A hybrid genetic algorithm for the vehicle routing problem with time windows and itinerary constraints

A variant of a new hybrid genetic algorithm to address the vehicle routing problem with time windows (VRPTW) and itinerary constraints is presented. It exploits implicit knowledge provided by the structure of intermediate solutions computed during the evolutionary process to explore the solution space. Accordingly, domain knowledge and, prior knowledge inferred from typical near-optimal solutions are both captured to enrich the recombination and mutation phases of the algorithm. The procedure is intended to be conceptually simple, easy to implement and allow fast computation of near-optimal solutions. A computational experiment has been conducted to compare the performance of the proposed algorithm with extended heuristics derived from a standard technique. Statistical analysis shows that the new hybrid genetic algorithm outperforms the heuristics most of the time.