A heuristic with tie breaking for certain 0–1 integer programming models

G. E. Fox, Gary D. Scudder
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引用次数: 24

Abstract

A heuristic for 0–1 integer programming is proposed that features a specific rule for breaking ties that occur when attempting to determine a variable to set to 1 during a given iteration. It is tested on a large number of small- to moderate-sized randomly generated generalized set-packing models. Solutions are compared to those obtained using an existing well-regarded heuristic and to solutions to the linear programming relaxations. Results indicate that the proposed heuristic outperforms the existing heuristic except for models in which the number of constraints is large relative to the number of variables. In this case, it performs on par with the existing heuristic. Results also indicate that use of a specific rule for tie breaking can be very effective, especially for low-density models in which the number of variables is large relative to the number of constraints.
一类0-1整数规划模型的启发式解法
提出了一种0-1整数规划的启发式方法,该方法具有一个特定的规则,用于打破在给定迭代期间试图确定要设置为1的变量时发生的联系。在大量中小规模随机生成的广义集集集集模型上进行了检验。将解与现有的公认的启发式解和线性规划松弛解进行了比较。结果表明,除了约束数量相对于变量数量较大的模型外,所提出的启发式算法优于现有的启发式算法。在这种情况下,它的性能与现有的启发式相当。结果还表明,使用特定规则来打破束缚可能非常有效,特别是对于变量数量相对于约束数量较大的低密度模型。
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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