A Fuzzy MCDM Model for the Evalution and Selection of the Locations of Distribution centers

碩士 === 南台科技大學 === 工業管理研究所 === 94 === With globalization and rapid progress in information industry, Taiwanese logistics industry has been growing vigorously . In order to satisfy consumer’s different demands,such as small amount with different varieties,home delivery goods tracking, ,quick response...

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Bibliographic Details
Main Authors: Liu sz xian, 劉思賢
Other Authors: Ta-Chung Chu
Format: Others
Language:zh-TW
Published: 2006
Online Access:http://ndltd.ncl.edu.tw/handle/57670592359822090407
Description
Summary:碩士 === 南台科技大學 === 工業管理研究所 === 94 === With globalization and rapid progress in information industry, Taiwanese logistics industry has been growing vigorously . In order to satisfy consumer’s different demands,such as small amount with different varieties,home delivery goods tracking, ,quick response to goods return and exchange etc.,enterprises must increase integral competitiveness. The location of a distribution center will influence the cost and efficiency of a enterprise,and will thus influence the enterprise competitiveness;the selection of the location of a distribution center has then become a very important issue. Many criteria must be considered when evaluating a distribution center location.Some criteria are quantitative,such as the machinery cost;some are qualitative,such as the political environment.Furthermore, different decision maker many have different opinion on the different important among criteria.When the data evaluated is fuzzy or qualitative, fuzzy multiple critera decision making (fuzzy MCDM) can effectively combine quantitative and quatitative criteria for comprehensive evaluation and analyses. Therefore,the purpose of this research is just to apply fuzzy MCDM to develop a model for the evaluation and selection fo the locations of distribution centers.Both the qualitative and quantitative criteria and the different importance among criteria can be considered in the proposed model.The membership functions of the final fuzzy evaluation values in the model will be derived.The concept of Mean of Removals is they applied to develop integration formulae in order to produce defuzzification values for the ranking of all candidate locations. Finally, numerical example will be used to demonstrate the feasibility of the proposed model.