problem formulation. Empirical results and analysis suggest that the proposed approach has a significant potential on difficult problem instances.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Computers and Operations Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers and Operations Research, 130, 2021 DOI: 10.1016/j.cor.2021.105221