AN INHOMOGENEOUS MULTI-ATTRIBUTE DECISION MAKING METHOD AND APPLICATION TO IT/IS OUTSOURCING PROVIDER SELECTION

Authors

  • Rui Qiang Fuzhou University
  • Debiao Li Fuzhou University

DOI:

https://doi.org/10.23055/ijietap.2015.22.2.1110

Abstract

Selecting the suitable outsourcing provider is one of the most critical activities in supply chain management. In this paper, a new fuzzy linear programming method is proposed to select outsourcing providers by formulating it as a fuzzy inhomogeneous multi-attribute decision making (MADM) problems with fuzzy truth degrees and incomplete weight information. In this method, the decision maker’s preferences are represented as trapezoidal fuzzy numbers (TrFNs), which obtained through pair-wise comparisons of alternatives. Based on the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS), the fuzzy consistency and inconsistency indices are defined by the relative closeness degrees in TrFNs. The attribute weights are estimated by solving the proposed fuzzy linear programming. And then the selection ranking is determined by the comprehensive relative closeness degree of each alternative to the FPIS. An industrial IT outsourcing provider selection example is analyzed to demonstrate the implementation process of this method.

Author Biography

Rui Qiang, Fuzhou University

Rui Qiang was born in 1963. He received Ph.D. degrees of management from Wuhan University of Technology. He is now an associate professor of School of management, Fuzhou University. His main research interests are supply chain management, quality management and industrial engineering. In recent years, he also focused on the area of Enterprise Energy Saving and Emission Reduction. He led 5 subject research projects，published one academic monograph and several papers in these related academic areas. 

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Published

2015-03-15

How to Cite

Qiang, R., & Li, D. (2015). AN INHOMOGENEOUS MULTI-ATTRIBUTE DECISION MAKING METHOD AND APPLICATION TO IT/IS OUTSOURCING PROVIDER SELECTION. International Journal of Industrial Engineering: Theory, Applications and Practice, 22(2). https://doi.org/10.23055/ijietap.2015.22.2.1110

Issue

Vol. 22 No. 2 (2015)

Section

Operations Research

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