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.

scholarly journals Multi-Objective Assembly Line Balancing Problem with Setup Times Using Fuzzy Goal Programming and Genetic Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 333
Author(s):  
Amy H. I. Lee ◽  
He-Yau Kang ◽  
Chong-Lin Chen
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Assembly Line ◽  
Programming Model ◽  
Assembly Line Balancing ◽  
Linear Programming Model ◽  
Line Balancing ◽  
Multi Objective ◽  
Assembly Line Balancing Problem ◽  
Genetic Algorithm Model

Assembly lines are often indispensable in factories, and in order to attain a certain level of assembly line productivity, multiple goals must be considered at the same time. However, these multiple goals may conflict with each other, and this is a multi-objective assembly line balancing problem. This study considers four objectives, namely minimizing the cycle time, minimizing the number of workstations, minimizing the workload variance, and minimizing the workstation idle time. Since the objectives conflict with each other, for example, minimizing the cycle time may increase the number of workstations, the fuzzy multi-objective linear programming model is used to maximize the satisfaction level. When the problem becomes too complicated, it may not be solved by the fuzzy multi-objective linear programming model using a mathematical software package. Therefore, a genetic algorithm model is proposed to solve the problem efficiently. By studying practical cases of an automobile manufacturer, the results show that the proposed fuzzy multi-objective linear programming model and the genetic algorithm model can solve small-scale multi-objective assembly line balancing problems efficiently, and the genetic algorithm model can obtain good solutions for large-scale problems in a short computational time. Datasets from previous works are adopted to examine the applicability of the proposed models. The results show that both the fuzzy multi-objective linear programming model and the genetic algorithm model can solve the smaller problem cases and that the genetic algorithm model can solve larger problems. The proposed models can be applied by practitioners in managing a multi-objective assembly line balancing problem.

Sign in / Sign up

Export Citation Format

Share Document