Data envelopment analysis on a relaxed set of assumptions
Management Science
Chance constrained efficiency evaluation
Management Science
Fuzzy DEA: a perceptual evalution method
Fuzzy Sets and Systems
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Fuzzy data envelopment analysis (DEA): Model and ranking method
Journal of Computational and Applied Mathematics
Fuzzy data envelopment analysis and its application to location problems
Information Sciences: an International Journal
Modeling data envelopment analysis by chance method in hybrid uncertain environments
Mathematics and Computers in Simulation
Modeling fuzzy data envelopment analysis by parametric programming method
Expert Systems with Applications: An International Journal
Fuzzy data envelopment analysis: A fuzzy expected value approach
Expert Systems with Applications: An International Journal
Fuzzy data envelopment analysis: A discrete approach
Expert Systems with Applications: An International Journal
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
Convergent results about the use of fuzzy simulation in fuzzy optimization problems
IEEE Transactions on Fuzzy Systems
Fuzzy stochastic data envelopment analysis with application to base realignment and closure (BRAC)
Expert Systems with Applications: An International Journal
Evaluation method based on ranking in data envelopment analysis
Expert Systems with Applications: An International Journal
A concept of fuzzy input mix-efficiency in fuzzy DEA and its application in banking sector
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper presents a new satisficing data envelopment analysis (DEA) model with credibility criterion, in which the inputs and outputs are assumed to be characterized by fuzzy variables with known membership functions. When the inputs and outputs are mutually independent trapezoidal fuzzy variables, we turn the proposed satisficing DEA model into its deterministic equivalent programming problem. For general fuzzy input and output variables, we design a hybrid particle swarm optimization (PSO) algorithm by integrating approximation method, neural network (NN) and PSO algorithm to solve the proposed DEA model, in which the approximation method is used to compute the credibility functions, NN is used to approximate the credibility functions, and PSO is used to find the optimal solution of the proposed DEA problem. Furthermore, the sensitivity analysis of the proposed model is discussed. Finally, we perform a number of numerical experiments to demonstrate the effectiveness of the hybrid PSO algorithm. The computational results show that the designed hybrid PSO algorithm outperforms the hybrid genetic algorithm (GA) in terms of runtime and solution quality.