[论文解读] Scalable Knee-Point Guided Activity Group Selection in Multi-Tree Genetic Programming for Dynamic Multi-Mode Project Scheduling

Yuan Tian, Yi Mei+1 more|arXiv (Cornell University)|Jan 20, 2026

Resource-Constrained Project Scheduling被引用 0

一句话总结

在多树遗传规划框架中基于拐点的分组选择机制，用于在动态多模式资源受限的项目调度中放大活跃组决策。

ABSTRACT

The dynamic multi-mode resource-constrained project scheduling problem is a challenging scheduling problem that requires making decisions on both the execution order of activities and their corresponding execution modes. Genetic programming has been widely applied as a hyper-heuristic to evolve priority rules that guide the selection of activity-mode pairs from the current eligible set. Recently, an activity group selection strategy has been proposed to select a subset of activities rather than a single activity at each decision point, allowing for more effective scheduling by considering the interdependence between activities. Although effective in small-scale instances, this strategy suffers from scalability issues when applied to larger problems. In this work, we enhance the scalability of the group selection strategy by introducing a knee-point-based selection mechanism to identify a promising subset of activities before evaluating their combinations. An activity ordering rule is first used to rank all eligible activity-mode pairs, followed by a knee point selection to find the promising pairs. Then, a group selection rule selects the best activity combination. We develop a multi-tree GP framework to evolve both types of rules simultaneously. Experimental results demonstrate that our approach scales well to large instances and outperforms GP with sequential decision-making in most scenarios.

研究动机与目标

• 设计基于拐点的分组选择以限制候选分组，同时保留决策质量。
• 开发一个多树GP框架，以进化排序规则和分组优先级规则。
• 在与顺序决策GP方法对比中评估可扩展性与性能。

提出的方法

• 用排序规则对合格的活动-模式对进行排序。
• 应用拐点选择在对分组枚举前识别有前景的对。
• 在资源约束下，从拐点筛选集枚举并剪枝活动分组。
• 使用Koza风格的多树GP框架同时进化排序和分组优先级规则。
• 通过求解训练实例并计算完成时间下界偏差来评估调度。

实验结果

研究问题

• RQ1 拐点基过滤是否能降低DMRCPSP的活动-分组选择的组合爆炸？
• RQ2 多树GP进化的排序和分组优先级规则是否优于顺序决策GP？
• RQ3 拐点引导的分组选择在不同前置关系和资源复杂度下如何影响可扩展性与解质量？

主要发现

• KGGP方法在大多数测试情景中优于顺序GP。
• 拐点选择在候选活动-模式对上显著减少数量，使更大规模的问题成为可能。
• 多树GP能够有效进化排序和分组优先级规则。
• 规则分析显示排序规则偏好紧急、资源高效的对，分组规则偏好快速且下游影响强的分组。
• 在前置关系更复杂时训练时间增加，但相对于完全枚举方法，KGGP仍具可扩展性。