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Application of a Genetic Algorithm to staff scheduling in retail sector

Author

Listed:
  • Saeed Zolfaghari
  • Vinh Quan
  • Ahmed El-Bouri
  • Maryam Khashayardoust

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.

Suggested Citation

  • Saeed Zolfaghari & Vinh Quan & Ahmed El-Bouri & Maryam Khashayardoust, 2010. "Application of a Genetic Algorithm to staff scheduling in retail sector," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 5(1), pages 20-47.
    • Handle: RePEc:ids:ijisen:v:5:y:2010:i:1:p:20-47
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    Cited by:

    1. Emir Hüseyin Özder & Evrencan Özcan & Tamer Eren, 2019. "Staff Task-Based Shift Scheduling Solution with an ANP and Goal Programming Method in a Natural Gas Combined Cycle Power Plant," Mathematics, MDPI, vol. 7(2), pages 1-26, February.

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