scholarly journals Research on Industrial Assembly Line Balancing Optimization Based on Genetic Algorithm and Witness Simulation

2017 ◽  
Vol 16 (2) ◽  
pp. 334-342 ◽  
Author(s):  
Y. Wang ◽  
O. Yang
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Industrial Assembly

Genetic algorithm for type-2 assembly line balancing

2015 ◽  
Author(s):  
Celso Gustavo Stall Sikora ◽  
Thiago Cantos Lopes ◽  
Heitor Silverio Lopes ◽  
Leandro Magatao
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing

scholarly journals Multi-objective assembly line balancing using genetic algorithm

2011 ◽  
Vol 2 (4) ◽  
pp. 863-872 ◽  
Author(s):  
Samad Ayazi ◽  
Abdol Naser Hajizadeh ◽  
Mostafa Emrani Nooshabadi ◽  
Hamid reza Jalaie ◽  
Yaghoob Mohammad moradi
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Multi Objective

Hull mixed-model assembly line balancing using a multi-objective genetic algorithm simulated annealing optimization approach

2016 ◽  
Vol 25 (1) ◽  
pp. 30-40 ◽  
Author(s):  
Yu-guang Zhong
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Load Balancing ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Optimization Approach ◽  
Multi Objective ◽  
Mutation Operators ◽  
Mixed Model Assembly Line

Hull assembly line balancing has significant impact on performance of shipbuilding system and is usually a multi-objective optimization problem. In this article, the primary objectives of the hull assembly line balancing are to minimize the number of workstations, to minimize the static load balancing index, to minimize the dynamic load balancing index between workstations, and to minimize the multi-station-associated complexity. Because this problem comes under combinatorial optimization category and is non-deterministic polynomial-time hard, an improved genetic algorithm simulated annealing is presented. In genetic algorithm simulated annealing, the task sequence numbers are used as chromosomes, and selection, crossover, and mutation operators only deal with the elements of task set instead of the ones of the problem space. In order to prevent the algorithm appearing early convergence or getting local optimal result, the simulated annealing algorithm is used to deal with the individuals. Meanwhile, the algorithm is embedded with the hierarchical scheduling tactics in order to solve the selection problem on optimal solution in the Pareto-optimal set. A number of benchmark problems are solved to prove the superior efficiency of the proposed algorithm. Finally, a case study of the optimization of a hull assembly line was given to illustrate the feasibility and effectiveness of the method.

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