Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function

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 techniq...

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Main Author: Vasant, Pandian
Format: Article
Language:English
Published: PERGAMON 2009
Subjects:
Online Access:http://scholars.utp.edu.my/id/eprint/2038/
http://scholars.utp.edu.my/id/eprint/2038/1/PV100.pdf
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author Vasant, Pandian
author_facet Vasant, Pandian
author_sort Vasant, Pandian
building UTP Institutional Repository
collection Online Access
description 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.
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spelling oai:scholars.utp.edu.my:20382017-01-19T08:25:23Z http://scholars.utp.edu.my/id/eprint/2038/ Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function Vasant, Pandian TS Manufactures 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. PERGAMON 2009-06 Article PeerReviewed application/pdf en http://scholars.utp.edu.my/id/eprint/2038/1/PV100.pdf Vasant, Pandian (2009) Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function. Engineering Applications of Artificial Intelligence , 22 (4-5). pp. 767-777. ISSN 0952-1976 http://www.elsevier.com
spellingShingle TS Manufactures
Vasant, Pandian
Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function
title Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function
title_full Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function
title_fullStr Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function
title_full_unstemmed Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function
title_short Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function
title_sort hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function
topic TS Manufactures
url http://scholars.utp.edu.my/id/eprint/2038/
http://scholars.utp.edu.my/id/eprint/2038/
http://scholars.utp.edu.my/id/eprint/2038/1/PV100.pdf