Abstract
Aiming at solving the vehicle routing problem, an improved genetic algorithm based on fuzzy C-means clustering (FCM) is proposed to solve the vehicle routing problem with capacity constraints. On the basis of genetic algorithm, the FCM algorithm is used to decompose the large-scale vehicle routing optimization problem into small-scale subproblems, which can effectively improve the efficiency of the algorithm. At the same time, a generation method of the initial solution to CVRP problem is designed. The improved algorithm has good robustness and can also reduce the possibility of falling into local optimization in the search process. Finally, a simulation example is provided to verify the efficiency and superiority of the proposed algorithm.
Highlights
Capacitated vehicle routing problem (CVRP) [1], which is well known and has high research value in the logistics field, was proposed first time by Dantzig in 1956
Inspired by the above two methods, in this paper, an improved genetic algorithm based on fuzzy C-means algorithm (FCM) is used to solve CVRP problems
The experiment by using benchmark examples is introduced, and the experiment demonstrates that this algorithm can solve CVRP problems with better precision and effectiveness
Summary
Capacitated vehicle routing problem (CVRP) [1], which is well known and has high research value in the logistics field, was proposed first time by Dantzig in 1956. In [7], the authors analyzed and compared the performance of multiple heuristic algorithms under the CVRP conditions, according to the genetic algorithm characteristics of simple and high scalability, which can search information based on the objective function, can provide better solutions. E second one is that introducing the other intelligent optimization algorithms to mix into the traditional genetic algorithms and get the hybrid genetic algorithms, which is able to provide new ideas of genetic algorithms to solve. In [19], a solution algorithm based on genetic simulated annealing was designed, which owns the strong global search capability of the genetic algorithm, as well as the high rate of convergence of the simulated annealing optimization. Inspired by the above two methods, in this paper, an improved genetic algorithm based on fuzzy C-means algorithm (FCM) is used to solve CVRP problems. The experiment by using benchmark examples is introduced, and the experiment demonstrates that this algorithm can solve CVRP problems with better precision and effectiveness
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