在政治分区模式中施加邻近限制

Oper. Res. Pub Date : 2021-12-28 DOI:10.1287/opre.2021.2141
Hamidreza Validi, Austin Buchanan, Eugene Lykhovyd
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引用次数: 34

摘要

近60年来,运筹学技术从整数规划模型开始,协助制定政治区划计划。这种以紧凑性为目标的模型倾向于生成连续或接近连续的区域,但不提供连续性的保证。在Hamidreza Validi、Austin Buchanan和Eugene Lykhovyd的论文《在政治区划模型中施加邻近约束》中,作者考虑并分析了四种不同的邻近模型(两种旧的和两种新的)。他们的计算机实现可以处理像印第安纳州(1,511个人口普查区)这样大的重新划分实例。他们最快的方法是使用分支切断算法,在回调中添加连续性约束。关键的是,许多变量可以通过拉格朗日参数先验地固定为零。所有的测试实例和源代码都是公开的。
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Imposing Contiguity Constraints in Political Districting Models
For nearly 60 years, operations research techniques have assisted in the creation of political districting plans, beginning with an integer programming model. This model, which seeks compactness as its objective, tends to generate districts that are contiguous, or nearly so, but provides no guarantee of contiguity. In the paper “Imposing contiguity constraints in political districting models” by Hamidreza Validi, Austin Buchanan, and Eugene Lykhovyd, the authors consider and analyze four different contiguity models (two old and two new). Their computer implementation can handle redistricting instances as large as Indiana (1,511 census tracts). Their fastest approach uses a branch-and-cut algorithm, where contiguity constraints are added in a callback. Critically, many variables can be fixed to zero a priori by Lagrangian arguments. All test instances and source code are publicly available.
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