基于奖牌学习算法的团队成员分配问题
The Team Member Allocation Problem Based on the Medalist Learning Algorithm
摘要: 高效且和谐的团队是组织成功的关键因素之一。团队的目标不仅在于完成任务，更在于促进成员能力提升和团队可持续发展。如何组建一个个人利益和群体利益均衡的团队是一项难题，因为这涉及到公平性问题。本文将团队组建视为一个多目标离散问题，并提出了一个综合考虑任务完成度、个人提升和群体公平性的优化模型。该模型旨在确保团队在尽可能保证达到任务目标的前提下，实现团队成员个人能力提升和同级成员间公平发展的均衡。通过对六个数据集进行优化求解，研究得出了最佳团队分配方案。对于一个以任务为导向的团队而言，团队规模在3到6人之间是最具效益的。这种规模的团队构成灵活多样，能够在完成任务的同时提供更多提升可能性。
Abstract: Efficient and harmonious teams are crucial for organizational success. The goals of a team extend beyond task completion to include the enhancement of members’ abilities and the sustainable development of the team. Forming a team that balances individual and collective interests is a challenge due to fairness considerations. This paper views team formation as a multi-objective discrete problem and proposes an optimization model that comprehensively considers task completion, personal improvement, and group fairness. The model aims to ensure that, while meeting task objectives, there is a balance between the personal development of team members and fairness among peers. Through optimization across six datasets, the study derives optimal team allocation strategies. For task-oriented teams, a team size of 3 to 6 members is found to be the most beneficial. This team size offers flexibility and variety, enabling task completion while providing ample opportunities for individual growth.
文章引用：何旺, 何胜学. 基于奖牌学习算法的团队成员分配问题[J]. 建模与仿真, 2025, 14(2): 593-607. https://doi.org/10.12677/mos.2025.142179

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