Title: Application of a Genetic Algorithm to staff scheduling in retail sector
Author: Saeed Zolfaghari, Vinh Quan, Ahmed El-Bouri, Maryam Khashayardoust
Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria Street, Toronto, M5B 2K3 Ontario, Canada.
Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, L1H 7K4 Ontario, Canada.
Department of Operations Management and Business Statistics, College of Commerce and Economics, Sultan Qaboos University, P.O. Box 20, Al-Khod 123, Oman.
Research and Development, Infor Global Solutions, 250 Ferrand Drive, Suite 1200, Toronto, M3C 3G8 Ontario, Canada
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.
Int. J. of Industrial and Systems Engineering, 2010 Vol.5, No.1, pp.20 - 47
Available online: 02 Dec 2009