Multiple depots vehicle routing based on the ant colony with the genetic algorithm
Abstract: the distribution
routing plans of multi-depots vehicle scheduling problem will increase
exponentially along with the adding of customers. So, it becomes an important
studying trend to solve the vehicle scheduling problem with heuristic
algorithm. On the basis of building the model of multi-depots vehicle
scheduling problem, in order to improve the efficiency of the multiple depots
vehicle routing, the paper puts forward a fusion algorithm on multiple depots
vehicle routing based on the ant colony algorithm with genetic algorithm.
Design/methodology/approach: to achieve this objective, the genetic
algorithm optimizes the parameters of the ant colony algorithm. The fusion
algorithm on multiple depots vehicle based on the ant colony algorithm with
genetic algorithm is proposed.
Findings: simulation experiment indicates that the result of the fusion
algorithm is more excellent than the other algorithm, and the improved
algorithm has better convergence effective and global ability.
Research limitations/implications: in this research, there are some
assumption that might affect the accuracy of the model such as the pheromone
volatile factor, heuristic factor in each period, and the selected multiple
depots. These assumptions can be relaxed in future work.
Originality/value: In this research, a new method for the multiple depots
vehicle routing is proposed. The fusion algorithm eliminate the influence of
the selected parameter by optimizing the heuristic factor, evaporation factor,
initial pheromone distribute, and have the strong global searching ability. The
Ant Colony algorithm imports cross operator and mutation operator for operating
the first best solution and the second best solution in every iteration, and
reserves the best solution. The cross and mutation operator extend the solution
space and improve the convergence effective and the global ability. This
research shows that considering both the ant colony and genetic algorithm
together can improve the efficiency multiple depots vehicle routing.
Keywords: genetic algorithm,
ant colony algorithm, multiple depots, vehicle routing, fusion algorithm
Author: ChunYing Liu, Jijiang
Yu
Journal Code: jptindustrigg130082
