| Summary: | 碩士 === 國立暨南國際大學 === 資訊管理學系 === 97 === It is time-consuming to generate nurse scheduling using traditional human-involved manner in order to account for administrative operations, business benefits, governmental regulations, and fairness perceived by nurses. Moreover, the objectives cannot be measured quantitatively even when the nurse scheduling is generated after a lengthy manual process. This paper presents a Multi-Objective Scatter PSO combined with Tabu Search to tackle the real-world nurse scheduling problem. By the proposed mathematical formulation, the hospital administrator can set up multiple objectives (such as cost reduction and nurse-satisfaction raising) and stipulate a set of scheduling constraints (such as operational practice and governmental regulations), and our system can automatically generate a set of solutions which nearly optimize the given objectives and meet the specified constraints. We used two kinds of problems to evaluate the performance of Scatter MOPSO, first is benchmark functions and second is nurse scheduling problem. The experimental results manifest that our method performs better than NSGA II and MOPSO on benchmark functions, and better than MOPSO on nurse scheduling problem.
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