Speed-up of Self-Organizing Networks for Routing Problems in a Polygonal Domain
This addresses routing optimization for logistics or robotics in obstacle-filled environments, but it appears incremental as it builds on existing neural network methods.
The authors tackled the computational speed of solving the Travelling Salesman Problem in polygonal domains with obstacles, using multidimensional scaling to reduce the runtime of a self-organizing neural network, though no specific numerical results are provided.
Routing problems are optimization problems that consider a set of goals in a graph to be visited by a vehicle (or a fleet of them) in an optimal way, while numerous constraints have to be satisfied. We present a solution based on multidimensional scaling which significantly reduces computational time of a self-organizing neural network solving a typical routing problem -- the Travelling Salesman Problem (TSP) in a polygonal domain, i.e. in a space where obstacles are represented by polygons. The preliminary results show feasibility of the proposed approach and although the results are presented only for TSP, the method is general so it can be used also for other variants of routing problems.