Load Optimization Scheduling of Chip Mounter Based on Hybrid Adaptive Optimization Algorithm

Xuesong Yan; Hao Zuo; Chengyu Hu; Wenyin Gong; Victor S. Sheng

doi:10.23919/CSMS.2022.0026

Complex System Modeling and Simulation 2023, 3(1): 1-11 https://doi.org/10.23919/CSMS.2022.0026

Open Access | Issue | Published: 09 March 2023

Load Optimization Scheduling of Chip Mounter Based on Hybrid Adaptive Optimization Algorithm

Show Author's Information Hide Author's Information Xuesong Yan^¹, Hao Zuo^¹, Chengyu Hu^¹(

), Wenyin Gong^¹, Victor S. Sheng^²

1School of Computer Science, China University of Geosciences, Wuhan 430074, China

2Department of Computer Science, Texas Tech University, Lubbock, TX 79409−3104, USA

Keywords:

ant colony algorithm, Surface Mount Technology (SMT), chip mounter, load optimization scheduling, adaptive genetic algorithm

Cite this article:

Yan X, Zuo H, Hu C, et al. Load Optimization Scheduling of Chip Mounter Based on Hybrid Adaptive Optimization Algorithm. Complex System Modeling and Simulation, 2023, 3(1): 1-11. https://doi.org/10.23919/CSMS.2022.0026

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Abstract

A chip mounter is the core equipment in the production line of the surface-mount technology, which is responsible for finishing the mount operation. It is the most complex and time-consuming stage in the production process. Therefore, it is of great significance to optimize the load balance and mounting efficiency of the chip mounter and improve the mounting efficiency of the production line. In this study, according to the specific type of chip mounter in the actual production line of a company, a maximum and minimum model is established to minimize the maximum cycle time of the chip mounter in the production line. The production efficiency of the production line can be improved by optimizing the workload scheduling of each chip mounter. On this basis, a hybrid adaptive optimization algorithm is proposed to solve the load scheduling problem of the mounter. The hybrid algorithm is a hybrid of an adaptive genetic algorithm and the improved ant colony algorithm. It combines the advantages of the two algorithms and improves their global search ability and convergence speed. The experimental results show that the proposed hybrid optimization algorithm has a good optimization effect and convergence in the load scheduling problem of chip mounters.

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Load Optimization Scheduling of Chip Mounter Based on Hybrid Adaptive Optimization Algorithm

Show Author's information Hide Author's Information Xuesong Yan^¹, Hao Zuo^¹, Chengyu Hu^¹(

), Wenyin Gong^¹, Victor S. Sheng^²

1School of Computer Science, China University of Geosciences, Wuhan 430074, China

2Department of Computer Science, Texas Tech University, Lubbock, TX 79409−3104, USA

Abstract

Keywords: ant colony algorithm, Surface Mount Technology (SMT), chip mounter, load optimization scheduling, adaptive genetic algorithm

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Publication history

Received: 12 November 2022

Revised: 29 November 2022

Accepted: 01 December 2022

Published: 09 March 2023

Issue date: March 2023

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Acknowledgements

Acknowledgment

This paper was supported by the National Natural Science Foundation of China (Nos. U1911205, 62073300, and 62076225) and the National Key Research and Development Program of China (No. 2021YFB3301602).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).