An improved genetic algorithm for vehicle routing problem with time windows

The vehicle routing problem (VRP) can be stated as the problem of designing least cost routes from one depot to a set of geographically scattered customers. The vehicle routing problem with time windows (VRPTW) is a generalization of the standard vehicle routing problem involving the added constraint that each customer specifies a time window in which the delivery must occur. VRPTW is encountered very frequently by the logistic team of a private company. A genetic algorithm incorporating with the elitism strategy (GAE) is proposed in this paper. For the elitism strategy we compare the best members among reproduction, crossover and mutation and select the best individuals. The best members of each generation are copied into the succeeding generation. The results of VRPTW are discussed in comparison with earlier findings. On well-known VRPTW benchmark problems, we obtain very competitive results with those reported by other researchers who also use genetic algorithms. Significance: Delivering material to different plants (locations) on time is an important factor for a logistic system. In this paper, a genetic algorithm based heuristic is developed to solve the vehicle routing problem with time windows. The heuristic has been demonstrated to be an effective approach and competitive with other GA approaches. This section should talk about the significance of your work.