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Hybrid Linear Search, Genetic Algorithms, and Simulated Annealing for Fuzzy Non-Linear Industrial Production Planning Problems

Hybrid Linear Search, Genetic Algorithms, and Simulated Annealing for Fuzzy Non-Linear Industrial Production Planning Problems

P. Vasant
Copyright: © 2013 |Pages: 23
ISBN13: 9781466620865|ISBN10: 1466620862|EISBN13: 9781466620872
DOI: 10.4018/978-1-4666-2086-5.ch003
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MLA

Vasant, P. "Hybrid Linear Search, Genetic Algorithms, and Simulated Annealing for Fuzzy Non-Linear Industrial Production Planning Problems." Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, edited by Pandian M. Vasant, IGI Global, 2013, pp. 87-109. https://doi.org/10.4018/978-1-4666-2086-5.ch003

APA

Vasant, P. (2013). Hybrid Linear Search, Genetic Algorithms, and Simulated Annealing for Fuzzy Non-Linear Industrial Production Planning Problems. In P. Vasant (Ed.), Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance (pp. 87-109). IGI Global. https://doi.org/10.4018/978-1-4666-2086-5.ch003

Chicago

Vasant, P. "Hybrid Linear Search, Genetic Algorithms, and Simulated Annealing for Fuzzy Non-Linear Industrial Production Planning Problems." In Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, edited by Pandian M. Vasant, 87-109. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2086-5.ch003

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Abstract

This chapter outlines an introduction to real-world industrial problem for product-mix selection involving eight variables and twenty one constraints with fuzzy technological coefficients, and thereafter, a formulation for an optimization approach to solve the problem. This problem occurs in production planning in which a decision maker plays a pivotal role in making decision under fuzzy environment. Decision-maker should be aware of his/her level of satisfaction as well as degree of fuzziness while making the product-mix decision. Thus, a thorough analysis is performed on a modified S-curve membership function for the fuzziness patterns and fuzzy sensitivity solution is found from the various optimization methodologies. An evolutionary algorithm is proposed to capture the optimal solutions respect to the vagueness factor and level of satisfaction. The near global optimal solution for objective function is obtained by hybrid meta-heuristics optimization algorithms such as line search, genetic algorithms, and simulated annealing.

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