Abstract
This paper addresses a nurse scheduling problem frequently encountered in hospital management. To make nurses satisfied and use their best skills during the work process is a critical issue at the center of this problem. Besides, hospitals need to minimize personnel costs while keeping service quality at the highest level. We try to schedule nurses by considering their preferences and meet hospital management expectations at the same time. Our problem has hard and soft constraints that are faced in real-world case studies. Hard constraints are satisfied directly by applying the constraint programming method, and soft constraints are satisfied using a penalty cost applied in meta-heuristic algorithms. The initial model is structured using a Genetic algorithm (GA), then it is hybridized with the simulated annealing (SA) to obtain a nurse schedule. Results are compared with MIP solutions concerning the quality of solutions and the corresponding running time. Achievements are analyzed and discussed to make the proposed model applicable by hospital managers as well as researchers.
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