scholarly journals A note on “A multi-objective genetic algorithm for solving assembly line balancing problem”

2010 ◽  
Vol 50 (5-8) ◽  
pp. 771-773 ◽  
Author(s):  
Masood Fathi ◽  
M. K. A. Ariffin ◽  
Napsiah Ismail
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Assembly Line Balancing Problem

A Genetic Algorithm for the Mixed-Model U-Line Balancing Problem

2013 ◽  
Vol 694-697 ◽  
pp. 3391-3394 ◽  
Author(s):  
Hai Dong ◽  
Jian Hua Cao ◽  
Wei Ling Zhao
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Mixed Model ◽  
Assembly Line Balancing ◽  
Performance Criteria ◽  
Line Balancing ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Proposed Model ◽  
Assembly Line Balancing Problem

This paper presents a multi-objective genetic algorithm to solve the U-shaped assembly line balancing problem. The performance criteria for the number of workstations and the variation of workload are considered. The results of experiments show that the proposed model produced as good or even better line efficiency of workstation integration and improved the variation of workload.

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.

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