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.
Additional information
Notes on contributors
Majid Azadi
Majid Azadi is a Strategic and Operational Management Consultant. His research interests include productivity analysis, artificial neural networks, integer programming, game theory, nonlinear programming, supply chain management, third-party reverse logistics, genetic algorithm and goal programming.
Reza Farzipoor Saen
Reza Farzipoor Saen is an associate professor in the Department of International Business and Asian Studies at the Griffith University in Australia. He obtained his PhD in Industrial Management from the Islamic Azad University Science and Research Branch in Iran. He has published over 52 refereed papers in many prestigious journals such as Annals of Operations Research, Journal of the Operational Research Society, European Journal of Operational Research, Applied Mathematics and Computation, Applied Mathematical Modelling, World Applied Sciences Journal, International Journal of Advanced Manufacturing Technology, Journal of Modelling in Management, International Journals of Applied Management and Technology, Asia Pacific Management Review and so on.