@article {
author = {Yaghoubi, Ali and Amiri, Maghsoud and Safi Samghabadi, Azamdokht},
title = {A New Dynamic Random Fuzzy DEA Model to Predict Performance of Decision Making Units},
journal = {Journal of Optimization in Industrial Engineering},
volume = {9},
number = {20},
pages = {75-90},
year = {2016},
publisher = {QIAU},
issn = {2251-9904},
eissn = {2423-3935},
doi = {10.22094/joie.2016.252},
abstract = {Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of decision making units (DMUs) which â€Žconsume the same types of inputs and producing the same types of outputs. Believing that future planning and predicting the â€Žefficiency are very important for DMUs, this paper first presents a new dynamic random fuzzy DEA model (DRF-DEA) with â€Žcommon weights (using multi objective DEA approach) to predict the efficiency of DMUs under mean chance constraints and â€Žexpected values of the objective functions. In the initial proposedâ€ â€DRF-DEA model, the inputs and outputs are assumed to be â€Žcharacterized by random triangular fuzzy variables with normal distribution, in which data are changing sequentially. Under this â€Žassumption, the solution process is very complex. So we then convert the initial proposed DRF-DEA model to its equivalent multi-â€Žobjective stochastic programming, in which the constraints contain the standard normal distribution functions, and the objective â€Žfunctions are the expected values of functions of normal random variables. In order to improve in computational time, we then â€Žconvert the equivalent multi-objective stochastic model to one objective stochastic model with using fuzzy multiple objectives â€Žprogramming approach. To solve it, we design a new hybrid algorithm by integrating Monte Carlo (MC) simulation and Genetic â€ŽAlgorithm (GA). Since no benchmark is available in the literature, one practical example will be presented. The computational results â€Žshow that our hybrid algorithm outperforms the hybrid GA algorithm which was proposed by Qin and Liu (2010) in terms of â€Žruntime and solution quality. â€Ž},
keywords = {Stochastic Data envelopment analysis,Dynamic programming,random fuzzy variable,Monte Carlo simulation,Genetic Algorithm},
url = {https://jie.qazvin.iau.ir/article_252.html},
eprint = {https://jie.qazvin.iau.ir/article_252_f499261f1b44c7e8bb765919ba8765a3.pdf}
}