用多类型Transformer优化冶金制造中的资源调度问题。
Enterprise Resource Planning Using Multi-type Transformers in Ferro-Titanium Industry
- 设计多类型Transformer统一建模作业车间与背包问题
- 在标准数据集上达到接近最优解，且适配不同规模任务
- 首次在真实钛铁行业实现多类型注意力机制落地
组合优化问题如作业车间调度（JSP）和背包问题（KP）是运筹学、物流及企业资源规划（ERP）中的核心挑战，常需复杂算法在合理时间内逼近最优解。深度学习新进展使基于Transformer的架构成为传统启发式与元启发式方法的有力替代。本文采用多类型Transformer（MTT）架构，在统一框架下解决上述基准问题。我们在JSP与KP的标准数据集上开展全面实验，证明MTT在不同规模问题上均具竞争力。此外，我们展示了多类型注意力机制在真实钛铁行业应用中的潜力。据我们所知，这是首个将多类型Transformer应用于实际制造业的案例。
原文摘要 · Abstract (English)
Combinatorial optimization problems such as the Job-Shop Scheduling Problem (JSP) and Knapsack Problem (KP) are fundamental challenges in operations research, logistics, and eterprise resource planning (ERP). These problems often require sophisticated algorithms to achieve near-optimal solutions within practical time constraints. Recent advances in deep learning have introduced transformer-based architectures as promising alternatives to traditional heuristics and metaheuristics. We leverage the Multi-Type Transformer (MTT) architecture to address these benchmarks in a unified framework. We present an extensive experimental evaluation across standard benchmark datasets for JSP and KP, demonstrating that MTT achieves competitive performance on different size of these benchmark problems. We showcase the potential of multi-type attention on a real application in Ferro-Titanium industry. To the best of our knowledge, we are the first to apply multi-type transformers in real manufacturing.
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