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Dynamic programming and mixed integer programming based algorithms for the online glass cutting problem with defects and production targets

机译:基于动态编程和混合整数编程的算法,用于具有缺陷和生产目标的在线玻璃切割问题

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In flat glass manufacturing, glass products of various dimensions are cut from a glass ribbon that runs continuously on a conveyor belt. Placement of glass products on the glass ribbon is restricted by the defects of varying severity located on the ribbon as well as the quality grades of the products to be cut. In addition to cutting products, a common practice is to remove defective parts of the glass ribbon as scrap glass. As the glass ribbon moves continuously, cutting decisions need to be made within seconds, which makes this online problem very challenging. A simplifying assumption is to limit scrap cuts to those made immediately behind a defect (a cut-behind-fault or CBF). We propose an online algorithm for the glass cutting problem that solves a series of static cutting problems over a rolling horizon. We solve the static problem using two methods: a dynamic programming algorithm (DP) that utilises the CBF assumption and a mixed integer programming (MIP) formulation with no CBF restriction. While both methods improve the process yield substantially, the results indicate that MIP significantly outperforms DP, which suggests that the computational benefit of the CBF assumption comes at a cost of inferior solution quality.
机译:在平板玻璃制造中,从连续在传送带上运行的玻璃带上切割出各种尺寸的玻璃产品。玻璃产品在玻璃带上的放置受到玻璃带上不同严重程度的缺陷以及要切割产品的质量等级的限制。除切割产品外,通常的做法是将玻璃带的有缺陷的部分作为废玻璃去除。随着玻璃带的不断移动,需要在几秒钟内做出切割决定,这使在线问题变得非常具有挑战性。一个简化的假设是将废料切割限制为在缺陷之后立即进行的裁切(故障后切割或CBF)。我们为玻璃切割问题提出了一种在线算法,该算法可以解决滚动状态下的一系列静态切割问题。我们使用两种方法解决静态问题:利用CBF假设的动态规划算法(DP)和无CBF限制的混合整数规划(MIP)公式。虽然这两种方法都可以显着提高过程产量,但结果表明MIP明显优于DP,这表明CBF假设的计算优势是以降低解决方案质量为代价的。

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