著者

川中 普晴 山本 康高 吉川 大弘 篠木 剛 鶴岡 信治

出版者

The Institute of Electrical Engineers of Japan

雑誌

電気学会論文誌C(電子・情報・システム部門誌) (ISSN:03854221)

巻号頁・発行日

vol.122, no.6, pp.1023-1032, 2002-06-01 (Released:2008-12-19)

参考文献数

10

被引用文献数

2

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Nurse Scheduling Problem (NSP) is the problem that allocating shifts (day and night shifts, holidays, and so on) for nurses under various constraints. Generally, NSP has a lot of constraints. As a result, it needs a lot of knowledge and experience to make the scheduling table with its constraints, and it has been made by the head nurse or the authority in the hospitals. Some researches for NSP using Genetic Algorithm (GA) have been reported. The conventional methods take the constraints into the fitness function. However, if it reduces the fitness value a lot to the parts of solution against the constraints, it causes useless search. Because most of chromosomes are selected in the initial population or as the change by the genetic operations. And if it doesn't reduce the fitness value so much, the final solution has some parts against the constraints. Some of them are established by the Labor Standards Act or the Labor Union Act, so the solution has to be modified. As a result, it is difficult to acquire an effective scheduling table automatically. We study the method of the coding and the genetic operations with their constraints for NSP. In this paper, we propose a new coding method and genetic operations considering the constraints. We apply this method to the NSP using actual shifts and constraints being used in a hospital. It shows that an effective scheduling table satisfying the constraints is acquired by this method.

著者

小島 輝之 山本 康高 吉川 大弘 古橋 武

出版者

日本感性工学会

雑誌

感性工学研究論文集 (ISSN:13461958)

巻号頁・発行日

vol.7, no.1, pp.63-70, 2007-05-31 (Released:2010-06-28)

参考文献数

11

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The purpose of this study is to visualize the impressions on words between two subjects. We employ Semantic Differential (SD) method that is one of the most popular methods to quantify individual subjectivity. The number of dimensions of eachi mpression word is same with that of objects in SD data. It needs to be reduced to less than three dimensions for visualization. This paper proposes the visualization method which focuses on correlations of SD data between two subjects. The impression words are visualized on three-dimensional space where impression words having high correlation between two subjects' SD data are put close one another. We can investigate and discuss the similarities/differences of impression words between two subjects through this visualized space.