Maximum Leaf Spanning Tree Problem Benchmarks

This database contains experimental problems designed to study the Maximum Leaf Spanning Tree Problem (MLSTP). More specifically, the dataset can be used to evaluate the performance of algorithms developed to solve MLSTP. We generated a set of large-scale instances. We also collected existing benchmarks {Lucena, A., Maculan, N. & Simonetti, L. Reformulations and solution algorithms for the maximum leaf spanning tree problem. Comput Manag Sci 7, 289–311 (2010). https://doi.org/10.1007/s10287-009-0116-5, GENDRON, B., LUCENA, A., DA CUNHA, A. S. & SIMONETTI, L. (2014), "Benders Decomposition, Branch-and-Cut, and Hybrid Algorithms for the Minimum Connected Dominating Set Problem", INFORMS Journal on Computing, 26, 645-657, doi: https://doi.org/10.1287/ijoc.2013.0589.} which were included in the dataset. These instances are used in a study entitled “A New Formulation and Algorithm for Maximum Leaf Spanning Tree Problem with an Application in the Forest Fire Detection” which will be appeared in ----. DOI reference: http://dx.doi.org/10.17632/w98s4tvfn8.1本数据库收录了为研究最大叶生成树问题（Maximum Leaf Spanning Tree Problem, MLSTP）而设计的实验测试用例集。具体而言，该数据集可用于评估针对MLSTP开发的求解算法的性能。本团队生成了一批大规模测试实例，同时收集了已公开的基准测试集，相关基准测试集包含以下两篇学术文献的测试用例：1. Lucena, A., Maculan, N. & Simonetti, L.，论文《最大叶生成树问题的重建模与求解算法》（Reformulations and solution algorithms for the maximum leaf spanning tree problem）发表于《计算管理科学（Computational Management Science）》2010年第7卷，第289–311页，DOI链接：https://doi.org/10.1007/s10287-009-0116-5；2. Gendron, B., Lucena, A., da Cunha, A. S. & Simonetti, L. (2014)，论文《Benders分解（Benders Decomposition）、分支切割法（Branch-and-Cut）及混合算法求解最小连通支配集问题》（Benders Decomposition, Branch-and-Cut, and Hybrid Algorithms for the Minimum Connected Dominating Set Problem）发表于《INFORMS计算期刊（INFORMS Journal on Computing）》2014年第26卷，第645-657页，DOI链接：https://doi.org/10.1287/ijoc.2013.0589。上述基准测试集均已纳入本数据集。 本数据集的测试实例将用于一篇题为《最大叶生成树问题的新模型与算法及其在森林火灾检测中的应用》（A New Formulation and Algorithm for Maximum Leaf Spanning Tree Problem with an Application in the Forest Fire Detection）的研究论文，该论文即将发表于[待公开期刊名称]。 本数据集的DOI引用链接：http://dx.doi.org/10.17632/w98s4tvfn8.1