Research on Workshop Layout Based on Genetic Algorithm of Machine Learning K-means Clustering

DOI

10.2991/978-94-6463-447-1_20How to use a DOI?

Keywords

Workshop layout; genetic algorithm; machine learning; K-means clustering

Abstract

Aiming at the problems of logistics confusion and low efficiency among equipment caused by unreasonable workshop layout, this problem can be effectively solved by optimizing mathematical model and adopting improved genetic algorithm based on multi-objective. On the basis of classical genetic algorithm, a multi-strategy parallel genetic algorithm based on machine learning is proposed, and the performance of genetic algorithm is improved by using machine learning method. Firstly, the parallel idea is used to accelerate the evolution process of genetic algorithm, and the initial population is divided into multiple clusters by using K-means clustering algorithm. Then, reinforcement learning is introduced to realize the self-learning of the crossover probability of important parameters in genetic algorithm, so that the crossover probability can adapt to the evolution process according to experience. The experimental results show that the multi-strategy parallel genetic algorithm of machine learning is obviously superior to the classical genetic algorithm, which can optimize the original layout of the workshop well and improve the effect significantly.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hongrun Pang
AU  - Chengjun Ji
PY  - 2024
DA  - 2024/07/14
TI  - Research on Workshop Layout Based on Genetic Algorithm of Machine Learning K-means Clustering
BT  - Proceedings of the 2024 3rd International Conference on Engineering Management and Information Science (EMIS 2024)
PB  - Atlantis Press
SP  - 173
EP  - 183
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-447-1_20
DO  - 10.2991/978-94-6463-447-1_20
ID  - Pang2024
ER  -