Abstract
In recent years, determining an appropriate supplier has become a crucial strategic consideration in a competitive market – with data envelopment analysis (DEA) methods increasingly important in this respect. DEA traditionally requires that the values for all inputs and outputs be known exactly. However, this assumption may not be true, because data in many real applications cannot be precisely measured. A successful approach for addressing uncertainty in data is to replace deterministic data with random variables, leading to chance-constrained DEA. In this article, the concept of chance-constrained programming is used to develop a Worst-practice frontier-Charnes-Cooper-Rhodes model and also its deterministic equivalent. Furthermore, it is shown that the latter can be formulated as a quadratic program. Finally, a numerical example demonstrates the application of the proposed model.
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Notes
For more details, please see Liu and Chen (2009).
Notice that the conversion process has been discussed for constraint 1 in Model (7). For constraint 2 the same process could be repeated.
The inputs and outputs selected in this article are not exhaustive by any means, but are some general measures that can be utilized to evaluate suppliers. In an actual application of this methodology, decision makers must carefully identify appropriate inputs and outputs measures to be used in the decision-making process.
Variance parameters have been obtained with the use of panel data. For more information on estimation issues using rotating panel data, please see Biorn (1981), Biorn and Jansen (1983), Kumbhakar and Heshmati (1991), Ivaldi et al (1995), and Hendershott et al (2002).
Banker-Charnes-Cooper (BCC).
For more details, please see Liu and Chen (2009).
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Azadi, M., Saen, R. Developing a WPF-CCR model for selecting suppliers in the presence of stochastic data. OR Insight 24, 31–48 (2011). https://doi.org/10.1057/ori.2010.16
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DOI: https://doi.org/10.1057/ori.2010.16