GPU-Based approach to Large scale Dynamic Vehicle Routing Problem.

Abstract

Vehicle routing problems (VRPs) are fundamental optimisation problems of transportation systems. In the real-world, VRPs are dynamic in the sense that new customers' requests continuously arrive over time, after a number of vehicles have already started their tours. Dynamic VRPs (DVRPs) require making decisions as fast as possible. This needs resolution methods with high computational efficiency especially for problems with a large number of customers. The aim of this paper is to attempt to achieve this objective. For this, we design a genetic algorithm for the DVRP and we implement it on GPU. The proposed approach inserts new requests into already planned routes then it optimises the resulting solution via genetic operators. To our knowledge, this is the first attempt to solve large DVRP on the GPU using evolutionary algorithm and seems to be efficient according to the experimental results on some published benchmarks and on our large instances (up to 10,000 nodes).