Novel heuristic and metaheuristic approaches to the automated scheduling of healthcare personnel

Curtois, Timothy (2008) Novel heuristic and metaheuristic approaches to the automated scheduling of healthcare personnel. PhD thesis, University of Nottingham.

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Abstract

This thesis is concerned with automated personnel scheduling in healthcare organisations; in particular, nurse rostering. Over the past forty years the nurse rostering problem has received a large amount of research. This can be mostly attributed to its practical applications and the scientific challenges of solving such a complex problem. The benefits of automating the rostering process include reducing the planner’s workload and associated costs and being able to create higher quality and more flexible schedules. This has become more important recently in order to retain nurses and attract more people into the profession. Better quality rosters also reduce fatigue and stress due to overwork and poor scheduling and help to maximise the use of leisure time by satisfying more requests. A more contented workforce will lead to higher productivity, increased quality of patient service and a better level of healthcare.

Basically stated, the nurse rostering problem requires the assignment of shifts to personnel to ensure that sufficient employees are present to perform the duties required. There are usually a number of constraints such as working regulations and legal requirements and a number of objectives such as maximising the nurses working preferences. When formulated mathematically this problem can be shown to belong to a class of problems which are considered intractable. The work presented in this thesis expands upon the research that has already been conducted to try and provide higher quality solutions to these challenging problems in shorter computation times.

The thesis is broadly structured into three sections. 1) An investigation into a nurse rostering problem provided by an industrial collaborator. 2) A framework to aid research in nurse rostering. 3) The development of a number of advanced algorithms for solving highly complex, real world problems.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Burke, E.K.
Qu, Rong
Keywords: scheduling, data processing, health care teams, healthcare personnel
Subjects: Q Science > QA Mathematics
T Technology > T Technology (General)
Faculties/Schools: UK Campuses > Faculty of Science > School of Computer Science
Item ID: 28309
Depositing User: Curtois, Timothy
Date Deposited: 06 Feb 2015 10:13
Last Modified: 16 Sep 2016 05:40
URI: http://eprints.nottingham.ac.uk/id/eprint/28309

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