Using Rough Set Theory to Location Selection-with Application in Food and Beverage Chain Industry in Taiwan

• Main Authors: TSAI, CHIH-TSUNG, 蔡志聰
• Other Authors: CHEN, LI-FEI
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/48840550710949965695

碩士 === 輔仁大學 === 管理學院經營管理碩士學程 === 99 === 英文摘要 Title of Thesis: Using Rough Set Theory to Location Selection- with Application in Food and Beverage Chain Industry in Taiwan Name of Student: Chih-Tsung Tsai Advisor: Dr. Li-Fei Chen Total Page: 61 Keywords: rough set theory, location selection, location theory, food and beverage chain industry, classification Abstract: Increasing the number of business locations is an integral part of success within the service chain industry. Furthermore, a successful strategy depends on choosing the right business location. As for operations, following past successful models can reduce the risk of failure. In past, many studies have been done investigating the key factors of selecting locations and analyzing methods in statistics. For example, using a regression analysis or a cluster analysis can increase the chance of selecting the right locations based on application of historical data. There are various and complicated factors that affect the criteria of location selection. Most of the factors are developed from location theory; for instance, gravity model, spatial competition model, land use model, and continuous location model are used to explain the criteria of location selection from different dimensions. Many statistical analyses have assumptions in which the sample data must be normal and the sample size must be large enough in order to identify the results. In contrast, rough set theory (RST) deals with ambiguous information and creates rules based on data itself without the restriction of small sample size. As a result, it shows that RST can extract key factors that affect store selection and create a better model for location decision making. Also, it allows us to gauge a store’s potential performance more effectively.