Dynamic Temperature Simulated Annealing Algorithm for the PCB Assembly Process

The optimization performance of surface mount machines plays a critical role in determining the production efficiency of surface mount production lines. This article decomposes the surface mount machine optimization problem into two sub-problems, component assignment and feeder allocation. The mathematical models of these two problems are established, and a dynamic temperature simulated annealing (DTSA) algorithm is proposed to solve them. DTSA introduces a dynamic temperature adjustment mechanism that adaptively modifies the temperature parameters. This mechanism effectively addresses the exploration deficiency typically encountered in the later stages of simulated annealing (SA) while mitigating the risk of non-convergence caused by excessively high temperatures. Moreover, DTSA incorporates a convex hull greedy algorithm for calculating the distance during component assignment and feeder assignment solving. Experimental results demonstrate that DTSA significantly outperforms traditional algorithms, offers superior solution quality and faster convergence speed.

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Publication Details

Published
2025-07-04
DOI
https://doi.org/10.1109/fasta65681.2025.11138635
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
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article

Dynamic Temperature Simulated Annealing Algorithm for the PCB Assembly Process

Guangyu Lu, Zhitai Liu, Bin Yong, Lilong Yang
Advanced Manufacturing and Logistics Optimization
article

Dynamic Temperature Simulated Annealing Algorithm for the PCB Assembly Process

Guangyu Lu, Zhitai Liu, Bin Yong, Lilong Yang
article en

Abstract

The optimization performance of surface mount machines plays a critical role in determining the production efficiency of surface mount production lines. This article decomposes the surface mount machine optimization problem into two sub-problems, component assignment and feeder allocation. The mathematical models of these two problems are established, and a dynamic temperature simulated annealing (DTSA) algorithm is proposed to solve them. DTSA introduces a dynamic temperature adjustment mechanism that adaptively modifies the temperature parameters. This mechanism effectively addresses the exploration deficiency typically encountered in the later stages of simulated annealing (SA) while mitigating the risk of non-convergence caused by excessively high temperatures. Moreover, DTSA incorporates a convex hull greedy algorithm for calculating the distance during component assignment and feeder assignment solving. Experimental results demonstrate that DTSA significantly outperforms traditional algorithms, offers superior solution quality and faster convergence speed.

Harbin Institute of Technology (CN), Heilongjiang Institute of Technology (CN)
Openalex Percentile: Top 66%
Advanced Manufacturing and Logistics Optimization
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