Solving fuzzy chance-constrained programming with ant colony optimization-based algorithms and application to fuzzy inventory model

Authors:
Yuanguo Zhu
Affiliations:
Department of Applied Mathematics, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

Citing 7
Cited 1

Fuzzy set theoretic interpretation of economic order quantity

IEEE Transactions on Systems, Man and Cybernetics
Chance constrained programming with fuzzy parameters

Fuzzy Sets and Systems
Theory and Practice of Uncertain Programming

Theory and Practice of Uncertain Programming
Optimization of fuzzy production inventory models

Information Sciences—Applications: An International Journal
Data mining with an ant colony optimization algorithm

IEEE Transactions on Evolutionary Computation
A short convergence proof for a class of ant colony optimizationalgorithms

IEEE Transactions on Evolutionary Computation
Expected value of fuzzy variable and fuzzy expected value models

IEEE Transactions on Fuzzy Systems

An intelligent algorithm: MACO for continuous optimization models

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

An ant colony optimization algorithm is designed to solve continuous optimization models. Based on this algorithm, a hybrid intelligent algorithm combined with fuzzy simulation and neural network is introduced for solving fuzzy chance constrained models. Several numerical examples are given to show the algorithms effective. As an application, a fuzzy inventory model is established and solved with the hybrid intelligent algorithm.