用AI模型加速复杂调度问题求解，提升效率与稳定性。
Improvement of Optimization using Learning Based Models in Mixed Integer Linear Programming Tasks
- 用图神经网络结合行为克隆与强化学习，生成高质量初始解
- 相比传统方法，优化时间缩短且结果波动更小
- 适合需要快速求解的工业调度场景，如物流与制造
混合整数线性规划（MILP）是建筑、制造和物流等领域规划与调度的核心工具，但其广泛应用受限于计算耗时，尤其在大规模实时场景中。为此，我们提出一种基于学习的框架，利用行为克隆（BC）和强化学习（RL）训练图神经网络（GNN），为多智能体任务分配与调度问题生成高质量初始解，用于热启动MILP求解器。实验表明，该方法在保持解质量与可行性的同时，显著降低优化时间和解的方差。
原文摘要 · Abstract (English)
Mixed Integer Linear Programs (MILPs) are essential tools for solving planning and scheduling problems across critical industries such as construction, manufacturing, and logistics. However, their widespread adoption is limited by long computational times, especially in large-scale, real-time scenarios. To address this, we present a learning-based framework that leverages Behavior Cloning (BC) and Reinforcement Learning (RL) to train Graph Neural Networks (GNNs), producing high-quality initial solutions for warm-starting MILP solvers in Multi-Agent Task Allocation and Scheduling Problems. Experimental results demonstrate that our method reduces optimization time and variance compared to traditional techniques while maintaining solution quality and feasibility.
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