Improving a Genetic Algorithm for Route Planning Using Parallelism with Speculative Execution

A typical United Parcel Service (UPS) route consists of approximately 100 stops. The distance traveled to visit those 100 stops is determined by the order in which they are visited and the paths traveled between consecutive stops. This is an example of a problem we call route planning. The reward for finding short routes is substantial. UPS estimates that reducing each of their 55,000 North American routes by only 1 mile per day results in annual savings of up to $50,000,000, in fuel costs alone [18]. We approach the route planning problem with a novel, parallel genetic algorithm. We demonstrate the utility of parallelism for route planning by showing decreasing run times and solving much more complex problem instances that a non-parallel implementation of the same algorithm. By introducing speculative execution to the algorithm, we further, significantly improve the results.