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Hybrid Methods for the Multileaf Collimator Sequencing Problem

机译:多叶准直仪排序问题的混合方法

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The multileaf collimator sequencing problem is an important component of the effective delivery of intensity modulated radiotherapy used in the treatment of cancer. The problem can be formulated as finding a decomposition of an integer matrix into a weighted sequence of binary matrices whose rows satisfy a consecutive ones property. In this paper we extend the state-of-the-art optimisation methods for this problem, which are based on constraint programming and decomposition. Specifically, we propose two alternative hybrid methods: one based on Lagrangian relaxation and the other on column generation. Empirical evaluation on both random and clinical problem instances shows that these approaches can out-perform the state-of-the-art by an order of magnitude in terms of time. Larger problem instances than those within the capability of other approaches can also be solved with the methods proposed.
机译:多叶准直仪排序问题是有效递送用于癌症治疗的调强放射疗法的重要组成部分。可以将问题表达为找到将整数矩阵分解为加权矩阵的二进制矩阵,该矩阵的行满足连续的1的性质。在本文中,我们基于约束编程和分解扩展了针对此问题的最新优化方法。具体来说,我们提出了两种替代的混合方法:一种基于拉格朗日弛豫,另一种基于色谱柱生成。对随机和临床问题实例的经验评估表明,这些方法在时间上可以比最新技术好一个数量级。所提出的方法也可以解决比其他方法所能解决的问题大的问题。

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