Authors:
Mehdi Sadeghilalimi
;
Malek Mouhoub
and
Aymen Ben Said
Affiliation:
Department of Computer Science, University of Regina, Regina, Canada
Keyword(s):
Combinatorial Optimization, Nature-Inspired Techniques, Metaheuristics, Stochastic Optimization, Resource Allocation, Nurse Scheduling Problem (NSP).
Abstract:
The Nurse Scheduling Problem (NSP) is a combinatorial optimization problem that creates weekly scheduling solutions for nurses. These solutions must satisfy constraints for the workload coverage requirements while optimizing one or more objectives related to hospital costs or nurses’ preferences. Although exact methods may be used to solve the NSP and return the optimal solution, they usually come with an exponential time cost. Therefore, approximate methods may be considered as they offer a good trade-off between the quality of the solution and the running time. In this context, we propose a solving method based on Genetic Algorithms (GAs) to solve the NSP. To evaluate the efficiency of our proposed method, we conducted experiments on various NSP instances. Further, we compared the quality of the returned solutions against solutions obtained from exact methods and metaheuristics. The experimental results reveal that our proposed method can fairly compete with B&B in terms of the qua
lity of the solution while delivering the solutions in much faster running times.
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