Abstract
Managing human resources is crucial in organizations, and this can be best done through optimal workforce scheduling. Workforce scheduling is conducted regularly in transportation, manufacturing, retail stores, academic institutions, and health care units. For the latter, healthcare personnel must be assigned required shifts to satisfy hospital requirements, while optimizing costs and quality of service. In this context, we propose a nature-inspired technique based on the Whale Optimization Algorithm (WOA) for solving the Nurse Scheduling Problem (NSP). More precisely, we have redefined the WOA to deal with this combinatorial problem efficiently. To assess the performance of different variants of our discrete WOA, we conducted several experiments on randomly generated NSP instances. In the experiments, our WOA has been compared with variants of the Branch & Bound (B &B) and the Stochastic Local Search (SLS) algorithms. B &B uses constraint propagation at different levels while SLS starts with an initial configuration obtained with a backtrack search technique. Overall, the results of the comparative experiments demonstrate the superiority of the proposed WOA in terms of quality of the solution returned and the related running time.
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Sadeghilalimi, M., Mouhoub, M., Said, A.B. (2023). Solving the Nurse Scheduling Problem Using the Whale Optimization Algorithm. In: Dorronsoro, B., Chicano, F., Danoy, G., Talbi, EG. (eds) Optimization and Learning. OLA 2023. Communications in Computer and Information Science, vol 1824. Springer, Cham. https://doi.org/10.1007/978-3-031-34020-8_5
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