scholarly journals Performance Study of a Genetic Algorithm for Sequencing in Mixed Model Non-permutation Flowshops Using Constrained Buffers

2006 ◽  
pp. 638-648 ◽  
Author(s):  
Gerrit Färber ◽  
Anna M. Coves Moreno
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Performance Study

A Performance Study of Real Coded Genetic Algorithm on Gear Design Optimization

2012 ◽  
Vol 622-623 ◽  
pp. 64-68 ◽  
Author(s):  
S. Padmanabhan ◽  
M. Chandrasekaran ◽  
P. Asokan ◽  
V. Srinivasa Raman
Keyword(s):  
Genetic Algorithm ◽  
Power Efficiency ◽  
Mechanical Design ◽  
Design Solution ◽  
Functional Requirements ◽  
Performance Study ◽  
Gear Design ◽  
Adverse Result ◽  
Real Coded Genetic Algorithm ◽  
Comparative Results

he major problem that deals with practical engineers is the mechanical design and creativeness. Mechanical design can be defined as the choice of materials and geometry, which satisfies, specified functional requirements of that design. A good design has to minimize the most significant adverse result and to maximize the most significant desirable result. An evolutionary algorithm offers efficient ways of creating and comparing a new design solution in order to complete an optimal design. In this paper a type of Genetic Algorithm, Real Coded Genetic Algorithm (RCGA) is used to optimize the design of helical gear pair and a combined objective function with maximizes the Power, Efficiency and minimizes the overall Weight, Centre distance. The performance of the proposed algorithms is validated through LINGO Software and the comparative results are analyzed.

A Modified Genetic Algorithm for Sequencing Problem in Mixed Model Assembly Lines

2013 ◽  
Vol 655-657 ◽  
pp. 1675-1681
Author(s):  
Shu Xu ◽  
Fu Ming Li
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Assembly Lines ◽  
Sequencing Problem ◽  
Mixed Model Assembly Lines ◽  
Modified Genetic Algorithm ◽  
Good Ability ◽  
Sequencing Problems ◽  
Dependent Setup Times ◽  
Mixed Model Assembly

On the base of summarizing and contrasting the objectives of sequencing problem in mixed model assembly lines (MMAL) , and in consideration of the influence sequence-dependent setup times , a objective is proposed to minimize the total unfinished works and idle times over all jobs and stations . And the corresponding model is presented. To solve this model, a modified genetic algorithm is proposed to determine suitable sequences. Comparing with the Lingo 9 software, the proposed GA turns out to have a good ability to solve the sequencing problems.

An Improved Hybrid Genetic Algorithm and Performance Study

2012 ◽  
Vol 482-484 ◽  
pp. 95-98
Author(s):  
Wei Dong Ji ◽  
Ke Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Convergence Speed ◽  
Test Results ◽  
Improved Genetic Algorithm ◽  
Performance Study ◽  
Speed Up ◽  
And Performance

Put forward a kind of the hybrid improved genetic algorithm of particle swarm optimization method (PSO) combine with and BFGS algorithm of, this method using PSO good global optimization ability and the overall convergence of BFGS algorithm to overcome the blemish of in the conventional algorithm slow convergence speed and precocious and local convergence and so on. Through the three typical high dimensional function test results show that this method not only improved the algorithm of the global search ability, to speed up the convergence speed, but also improve the quality of the solution and its reliability of optimization results.

Simulated Annealing Genetic Algorithm and its Application in Mixed-Model Assembly Line Design

2010 ◽  
Vol 136 ◽  
pp. 64-68 ◽  
Author(s):  
Yan Jiang ◽  
Xiang Feng Li ◽  
Dun Wen Zuo ◽  
Guang Ming Jiao ◽  
Shan Liang Xue
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Local Search ◽  
Assembly Line ◽  
Mixed Model ◽  
Simulated Annealing Genetic Algorithm ◽  
Simple Genetic Algorithm ◽  
Mixed Model Assembly Line ◽  
Line Design ◽  
Mixed Model Assembly

Simple genetic algorithm has shortcomings of poor local search ability and premature convergence. To overcome these disadvantages, simulated annealing algorithm which has good local search ability was combined with genetic algorithm to form simulated annealing genetic algorithm. The tests by two commonly used test functions of Shaffer’s F6 and Rosenbrock show that simulated annealing genetic algorithm outperforms the simple genetic algorithm both in convergence rate and convergence quality. Finally, the simulated annealing genetic algorithm was firstly applied in a practical problem of balancing and sequencing design of mixed-model assembly line, once again, the solution results show that simulated annealing genetic algorithm outperforms the simple genetic algorithm. Meanwhile, it provides a new algorithm for solving the design problem of mixed-model assembly line.

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