Abstract
To solve the problem of vehicle routing problem under capacity limitation, this paper puts forward a novel method of logistics distribution route optimization based on genetic algorithm and ant colony optimization algorithm (GA-ACO). On the first stage, improved genetic algorithm with a good global optimization searching ability is used to find the feasible routes quickly. On the second stage, the result of the genetic algorithm is used as the initial solution of the ant colony algorithm to initialize the pheromone. And then improved ant colony optimization algorithm is used to find the optimal solution of logistics distribution route. Experimental results show that the optimal or nearly optimal solutions of the logistic distribution routing can be quickly obtained by this two stages method.
Highlights
For modern logistics enterprises, how to generate vehicle schedules in transportation reasonably, to optimize the transportation line, and to reduce logistics cost has become a core problem of logistics management
Some scholars put forward particle swarm optimization combined with genetic algorithm and simulated annealing algorithm combined with genetic algorithm to solve logistics distribution route optimization problem based on the theory of combination optimization[1,2]
Genetic algorithm has the advantages of powerful global search ability and high rates of convergence, but it can't use feedback information which leads to poor search ability, premature convergence and fall into local optimum [3].The characteristics of ant colony algorithm is heuristic search and positive feedback mechanism, so it has the advantages of obtaining optimal solution with high efficiency, good local searching ability, distributed computing ability and strong robustness
Summary
How to generate vehicle schedules in transportation reasonably, to optimize the transportation line, and to reduce logistics cost has become a core problem of logistics management. Due to the fact that the logistics distribution vehicle routing optimization problem is a Non-deterministic Polynomial Complete problem, using only one method to obtain the global optimal solution is difficult. Genetic algorithm has the advantages of powerful global search ability and high rates of convergence, but it can't use feedback information which leads to poor search ability, premature convergence and fall into local optimum [3].The characteristics of ant colony algorithm is heuristic search and positive feedback mechanism, so it has the advantages of obtaining optimal solution with high efficiency, good local searching ability, distributed computing ability and strong robustness. Integrating the advantages of genetic and ant colony algorithm, this paper proposes a two stage method
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