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.

翻译：组合优化问题，如作业车间调度问题（JSP）和背包问题（KP），是运筹学、物流学和企业资源规划（ERP）中的基础性难题。这些问题通常需要复杂的算法才能在可行的时间约束内获得近似最优解。深度学习的最新进展引入了基于Transformer的架构，作为传统启发式和元启发式方法的有前景的替代方案。我们利用多类型Transformer（MTT）架构，在一个统一框架中处理这些基准问题。我们在JSP和KP的标准基准数据集上进行了广泛的实验评估，结果表明MTT在不同规模的基准问题上均取得了有竞争力的性能。我们展示了多类型注意力机制在铁钛合金行业实际应用中的潜力。据我们所知，我们是首个将多类型Transformer应用于实际制造业的研究。