%0 Journal Article
%T A New Dynamic Random Fuzzy DEA Model to Predict Performance of Decision Making Units
%J Journal of Optimization in Industrial Engineering
%I QIAU
%Z 2251-9904
%A Yaghoubi, Ali
%A Amiri, Maghsoud
%A Safi Samghabadi, Azamdokht
%D 2016
%\ 09/25/2016
%V 9
%N 20
%P 75-90
%! A New Dynamic Random Fuzzy DEA Model to Predict Performance of Decision Making Units
%K Stochastic Data envelopment analysis
%K Dynamic programming
%K random fuzzy variable
%K Monte Carlo simulation
%K Genetic Algorithm
%R 10.22094/joie.2016.252
%X 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
%U https://jie.qazvin.iau.ir/article_252_f499261f1b44c7e8bb765919ba8765a3.pdf