论文标题
Deepco：使用深度学习的离线组合优化框架
DeepCO: Offline Combinatorial Optimization Framework Utilizing Deep Learning
论文作者
论文摘要
组合优化是许多现代工业应用中的重要组成部分。由于安全性和/或成本问题，许多问题是离线设置。尽管基于模拟的方法对于复杂的系统似乎很难实现，但在这项研究中，我们提出了DeepCo，这是一个利用深度学习的离线组合优化框架。我们还设计了旅行推销员问题（TSP）的离线变化，以建模仓库操作序列优化问题以进行评估。只有有限的历史数据，新颖的提议分布正则优化方法优于离线TSP实验中现有的基线方法，将路线长度降低了5.7％，并且在现实世界中的问题中显示出很大的潜力。
Combinatorial optimization serves as an essential part in many modern industrial applications. A great number of the problems are offline setting due to safety and/or cost issues. While simulation-based approaches appear difficult to realise for complicated systems, in this research, we propose DeepCO, an offline combinatorial optimization framework utilizing deep learning. We also design an offline variation of Travelling Salesman Problem (TSP) to model warehouse operation sequence optimization problem for evaluation. With only limited historical data, novel proposed distribution regularized optimization method outperforms existing baseline method in offline TSP experiment reducing route length by 5.7% averagely and shows great potential in real world problems.
