A decision-making methodology for vendor selection problem with uncertain inputs

Abstract

This paper presents a decision-making scheme for selecting appropriate method for supplier selection under certainly, uncertainly, and stochastic conditions. These models are categorized into three techniques containing deterministic data envelopments analysis (DEA), fuzzy data envelopment analysis (FDEA), and stochastic data envelopment analysis (SDEA). The models are written on the basis of an improved integrated DEA model (i.e. MiniMax model) to find the most efficient vendor. The objective of the model is to minimize the maximum inefficient variables. First, it is supposed that data are crisp and DEA model is developed. Second, it is assumed that data are not crisp and sufficient historical data are not available and fuzzy DEA model is employed. Third, for those cases that data are not crisp but sufficient historical data are available, stochastic DEA is used. Finally, average efficiency scores of decision-making units for each model under different random types of inputs are calculated and the results are analyzed. This is the first study that provides a flexible tool in order to select the most efficient vendors in uncertainty condition.

Keywords:

• Decision-making methodology
• Vendor selection problem
• Deterministic data envelopment analysis (deterministic DEA)
• Fuzzy DEA
• Stochastic DEA

Acknowledgment

The authors are grateful for the valuable comments and suggestions by the respected reviewers, which have enhanced the strength and significance of this paper. This study was supported by a grant from College of Engineering, University of Tehran [Grant No. 8106013/1/2].