EFFICIENT OVERLAP DETECTION AND CONSTRUCTION ALGORITHMS FOR THE BITMAP SHAPE PACKING PROBLEM

Yannan Hu, Sho Fukatsu, H. Hashimoto, S. Imahori, M. Yagiura
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引用次数: 10

Abstract

The two-dimensional strip packing problem arises in wide variety of industrial applications. In this paper, we focus on the bitmap shape packing problem in which a set of arbitrarily shaped objects represented in bitmap format should be packed into a larger rectangular container without overlap. The complex geometry of bitmap shapes and the large amount of data to be processed make it difficult to check for overlaps. In this paper, we propose an efficient method for checking for overlaps and design efficient implementations of two construction algorithms, which are based on the bottom-left strategy. In this strategy, starting from an empty layout, items are packed into the container one by one. Each item is placed in the lowest position where there is no overlap relative to the current layout. We consider two algorithms, the bottom-left and the best-fit algorithm, which adopt this strategy. The computational results for a series of instances that are generated from well-known benchmark instances show that the proposed algorithms obtain good solutions in remarkably short time and are especially effective for large-scale instances.
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位图形状填充问题的有效重叠检测和构造算法
二维条形填料问题出现在各种各样的工业应用中。在本文中,我们关注位图形状打包问题,其中以位图格式表示的一组任意形状的对象应该被打包到一个更大的矩形容器中,而不重叠。位图形状的复杂几何结构和需要处理的大量数据使检查重叠变得困难。在本文中,我们提出了一种检查重叠的有效方法,并设计了两种基于左下角策略的构造算法的有效实现。在这种策略中,从一个空的布局开始,项目被一个接一个地打包到容器中。每个项目都放置在相对于当前布局没有重叠的最低位置。我们考虑了采用这种策略的两种算法,左下角算法和最佳拟合算法。由著名的基准实例生成的一系列实例的计算结果表明,所提出的算法在极短的时间内获得了良好的解,并且对大规模实例特别有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of the Operations Research Society of Japan
Journal of the Operations Research Society of Japan 管理科学-运筹学与管理科学
CiteScore
0.70
自引率
0.00%
发文量
12
审稿时长
12 months
期刊介绍: The journal publishes original work and quality reviews in the field of operations research and management science to OR practitioners and researchers in two substantive categories: operations research methods; applications and practices of operations research in industry, public sector, and all areas of science and engineering.
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