A New Dynamic Random Fuzzy DEA Model to Predict Performance of Decision Making Units
Author(s):
Abstract:
Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of decision making units (DMUs) which âŽconsume the same types of inputs and producing the same types of outputs. Believing that future planning and predicting the âŽefficiency are very important for DMUs, this paper first presents a new dynamic random fuzzy DEA model (DRF-DEA) with âŽcommon weights (using multi objective DEA approach) to predict the efficiency of DMUs under mean chance constraints and âŽexpected values of the objective functions. In the initial proposedâ âDRF-DEA model, the inputs and outputs are assumed to be âŽcharacterized by random triangular fuzzy variables with normal distribution, in which data are changing sequentially. Under this âŽassumption, the solution process is very complex. So we then convert the initial proposed DRF-DEA model to its equivalent multi-âŽobjective stochastic programming, in which the constraints contain the standard normal distribution functions, and the objective âŽfunctions are the expected values of functions of normal random variables. In order to improve in computational time, we then âŽconvert the equivalent multi-objective stochastic model to one objective stochastic model with using fuzzy multiple objectives âŽprogramming approach. To solve it, we design a new hybrid algorithm by integrating Monte Carlo (MC) simulation and Genetic âŽAlgorithm (GA). Since no benchmark is available in the literature, one practical example will be presented. The computational results âŽshow that our hybrid algorithm outperforms the hybrid GA algorithm which was proposed by Qin and Liu (2010) in terms of âŽruntime and solution quality. âŽ
Keywords:
Language:
English
Published:
Journal of Optimization in Industrial Engineering, Volume:9 Issue: 20, Summer and Autumn 2016
Pages:
75 to 90
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