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Accelerating mathematical programming techniques with the corridor method

机译:用走廊方法加速数学编程技术

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In this paper we investigate how the Benders decomposition, Lagrangean relaxation, and Dantzig-Wolfe reformulation techniques can be accelerated when intertwined with the corridor method. We test the approaches on the capacitated lot sizing problem with setups. Due to the computational complexity of this lot sizing problem, one would expect to find a number of approaches based on decomposition techniques in the literature. While this is true for Lagrangean relaxation and Dantzig-Wolfe reformulation, we could not find any paper proposing the use of Benders decomposition for the problem at hand. Consequently, with this study, we pursue a two-fold goal: First, and foremost, we want to determine how effective the corridor method is as acceleration scheme for these decomposition techniques; second, we aim at gaining some insight into why Benders has not been proposed for this class of problems. Our results shed light on both issues. On the one hand, we show that all the decomposition methods benefit from the hybridisation with the corridor method. On the other hand, a thorough analysis on the behaviour and limitations of Benders algorithm is provided. We conclude the study with a statistical analysis to determine whether significant differences in performance among the different implementations arise.
机译:在本文中,我们调查如何在与走廊方法交织在线时加速弯管分解,拉格朗扬的弛豫和Dantzig-Wolfe重构技术。我们使用设置测试电容批量尺寸问题的方法。由于这种批量尺寸的计算复杂性,人们希望基于文献中的分解技术找到许多方法。虽然这是针对拉格朗各加的放松和唐兹格 - 沃尔夫的重构,但我们找不到任何文件提出使用弯道分解在手中的问题。因此,通过这项研究,我们追求了两倍的目标:首先,我们希望确定走廊方法如何有效地是这些分解技术的加速度方案;其次,我们旨在获得一些深入了解为什么弯曲者尚未为这类问题提出。我们的结果阐明了这两个问题。一方面,我们表明所有分解方法都受益于与走廊方法的杂交。另一方面,提供了对弯曲算法的行为和限制的彻底分析。我们在统计分析中得出研究,确定不同实现中的性能的显着差异。

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