A route-directed hybrid genetic approach for the vehicle routing problem with time windows

A route-directed hybrid genetic approach to address the Vehicle Routing Problem with Time Windows is presented. The proposed scheme relies on the concept of simultaneous evolution of two populations pursuing different objectives subject to partial constraint relaxation. The first population evolves individuals to minimize total traveled distance while the second focuses on minimizing temporal constraint violation to generate a feasible solution, both subject to a fixed number of tours. Genetic operators have been designed to incorporate key concepts emerging from recent promising techniques such as insertion heuristics and large neighborhood search to further explore the solution space. Results from a computational experiment over common benchmark problems show that the proposed technique matches or outperforms some of the best heuristic routing procedures, providing six new best-known solutions. In comparison, the method proved to be fast, cost-effective and highly competitive.