Abstract
Nurse Scheduling Problem (NSP) is very complex in nature and involves a large number of constraints to satisfy. In hospitals, nursing homes, and other health-care organizations, where the daily work is divided into shifts, the Nurse Scheduling Problem arises there to assign duties to all the nurses over a short-term planning period satisfying constraints as much as possible. It can be viewed as a Combinatorial Optimization Problem. The constraints are related to labor contract rules, preferences submitted by nurses, concerns of the employers and other real life situations. In this paper, apart from applying Simulated Annealing and Genetic Algorithms, we converted the cyclic NSP to Satisfiability Problem (SAT) and applied local search SAT techniques to solve it. In comparative assessment of the methods we show that USAT incorporated with a tabu list has outperformed other SAT methods in some instances. Otherwise, the SAT methods are almost equivalent in performance.
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Kundu, S., Acharyya, S. (2011). Stochastic Local Search Approaches in Solving the Nurse Scheduling Problem. In: Chaki, N., Cortesi, A. (eds) Computer Information Systems – Analysis and Technologies. Communications in Computer and Information Science, vol 245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27245-5_25
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DOI: https://doi.org/10.1007/978-3-642-27245-5_25
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