The Improved Genetic Algorithm for Balancing Mixed-Model Assembly Line

2011 ◽  
Vol 127 ◽  
pp. 603-608
Author(s):  
Qiu Hua Tang ◽  
Yan Li Liang
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Mixed Model ◽  
Assembly Line Balancing ◽  
Improved Genetic Algorithm ◽  
Assembly Lines ◽  
Mixed Model Assembly Lines ◽  
Assembly Line Balancing Problem ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

Mixed-model assembly lines are become more and more important by producing different models of the same product on an assembly line. Aiming at the existing mixed-model assembly line balancing problem, first, two important objective functions for minimizing cycle time and workload variance were provided, and mathematical models were established. Furthermore, in order to obtain the optimal or near optimal solutions, an improved genetic algorithm was proposed with combined precedence graph. Finally, the experiment results illustrate the feasibility and validity of the proposed improved genetic algorithm.

scholarly journals A Hybrid Grasp-genetic Algorithm for Mixed-model Assembly Line Balancing Problem Type 2

2021 ◽  
pp. 424-432
Author(s):  
Lakhdar Belkharroubi ◽  
Khadidja Yahyaoui
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Mixed Model ◽  
Assembly Line Balancing ◽  
Search Procedure ◽  
Problem Type ◽  
Assembly Line Balancing Problem ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

In manufacturing systems, mixed model assembly lines are used to produce different products to deal with the problem of customers’ demands variety, and minimizing the cycle time in such assembly line is a critical problem. This paper addresses the mixed model assembly line balancing problem type 2 that consists in finding the optimal cycle time for a given number of workstations.  A hybrid Greedy randomized adaptive search procedure-Genetic algorithm is proposed to find the optimal assignment of tasks among workstations that minimize the cycle. A Ranked Positional Weight heuristic is used in the construction phase of the proposed GRASP, and in the local search phase, a neighborhood search procedure is used to ameliorate the constructed solutions in the construction phase. The GRASP is executed many times in order to seed the initial population of the proposed genetic algorithm, and the results of the executions are compared with the final solutions obtained by the hybrid GRASP-GA. In order to test the proposed approaches, a numerical example is used.

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