INTEGRATION OF DATA ENVELOPMENT ANALYSIS WITH DECISION MAKER PREFERENCE FOR SUPPLIER SELECTION

Authors

  • Vahid Nourbakhsh Amirkabir University of Technology
  • Masoud Mahootchi Amirkabir University of Technology
  • Abbas Ahmadi Amirkabir University of Technology
  • Farzad Mahmoodi School of Business, Clarkson University

DOI:

https://doi.org/10.23055/ijietap.2020.27.6.3092

Keywords:

supplier selection, decision maker preference, data envelopment analysis, particle swarm optimization

Abstract

Data Envelopment Analysis (DEA) is a well-known method for selecting suppliers. Although Data Envelopment Analysis can compare suppliers in terms of their efficiencies, it cannot capture suppliers’ effectiveness and decision maker’s preference over criteria in the suppliers’ scores. We introduce a function called the Preference Function to capture the decision maker’s preference over different criteria. After applying Preference Functions on criteria’s data, we run Data Envelopment Analysis. Two different methods are proposed to derive the Preference Functions: direct and feedback-based (indirect). In the direct method, the decision-maker directly tailors Preference Functions to her preferences toward different criteria. In the feedback-based method, Preference Functions are formed via an optimization scheme. In this method, a mathematical model is solved, and then the result is used to iteratively build Preference Functions. We deploy the Particle Swarm Optimization technique to solve this problem. We illustrate both direct and indirect methods through solving a supplier selection example and compare it with plain Data Envelopment Analysis. Finally, through extensive numerical analysis, we show that Particle Swarm Optimization effectively solves the problem in the indirect method.

Published

2021-06-04

How to Cite

Nourbakhsh, V., Mahootchi, M., Ahmadi, A., & Mahmoodi, F. (2021). INTEGRATION OF DATA ENVELOPMENT ANALYSIS WITH DECISION MAKER PREFERENCE FOR SUPPLIER SELECTION. International Journal of Industrial Engineering: Theory, Applications and Practice, 27(6). https://doi.org/10.23055/ijietap.2020.27.6.3092

Issue

Section

Operations Research