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论文类型：期刊论文

发表时间：2012-01-01

发表刊物：INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

收录刊物：SCIE、EI、SSCI、Scopus

卷号：50

期号：1,SI

页面范围：177-190

ISSN号：0020-7543

关键字：information granulation; entropy; multicriteria; weight derivation; third-party logistics

摘要：We introduce an innovative Information Granulation Entropy method to evaluate third-party logistics providers. Conventional fuzzy evaluation methods are valuable but biased at times. Objective measurements are rational; however its results are often difficult to explain. To take advantage of the strength of both methods, we propose a comprehensive evaluation framework to allow subjective judgment on alternatives, at the same time deriving criteria weights objectively. In the proposed model, experts input fuzzy language to form an evaluation matrix. After defuziffying the matrix, the K-means clustering method is applied to discretise the matrix. An information granulation entropy approach, based on information science theory and data mining technique, is then developed to determine the weights of criteria. Finally, TOPSIS closeness rating method is applied to derive the priorities of alternatives. To demonstrate its validity, we present a real-world application for selecting a third-party logistics provider. The proposed evaluation framework is particularly beneficial when dealing with large-scale, diverse criteria and alternatives.