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PAPER Special Section on Discrete Mathematics and Its Applications Analysis of Lower Bounds for Online Bin Packing with Two Item Sizes

机译:两件尺寸的纸张专题分立数学及其在线箱包装下界的应用分析

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In the bin packing problem, we are asked to place given items, each being of size between zero and one, into bins of capacity one. The goal is to minimize the number of bins that contain at least one item. An online algorithm for the bin packing problem decides where to place each item one by one when it arrives. The asymptotic approximation ratio of the bin packing problem is defined as the performance of an optimal online algorithm for the problem. That value indicates the intrinsic hardness of the bin packing problem. In this paper we study the bin packing problem in which every item is of either size alpha or size beta (= alpha). While the asymptotic approximation ratio for alpha 1/2 was already identified, that for alpha = 1/2 is only partially known. This paper is the first to give a lower bound on the asymptotic approximation ratio for any alpha = 1/2, by formulating linear optimization problems. Furthermore, we derive another lower bound in a closed form by constructing dual feasible solutions.
机译:在垃圾包装问题中,我们被要求将给定的项目放置在零和一个之间的每个大小,进入容量的垃圾箱。目标是最小化包含至少一个项目的箱数。箱包装问题的在线算法决定在到达时逐个将每个项目放置在其中。箱包装问题的渐近近似比被定义为问题的最佳在线算法的性能。该值表明了箱包装问题的内在硬度。在本文中,我们研究了垃圾包装问题,其中每个项目都是尺寸α或尺寸的beta(& = alpha)。虽然α和gt的渐近近似率;已经识别了1/2,仅为alpha& = 1/2仅是部分已知的。本文通过制定线性优化问题,第一纸张是第一个在任何α= 1/2的渐近近似比上的下限。此外,我们通过构建双重可行解决方案来派生封闭形式的另一个下限。

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