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Innovative Hybrid Genetic Algorithms and Line Search Method for Industrial Production Management

Innovative Hybrid Genetic Algorithms and Line Search Method for Industrial Production Management

ISBN13: 9781615208098|ISBN10: 1615208097|EISBN13: 9781615208104
DOI: 10.4018/978-1-61520-809-8.ch008
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MLA

Vasant, Pandian M. "Innovative Hybrid Genetic Algorithms and Line Search Method for Industrial Production Management." Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques, edited by Monica Chis, IGI Global, 2010, pp. 142-160. https://doi.org/10.4018/978-1-61520-809-8.ch008

APA

Vasant, P. M. (2010). Innovative Hybrid Genetic Algorithms and Line Search Method for Industrial Production Management. In M. Chis (Ed.), Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques (pp. 142-160). IGI Global. https://doi.org/10.4018/978-1-61520-809-8.ch008

Chicago

Vasant, Pandian M. "Innovative Hybrid Genetic Algorithms and Line Search Method for Industrial Production Management." In Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques, edited by Monica Chis, 142-160. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-809-8.ch008

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Abstract

Many engineering, science, information technology and management optimization problems can be considered as non linear programming real world problems where the 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 chapter 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 by using 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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