94
Views
9
CrossRef citations to date
0
Altmetric
General Paper

A tabu search algorithm applied to the staffing roster problem of Leicestershire police force

&
Pages 489-496 | Received 01 Apr 2010, Accepted 01 Oct 2010, Published online: 21 Dec 2017
 

Abstract

This paper presents an application of the tabu search algorithm to a staff rostering problem relevant to Leicestershire Police. The aim is to address the issue of structuring staff rosters to enable effective use of staff to meet the demand on the Police to reduce and deal with crime-related incidents. This problem is defined through the compilation of a time-varying level of required staff and an associated staff roster. The objective is an optimised work set-up, maximising staff resources and the meeting of demand. Optimisation of staff levels to demand is sought through use of a series of tabu search algorithms, making use of two diversification techniques and an intensification technique individually and in compilation. The tabu search is shown to be a well-suited optimisation approach to the type of problem defined, with individual conclusions drawn for each of the technique combinations used.

Acknowledgements

The authors would like to acknowledge the assistance provided by Leicestershire Constabulary throughout this research. Notably, the assistance of Ch. Supt. Jason Masters, Sgt. Dave Hall and Mrs Carolyn Steptoe in managing Police aspects of the work and of Mr Jonathan White in provision of data.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 277.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.