A hybrid genetic algorithm for the vehicle routing problem with simultaneous pickup and delivery

Authors:
Fanggeng Zhao;Dong Mei;Jiangsheng Sun;Weimin Liu
Affiliations:
Vehicle Management Institute, Bengbu, China;Vehicle Management Institute, Bengbu, China;Ordnance Technology Research Institute, Shijiazhuang, China;School of Mechanical Engineering, Hebei Polytechnic University, Tangshan, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 9
Cited 0

The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best

Proceedings of the third international conference on Genetic algorithms
MAX-MIN Ant system

Future Generation Computer Systems
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence

Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Performance of Various Computers Using Standard Linear Equations Software

Performance of Various Computers Using Standard Linear Equations Software
A hybrid genetic algorithm for the job shop scheduling problems

Computers and Industrial Engineering
Ant Algorithms: Theory and Applications

Programming and Computing Software
A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service

Computers and Operations Research
A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery

Computers and Operations Research
Implementation of an Effective Hybrid GA for Large-Scale Traveling Salesman Problems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The vehicle routing problem with simultaneous pickup and delivery is an important variation of VRP where customers require simultaneous pickup and delivery service. In this paper, we proposed a hybrid genetic algorithm to solve this problem. In the proposed algorithm, we proposed a pheromone-based crossover operator that utilizes both the local and global information to construct offspring. The local information used in crossover operator includes edge lengths and adjacency relations, while the global information is stored as pheromone trails. To improve the performance of genetic algorithm, a local search procedure is integrated into GA, and acts as the mutation operator. Our hybrid algorithm was tested on benchmark instances, and experimental results are conclusively in favor of our algorithm.