  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.
