发表机构
Virginia Tech（弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大规模两阶段随机混合整数规划，提出学习增强的Benders分解框架，利用预训练Transformer模型加速收敛，含可扩展生成机制，能求解到T = 270的实例，证明了Transformer在经典分解算法中的潜力。
AI 中文摘要
我们提出了一个学习增强的Benders分解框架来解决大规模两阶段随机混合整数规划。聚焦于需求不确定下的两阶段随机容量批量问题（TSSCLSP）。我们的方法通过使用预训练的Transformer模型为场景子问题快速生成高质量近似解来加速分解收敛。这种混合策略利用Transformer预测生成强最优性和可行性割，有效指导Benders主问题。我们的框架包含新颖的可扩展生成机制，能让固定时域训练的模型求解任意长度实例。对于所考虑的测试集，我们的方法能求解到T = 270的实例，此前该方法对此规模难以处理，且子问题解保持零不可行性。这证明了Transformer作为嵌入经典分解算法的强大替代求解器的潜力。
英文摘要
We propose a learning-augmented Benders decomposition framework to solve large-scale two-stage stochastic mixed-integer programs. We focus on the two-stage stochastic capacitated lot-sizing problem (TSSCLSP) under demand uncertainty. Our method accelerates the convergence of the decomposition by using a pre-trained TransfORmer model to rapidly generate high-quality approximate solutions for the scenario subproblems. This hybrid strategy uses the TransfORmer predictions to generate strong optimality and feasibility cuts, effectively guiding the Benders master problem. Our framework includes a novel expandable generation mechanism, allowing a model trained on a fixed horizon to solve instances of arbitrary length. For the test set considered, our method solves instances up to T = 270, a scale previously intractable for this approach, while maintaining zero infeasibility in the generated subproblem solutions. This demonstrates the potential of TransfORmers as powerful surrogate solvers embedded within classical decomposition algorithms.
