Application of a Genetic Algorithm to staff scheduling in retail sector

Saeed Zolfaghari, Vinh Quan, Ahmed El-Bouri and Maryam Khashayardoust

International Journal of Industrial and Systems Engineering, 2010, vol. 5, issue 1, 20-47

Abstract: A Genetic Algorithm (GA) is developed for the retail staff scheduling problem. The proposed algorithm is implemented and compared with a conventional integer programming branch-and-bound approach. The performance of the algorithm is tested on six real-world problems. A sensitivity analysis is carried out on three problems for two genetic parameters: population size and mutation rate. Using statistical analysis, the effects of these parameters on the solution quality and computational times are studied. The comparative study shows that GA can produce near-optimal solutions for all of the test problems, and for half of them, it is more successful than the branch-and-bound method.

Keywords: genetic algorithms; GAs; labour scheduling; service operations management; metaheuristics; integer programming; retailing; branch and bound algorithms; statistical analysis; personnel management; retail staffing; staff scheduling. (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=29755 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:5:y:2010:i:1:p:20-47

Access Statistics for this article

More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().