Evolutionary-based hybrid algorithm for 2D cutting stock problem

M. Fathy, M. Osama, M. El-Mahallawy
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Abstract

Cutting stock problem (CSP) affects cost of production and stock use efficiency in many industries. The majority of such industries handle stock of raw material in sheet form with the priority of waste reduction. Thus, in this paper we study the two-dimensional CSP (2-D CSP) with main goal of minimizing trim loss. Current approaches are primarily designed to deal with regular stock sheets only and do not handle irregular or defective sheets. That is why the problem is considered to be partially solved from an industrial stand point. In this paper, we introduce a novel algorithm for 2D CSP to minimize the waste and address the issue of defective and/or irregular stock sheets. The algorithm utilizes image processing, evolutionary-programming (EP), and Linear programming (LP) to form a practical solution. Detection & Isolation of sheets' defects and conversion of irregular sheets to regular is accomplished by image processing. Further processing is done by the remaining techniques to efficiently minimize the waste. Experimental results show that the proposed algorithm succeeds in achieving lower waste values compared to conventional EP algorithms.
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二维切料问题的进化混合算法
切削库存问题影响着许多行业的生产成本和库存利用效率。大多数这类工业处理以单张形式储存的原材料,优先考虑减少浪费。因此,在本文中,我们研究二维CSP (2-D CSP)的主要目标是最小化修剪损失。目前的方法主要是设计来处理正常的库存单,而不处理不规则或有缺陷的单。这就是为什么从工业的角度来看,这个问题被认为部分解决了。在本文中,我们介绍了一种新的二维CSP算法,以最大限度地减少浪费,并解决不良和/或不规则库存单的问题。该算法利用图像处理、进化规划(EP)和线性规划(LP)来形成一个实用的解决方案。通过图像处理实现板材缺陷的检测与隔离,将不规则板材转化为规则板材。进一步的处理由剩余的技术来完成,以有效地减少浪费。实验结果表明,与传统的EP算法相比,该算法可以获得更低的浪费值。
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