Research On Improved Genetic Algorithm For Multi-objective Vehicle Routing Problem With Time Windows

With the rapid development of e-commerce, logistics and transport industry has become a hot industry at present. How to quickly, effectively, safely and easily deliver goods has attracted plenty of enterprises’ attention. Vehicle routing problem is a very important issue in logistics and transport industry, which attracted many researchers in the field of operations research and combination optimization.This paper researches on multi-objective vehicle routing problem with time windows. We set up a mathematical model for the multi-objective vehicle routing problem with time windows(VRPTW). We propose an improved genetic algorithm for VRPTW and simultaneously optimizes two objectives, namely as the number of vehicles and the driving distance. At first,we produce initial solutions by the random and the push forward insertion heuristic methods.Next, we use the methods of most excellent individual choice and roulette to the operation of choice. Then, we make individual performance become better through the improved crossover operator. Improved mutation operator promotes the diversity of the population. We find a better solution by local search operator in the neighborhood. At last, according to improved non-dominated sorting genetic algorithm, we combine the new population and the last generation population to obtain the Pareto optimal solutions by non dominated sorting.Finally, the improved genetic algorithm is programmed by VC ++ 6.0. We conducted the experiments on these test sets of Solomon. The experimental results show that the improved algorithm is an effective method for multi-objective VRPTW.