scholarly journals Research on Automobile Assembly Line Optimization Based on Industrial Engineering Technology and Machine Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaorui Shi ◽  
Wei Cui ◽  
Ping Zhu ◽  
Yanhua Yang
Keyword(s):  
Genetic Algorithm ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Assembly Line ◽  
Analysis Method ◽  
Improved Genetic Algorithm ◽  
Line Balance ◽  
Automobile Assembly ◽  
Assembly Line Balance ◽  
Population Clustering

Aiming at the lack of search depth of traditional genetic algorithm in automobile assembly line balance optimization, an improved genetic algorithm based on bagging integrated clustering is proposed for balance optimization. Through the integrated learning of several K -means algorithm based learners through bagging, a population clustering analysis method based on bagging integrated clustering algorithm is established, and then, a dual objective automobile assembly line balance optimization model is established. The population clustering analysis method is used to improve the intersection link of genetic algorithm to improve the search depth. The effectiveness and search performance of the improved genetic algorithm in solving the double objective assembly line balance problem are verified in an example.

Study on Multi-Specification Small-Batch Line Balancing and Optimization Base on the Streamline Balancing

2014 ◽  
Vol 635-637 ◽  
pp. 1952-1955 ◽  
Author(s):  
Ji Jun Xiao ◽  
Shu Ou ◽  
Qing Hua Zhou ◽  
Hao Dong
Keyword(s):  
Production Line ◽  
Assembly Line ◽  
Mixed Model ◽  
Balance Method ◽  
Line Balancing ◽  
Workplace Design ◽  
Work Processes ◽  
Batch Production ◽  
Line Balance ◽  
Assembly Line Balance

Based on the principal concepts of streamline balance, this paper analyses the issue of the line balancing, through analyzing traditional assembly line balance method and having the bottleneck work processes to amend the parameters of the Workplace design, and then building up a multi-variety small-batch production line balancing mixed-model.

Weighted K-Means Clustering Analysis Based on Improved Genetic Algorithm

2014 ◽  
Vol 511-512 ◽  
pp. 904-908 ◽  
Author(s):  
Tong Jie Zhang ◽  
Yan Cao ◽  
Xiang Wei Mu
Keyword(s):  
Genetic Algorithm ◽  
Clustering Analysis ◽  
Local Optimum ◽  
Speed Of Convergence ◽  
Improved Genetic Algorithm ◽  
Low Speed ◽  
Dynamic Mutation ◽  
Mutation Operators

An algorithm of weighted k-means clustering is improved in this paper, which is based on improved genetic algorithm. The importance of different contributors in the process of manufacture is not the same when clustering, so the weight values of the parameters are considered. Retaining the best individuals and roulette are combined to decide which individuals are chose to crossover or mutation. Dynamic mutation operators are used here to decrease the speed of convergence. Two groups of data are used to make comparisons among the three algorithms, which suggest that the algorithm has overcome the problems of local optimum and low speed of convergence. The results show that it has a better clustering.

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