有指导的团队划分问题：定义、复杂性和算法
The Guided Team-Partitioning Problem: Definition, Complexity, and Algorithm
摘要：
一长串的文献都集中在从大量的候选人中选择一个团队的问题，这样就可以尊重某些约束条件，并使给定的目标函数最大化。尽管现有的研究已经成功地考虑了不同的家庭的目标函数和约束，最常见的局限性之一是集中在单团队的范式。尽管它在多个领域中有着良好的应用，但当团队构建者需要将整个群体划分为多个团队时，这种范式并不合适。团队划分任务在教育环境中非常常见，在这种情况下，教师必须将班级中的学生分成团队进行协作项目。这个任务也出现在组织的背景下，当管理者需要将劳动力划分为具有特定属性的团队来处理相关项目时。在这项工作中，我们通过引入引导团队划分（GTP）问题，它要求将人口划分为团队，使每个团队的质心尽可能接近给定的目标向量，从而扩展了团队形成文献。正如我们在工作中详细描述的那样，这个公式允许团队建设者控制生产团队的组成，并在实际环境中有自然的应用。GTP的算法需要同时考虑竞争相同候选人群体的多个非重叠团队的组成。这使得这个问题比专注于优化单个团队的公式更具挑战性。事实上，我们证明了GTP是NP-难解决的，甚至近似。问题的复杂性促使我们考虑有效的算法算法，我们通过在真实和合成数据集上的实验进行评估。
英文摘要：
A long line of literature has focused on the problem of selecting a team of individuals from a large pool of candidates, such that certain constraints are respected, and a given objective function is maximized. Even though extant research has successfully considered diverse families of objective functions and constraints, one of the most common limitations is the focus on the single-team paradigm. Despite its well-documented applications in multiple domains, this paradigm is not appropriate when the team-builder needs to partition the entire population into multiple teams. Team-partitioning tasks are very common in an educational setting, in which the teacher has to partition the students in her class into teams for collaborative projects. The task also emerges in the context of organizations, when managers need to partition the workforce into teams with specific properties to tackle relevant projects. In this work, we extend the team formation literature by introducing the Guided Team-Partitioning (GTP) problem, which asks for the partitioning of a population into teams such that the centroid of each team is as close as possible to a given target vector. As we describe in detail in our work, this formulation allows the team-builder to control the composition of the produced teams and has natural applications in practical settings. Algorithms for the GTP need to simultaneously consider the composition of multiple non-overlapping teams that compete for the same population of candidates. This makes the problem considerably more challenging than formulations that focus on the optimization of a single team. In fact, we prove that GTP is NP-hard to solve and even to approximate. The complexity of the problem motivates us to consider efficient algorithmic heuristics, which we evaluate via experiments on both real and synthetic datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
