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An efficient genetic algorithm with a corner space algorithm for a cutting stock problem in the TFT-LCD industry

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  • Lu, Hao-Chun
  • Huang, Yao-Huei
Abstract
In this study, we investigate a two-dimensional cutting stock problem in the thin film transistor liquid crystal display industry. Given the lack of an efficient and effective mixed production method that can produce various sizes of liquid crystal display panels from a glass substrate sheet, thin film transistor liquid crystal display manufacturers have relied on the batch production method, which only produces one size of liquid crystal display panel from a single substrate. However, batch production is not an effective or flexible strategy because it increases production costs by using an excessive number of glass substrate sheets and causes wastage costs from unused liquid crystal display panels. A number of mixed production approaches or algorithms have been proposed. However, these approaches cannot solve industrial-scale two-dimensional cutting stock problem efficiently because of its computational complexity. We propose an efficient and effective genetic algorithm that incorporates a novel placement procedure, called a corner space algorithm, and a mixed integer programming model to resolve the problem. The key objectives are to reduce the total production costs and to satisfy the requirements of customers. Our computational results show that, in terms of solution quality and computation time, the proposed method significantly outperforms the existing approaches.

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  • Lu, Hao-Chun & Huang, Yao-Huei, 2015. "An efficient genetic algorithm with a corner space algorithm for a cutting stock problem in the TFT-LCD industry," European Journal of Operational Research, Elsevier, vol. 246(1), pages 51-65.
    • Handle: RePEc:eee:ejores:v:246:y:2015:i:1:p:51-65
    DOI: 10.1016/j.ejor.2015.04.044
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    Cited by:

    1. Zhang, Haowei & Xie, Junwei & Ge, Jiaang & Zhang, Zhaojian & Zong, Binfeng, 2019. "A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar," European Journal of Operational Research, Elsevier, vol. 272(3), pages 868-878.
    2. Jie Fang & Yunqing Rao & Qiang Luo & Jiatai Xu, 2023. "Solving One-Dimensional Cutting Stock Problems with the Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(4), pages 1-16, February.

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