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Solving a Real Case of Seafaring Staff Scheduling Problem Using Cuckoo Optimization Algorithm

Author

Listed:
  • Marwa Koubaa

    (OLID Lab, University of Sfax, Tunisia)

  • Mohamed Haykal Ammar

    (OLID Lab, University of Sfax, Tunisia)

  • Noura Beji

    (OLID Lab, University of Sfax, Tunisia)

Abstract

This work deals with Human Resource Scheduling Problem (HRSP) where fairness is a very important factor when assigning different shifts to the seafaring teams. This type of problem is part and partial of the NP-hard problems category. We propounded to work out this Seafaring Staff Scheduling Problem (SSSP) using one of the population-based meta-heuristics called Cuckoo Optimization Algorithm (COA), one of the newest, most robust and most popular bio-inspired algorithms. rnAffording schedules that ensure an enhanced staff rest to the company compared to the traditionally used ones was the main objective of the paper. The results indicate that this method outperforms the traditional one in solving this NP -hard problem. In addition, they prove the COA performance in the improvement of the objective function value compared to the previously proposed methods in the literature namely GRASP and ABC. Finally, the use of the COA in scheduling also increased the total posts to be assigned by one compared to the ABC method.

Suggested Citation

  • Marwa Koubaa & Mohamed Haykal Ammar & Noura Beji, 2022. "Solving a Real Case of Seafaring Staff Scheduling Problem Using Cuckoo Optimization Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-19, January.
    • Handle: RePEc:igg:jamc00:v:13:y:2022:i:1:p:1-19
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    References listed on IDEAS

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    1. Mohamed Guendouz & Abdelmalek Amine & Reda Mohamed Hamou, 2018. "Penguins Search Optimization Algorithm for Community Detection in Complex Networks," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 9(1), pages 1-14, January.
    2. Hiba Yahyaoui & Saoussen Krichen & Abdelkader Dekdouk, 2018. "A Decision Model Based on a GRASP Genetic Algorithm for Solving the Vehicle Routing Problem," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 9(2), pages 72-90, April.
    3. Satya Ranjan Dash & Ratula Ray, 2020. "Predicting Seminal Quality and its Dependence on Life Style Factors Through Ensemble Learning," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 11(2), pages 78-95, April.
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