Maximum Random Fuzzy Weighted Matching Models and Hybrid Genetic Algorithm
نویسندگان
چکیده
The maximum weighted matching problem is to find a maximum matching in a given graph such that the sum of weights of edges in it is maximum. In this paper, the concept of maximum random fuzzy weighted matching is proposed firstly, and then the maximum random fuzzy weighted matching problem is formulated as expected value model, chance-constrained programming and dependent-chance programming according to various decision criteria. Furthermore, a hybrid genetic algorithm is designed to solve the proposed random fuzzy programming models and finally a corresponding numerical example is presented.
منابع مشابه
A Hybrid Approach for Fuzzy Just-In-Time Flow Shop Scheduling with Limited Buffers and Deteriorating Jobs
This paper investigates the problem of just-in-time permutation flow shop scheduling with limited buffers and linear job deterioration in an uncertain environment. The fuzzy set theory is applied to describe this situation. A novel mixed-integer nonlinear program is presented to minimize the weighted sum of fuzzy earliness and tardiness penalties. Due to the computational complexities, the prop...
متن کاملA Novel Fuzzy-Genetic Differential Evolutionary Algorithm for Optimization of A Fuzzy Expert Systems Applied to Heart Disease Prediction
This study presents a novel intelligent Fuzzy Genetic Differential Evolutionary model for the optimization of a fuzzy expert system applied to heart disease prediction in order to reduce the risk of heart disease. To this end, a fuzzy expert system has been proposed for the prediction of heart disease. The proposed model can be used as a tool to assist physicians. In order to: (1) tune the para...
متن کاملMEAN-ABSOLUTE DEVIATION PORTFOLIO SELECTION MODEL WITH FUZZY RETURNS
In this paper, we consider portfolio selection problem in which security returns are regarded as fuzzy variables rather than random variables. We first introduce a concept of absolute deviation for fuzzy variables and prove some useful properties, which imply that absolute deviation may be used to measure risk well. Then we propose two mean-absolute deviation models by defining risk as abs...
متن کاملPrediction of Gain in LD-CELP Using Hybrid Genetic/PSO-Neural Models
In this paper, the gain in LD-CELP speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (PSO) algorithms to optimize the structure and parameters of neural networks. Elman, multi-layer perceptron (MLP) and fuzzy ARTMAP are the candidate neural models. The optimized number of nodes in the first and second hidden layers of El...
متن کاملAdaptive Network-based Fuzzy Inference System-Genetic Algorithm Models for Prediction Groundwater Quality Indices: a GIS-based Analysis
The prediction of groundwater quality is very important for the management of water resources and environmental activities. The present study has integrated a number of methods such as Geographic Information Systems (GIS) and Artificial Intelligence (AI) methodologies to predict groundwater quality in Kerman plain (including HCO3-, concentrations and Electrical Conductivity (EC) of groundwater)...
متن کامل