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Sustainability 2018, 10(11), 4123; https://doi.org/10.3390/su10114123

Learning to Dispatch Operations with Intentional Delay for Re-Entrant Multiple-Chip Product Assembly Lines

1
Department of Industrial Engineering and Institute for Industrial Systems Innovation, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-744, Korea
2
Department of Industrial and Management Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 407-772, Korea
*
Author to whom correspondence should be addressed.
Received: 20 September 2018 / Revised: 27 October 2018 / Accepted: 5 November 2018 / Published: 9 November 2018
(This article belongs to the Special Issue Sustainable Materials and Manufacturing)
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Abstract

As the demand for small devices with embedded flash memory increases, semiconductor manufacturers have been recently focusing on producing high-capacity multiple-chip products (MCPs). Due to the frequently re-entrant lots between the die attach (DA) and wire bonding (WB) assembly stages in MCP production, increased flow time and decreased resource utilization are unavoidable. In this paper, we propose a dispatcher based on artificial neural networks, which minimizes the flow time while maintaining high utilization of resources at the same time through exploiting the possible intentional delays on DA stage. Specifically, the proposed dispatcher learns the assignment preferences between available lots and DA resources based on assembly line data generated by using a simulator, then the proposed dispatcher performs lot dispatching decisions by considering the intentional delays. The numerical experiments were performed under various configurations of the MCP assembly lines, and the results show that the proposed dispatcher outperformed the existing methods. View Full-Text
Keywords: lot dispatching; semiconductor; assembly line; multiple-chip product; intentional delay; artificial neural network; sustainability lot dispatching; semiconductor; assembly line; multiple-chip product; intentional delay; artificial neural network; sustainability
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Huh, J.; Park, I.; Lim, S.; Paeng, B.; Park, J.; Kim, K. Learning to Dispatch Operations with Intentional Delay for Re-Entrant Multiple-Chip Product Assembly Lines. Sustainability 2018, 10, 4123.

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