The optimal configuration for various placement machines in PCB assembly lines

Tzu-Li Chen, James C. Chen, Yin-Yann Chen () and Yu-Jie Chang
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Tzu-Li Chen: National Taiwan University of Science and Technology
James C. Chen: National Tsing Hua University
Yin-Yann Chen: National Formosa University
Yu-Jie Chang: National Tsing Hua University

Annals of Operations Research, 2025, vol. 349, issue 1, No 17, 365-396

Abstract: Abstract Surface Mount Technology (SMT) has become widely adopted in the electronic assembly industry for printed circuit board (PCB) processes. In most instances, the placement machine is a bottleneck within the assembly line. Consequently, it is crucial to optimize the utilization of these machines to enhance the PCB assembly system, particularly in ultra-high-mix, low-volume production environments. In the past, the excessively high cost of equipment configuration led to less consideration of the combinatorial optimization problem involving different placement machines, which included factors such as chip size, height, and accuracy. As a result, machines were underutilized, as previous combination modes could not efficiently fulfill placement requirements when demands or designs changed. Recently, the shift towards modular design in placement machines has made the configuration process less time-consuming compared to traditional methods. This study aims to address multi-level capacity planning, providing comprehensive and suitable machine combinations that swiftly adapt to the rapidly changing, unpredictable, and volatile market demands of today. The proposed matheuristic approach combines the mathematical programming model and the heuristic algorithm to effectively solve the large-scale machine combination problem. This research significantly improves PCB assembly lines by minimizing idle waste and bolstering the competitiveness of the electronic assembly industry.

Keywords: Surface mount technology; Printed circuit board; Ultra-high-mix Low-volume; Combinatorial optimization; Matheuristic (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-024-05828-6

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