Vehicle Routing Problem Based on Heuristic Artificial Fish School Algorithm

Lei Qin; Ya Qin Li; Kang Zhou

doi:10.4028/www.scientific.net/AMM.721.56

Paper Titles

Effects of Traffic Calming Measures on Vehicle Speed Control and Road Safety
p.39

Violation Behavior Analysis of Pedestrian and Non-Motorized Vehicle
p.43

The Study of Traffic Rules Based on Cellular Automata Traffic Wave Model
p.47

The Overall Structure of IOV and the Research of the Unsolved Key Technology of IOV in Intelligent Transportation System
p.52

Vehicle Routing Problem Based on Heuristic Artificial Fish School Algorithm
p.56

An Improved Cellular Automaton Model of Traffic Regulations and the Effect of Deceleration Probability
p.62

Smoke Management System Design for Airport Terminal
p.66

Influence of Guide Vane Wrap Angle Key Design Parameters on Hydraulic Performance of Nuclear Reactor Coolant Pump
p.73

Numerical Investigation on the Flow in Tip Clearance of the Last Three Stages of Industrial Steam Turbine
p.78

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 721Vehicle Routing Problem Based on Heuristic...

Vehicle Routing Problem Based on Heuristic Artificial Fish School Algorithm

Abstract:

Vehicle Routing Problem (VRP) is one of the core issue of logistics distribution, for traditional precision algorithms and heuristic algorithms had low accuracies or easily fell into local optimal solutions, it was difficult to obtain the optimal solution. This paper proposes a heuristic artificial fish school algorithm (HAFSA) for VRP, firstly, three-dimensional particle coding method is applied to vehicle routing code, and infeasible and inadequate artificial fish coding for heuristic repair, secondly HAFSA steps are given, finally the algorithm is tested through a simulative example. The experimental results show that compared with traditional genetic algorithm (GA) and particle swarm optimization (PSO), AFSA and their extension algorithms, HAFSA has a better performance in time and space cost and convergence.

You might also be interested in these eBooks

Vehicle, Mechanical and Electrical Engineering

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 721)

Pages:

56-61

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.721.56

Citation:

Cite this paper

Online since:

December 2014

Authors:

Lei Qin, Ya Qin Li, Kang Zhou

Keywords:

Artificial Fish School Algorithm, Coding, Vehicle Routing Problem

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] G. Laport: The vehicle routing problem: An overview of exact and approximate algorithms, European Journal of Operational Research, vol. 59 (1992) no. 4, pp.345-358.

DOI: 10.1016/0377-2217(92)90192-c

Google Scholar

[2] Y.W. Zhao, B. Wu, L. Jiang, et al: Double populations genetic algorithm for Vehicle Routing Problem, Computer Integrated Manufacturing Systems, vol. 10 (2004) No. 3, pp.303-306.

Google Scholar

[3] B.M. Baker, M.A. Ayechew: A genetic algorithm for the vehicle routing problem. Computers and Operations Research, vol. 30 (2003) no. 5, pp.787-800.

DOI: 10.1016/s0305-0548(02)00051-5

Google Scholar

[4] B. Ombuki, B.J. Ross, F. Hanshar: Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows, Applied Intelligence, vol. 24 (2006) no. 1, pp.17-30.

DOI: 10.1007/s10489-006-6926-z

Google Scholar

[5] B. Bullnheimer, R.F. Hartl, C. Strauss: An improved ant system algorithm for the vehicle routing problem, Annals of Operation Research, vol. 89(1999) no. 13, pp.319-328.

DOI: 10.1023/a:1018940026670

Google Scholar

[6] Z.X. Liu: Vehicle scheduling optimization in logistics distribution based on particle swarm optimization algorithm, Journal of Wuhan University of Science and Technology (Natural science edition), vol. 32 (2009) no. 6, pp.615-618.

Google Scholar

[7] X.L. Li, Z.J. Shao, J.X. Qian: An Optimizing Method Based on Autonomous Animats: Fish-swarm Algorithm, Systems Engineering Theory and Practice, vol. 22 (2002) no. 11, pp.32-38.

Google Scholar

[8] X.L. Li, F. Lu, G.H. Tian, et al: Applications of Artificial fish school algorithm in Combinatorial optimization problems, Journal of Shandong University (Engineering Science), vol. 34 (2004) no. 5, pp.64-67.

Google Scholar