Measuring efficiency in DEA by differential evolution algorithm

Document Type: Original Article

Author

Department of Mathematics, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran

10.22121/aotp.2019.191553.1020

Abstract

In Data Envelopment Analysis (DEA) models, for measuring the relative efficiency of Decision Making Units (DMUs), for a large dataset with many inputs/outputs would need to have a long time with a huge computer. This paper proposed and developed the Differential evolution (DE) for DEA. DE requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Since the operators have important roles on the fitness of the algorithms, all the operators and parameters are calibrated by means of the Taguchi experimental design in order to improve their performances.

Keywords


Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization11(4), 341-359.

Azadeh, A., Asadzadeh, S. M., & Ahmadi Movaghar, S. (2011). Implementation of data envelopment analysis–genetic algorithm for improved performance assessment of transmission units in power industry. International Journal of Industrial and Systems Engineering8(1), 83-103.

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research2(6), 429-444.

Cooper, W. W., Seiford, L. M., & Tone, K. (2006). Introduction to data envelopment analysis and its uses: with DEA-solver software and references. Springer Science & Business Media.

Emrouznejad, A., & Shale, E. (2009). A combined neural network and DEA for measuring efficiency of large scale datasets. Computers & Industrial Engineering56(1), 249-254.

Udhayakumar, A., Charles, V., & Kumar, M. (2011). Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems. Omega39(4), 387-397.

Thompson, R. G., & Thrall, R. M. (1994). Polyhedral assurance regions with linked constraints. In New Directions in Computational Economics (pp. 121-133). Springer, Dordrecht.

Mahmoodirad, A., & Sanei, M. (2016). Solving a multi-stage multi-product solid supply chain network design problem by meta-heuristics. Scientia Iranica E23(3), 1428-1440.

Molla, a. z. a., Sanei, M., Soltani, R., & Mahmoodirad, A. (2014). Solving a step fixed charge transportation problem by a spanning tree-based memetic algorithm.