Distance-Based Neural Combinatorial Optimization for Context-based Route Planning

Platform-based large-scale journey planning of autonomous vehicles and context-sensitive route planning applications require new scalable approaches in order to work within an on-demand mobility service. In this work we present and test a machine learning-based approach for distance-based roundtrip planning in a Traveling Salesman Problem (TSP) setting. We introduce our applied Distance-Based Pointer Network (DBPN) algorithm which solves mini-batches of multiple symmetric and asymmetric 2D Euclidean TSPs. We provide our algorithm and test results for symmetric and asymmetric TSP distances, as present in real road and traffic networks. Subsequently, we compare our results with an industry standard routing solver OR-Tools. Here, we focus on solving comparably small TSP instances which commonly occur on our platform-based service. Our results show that compared to the State-of-the-Art methods such as the Coordinate-Based Pointer Network (CBPN) and OR-Tools, our approach solves asymmetric TSPs which cannot be solved by the CBPN approach. The results furthermore show that our approach achieves near-optimal results by a 5.9% mean absolute percentage error, compared to the OR-Tools solution. By solving 1000 TSPs, we show that our DBPN approach is approximately 27 times faster than the OR-Tools solver.