Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 3rd International Conference on Genetic Algorithms
A genetic algorithm for the vehicle routing problem
Computers and Operations Research
Design and Analysis of Experiments
Design and Analysis of Experiments
A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem
Expert Systems with Applications: An International Journal
Solving capacitated vehicle routing problems via genetic particle swarm optimization
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Solving capacitated vehicle routing problems by modified differential evolution
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
A new geometric shape-based genetic clustering algorithm for the multi-depot vehicle routing problem
Expert Systems with Applications: An International Journal
A distribution network optimization problem for third party logistics service providers
Expert Systems with Applications: An International Journal
Multiple Phase Neighborhood Search-GRASP for the Capacitated Vehicle Routing Problem
Expert Systems with Applications: An International Journal
Survey of Green Vehicle Routing Problem: Past and future trends
Expert Systems with Applications: An International Journal
| Hi-index | 12.07 |
This study primarily focuses on solving a capacitated vehicle routing problem (CVRP) by applying a novel hybrid genetic algorithm (HGA) capable of practical use for manufacturers. The proposed HGA has three stages. First, the nearest addition method (NAM) was incorporated into sweep algorithm (SA) that simultaneously accounts for axial and radius relationships among distribution points with the depot to generate a well-structured initial chromosome population, rather than adopting either the NAM OR SA alone. Second, response surface methodology (RSM) was employed to optimize crossover probability and mutation probability via systematic experiments. Finally, an improved sweep algorithm was incorporated into the GA, producing a stir over gene permutations in chromosomes that enhance the exploration diversity of the GA, thereby avoiding convergence in a limited region, and enhancing the search capability of the GA in approaching a close-to-optimal solution. Furthermore, an elitism conservation strategy holding superior chromosomes to replace inferior chromosomes was also performed. As the proposed HGA is primarily used to solve practical problem, benchmark problems with fewer than 100 distribution points from an Internet website were utilized to confirm the effectiveness of the proposed HGA. A real case regarding the mission of local active distribution from armed forces in Taiwan details the analytical process and demonstrates the practicability of the proposed HGA to optimize the CVRP.