A parametric approach to fuzzy linear programming
Fuzzy Sets and Systems
A solution algorithm for fuzzy linear programming with piecewise linear membership functions
Fuzzy Sets and Systems
A general approach to solving a wide class of fuzzy optimization problems
Fuzzy Sets and Systems
A differential equation approach to fuzzy non-linear programming problems
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Hybrid genetic algorithm for optimization problems with permutation property
Computers and Operations Research
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A hierarchy of evolution programs: An experimental study
Evolutionary Computation
Similarity relations and fuzzy orderings
Information Sciences: an International Journal
Solving fuzzy inequalities with piecewise linear membership functions
IEEE Transactions on Fuzzy Systems
Hybrid pattern search and simulated annealing for fuzzy production planning problems
Computers & Mathematics with Applications
An adaptive rule-based approach for managing situation-awareness
Expert Systems with Applications: An International Journal
Purposeful model parameters genesis in simple genetic algorithms
Computers & Mathematics with Applications
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Dynamic security consideration in multiobjective electricity markets
Applied Soft Computing
Engineering Applications of Artificial Intelligence
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Many engineering, science, information technology and management optimization problems can be considered as non-linear programming real-world problems where all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research paper is to solve non-linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers, which was represented by logistic membership functions using the hybrid evolutionary optimization approach. To explore the applicability of the present study, a numerical example is considered to determine the production planning for the decision variables and profit of the company.