Scheduling Problem of Shared Car Based on Fish Swarm Algorithm
International Journal of Management and Fuzzy Systems
Volume 4, Issue 3, September 2018, Pages: 41-45
Received: Jul. 9, 2018;
Accepted: Aug. 2, 2018;
Published: Aug. 31, 2018
Views 1288 Downloads 96
Linlin Shen, College of Civil Engineering and Architecture, Hebei University, Baoding, China
Xiaodong Pan, College of Civil Engineering and Architecture, Hebei University, Baoding, China
Jingbo Zhou, College of Civil Engineering and Architecture, Hebei University, Baoding, China
Longcheng Xing, College of Civil Engineering and Architecture, Hebei University, Baoding, China
Follow on us
In order to improve the utilization and competitiveness of shared vehicles, the emerging car sharing system tends to provide one-way mode without reservation and allow remote borrowing. Unbooked one-way vehicle sharing system is characterized by the opening of vehicle mobility, allowing vehicles to return at other stations. But it leads to the imbalance of demand distribution in a certain period of time. When the demand is satisfied and the trip is completed, the vehicle will deviate from the original layout. The subsequent demand for areas with large demand can not be met, and vehicles with low demand are idle. This paper considers the sustainable development of shared car rental companies. In order to optimize the profit of shared car rental enterprises and enhance their competitiveness, intelligent algorithm is used to optimize the scheduling of vehicles with different outlets. So as to maximize service quality and company profits. First, a mathematical model for the scheduling of shared car is established. Secondly, different scheduling strategies are designed for different network scheduling. At last, an artificial fish swarm algorithm is used to analyze the case in MATLAB. There are two car outlets in the car rental company, with a maximum of 20 cars available for lease at each location, and the most profitable scheduling method when the most of the 5 cars are scheduled to be transferred every day.
Shared Car, Artificial Fish Swarm Algorithm, Scheduling Scheme, Maximum Profit
To cite this article
Scheduling Problem of Shared Car Based on Fish Swarm Algorithm, International Journal of Management and Fuzzy Systems.
Vol. 4, No. 3,
2018, pp. 41-45.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
L. J. Yin, X. D. Wang, Z. Q. Xiong, Summary of research on vehicle sharing services [J]. Science and Technology Monthly, 2010 (10):98-100.
L. j. Jia, S. G. Xin, Research on feasibility analysis based on investment projects [J]. Today Keyuan, 2008 (6):108-108.
L. Y. Ma, X. Y. Liu, Feasibility analysis and Countermeasures of Xiamen automobile sharing service [J]. Business, 2016 (9): 244-244.
B. J. Jiang, Research on matching of automobile supply and demand under shared lease mode [D]. HeFei University of Technology, 2017.
P. F. Zhou, J. Qiao, L. Li, Research on shared vehicle intelligent scheduling expert system [J]. Computer application and software, 2018, 35 (04):109-111+190.
L. Hong, Particle swarm optimization and artificial fish swarm algorithm optimization research [J], software, 2014, 08:83~86.
M. Wang, Based on information weighting adaptive ant colony algorithm to solve TSP problem [J]. Chinese science and technology thesis, 2015, 10 (05):573-576.
D. l. Liu, A summary of genetic algorithms [J]. Western China Science and technology, 2009, 8 (25):41-43.
L. D. Qu, D. X. He, A chaos artificial fish swarm optimization algorithm [J]. Computer engineering and application, 2010, 22:40~42.
S. H. Yu, S. B. Su, Reaserch and application of chaotic glowworm swarm optimization algorithm [J], Journal of Frontiers of Computer Science and Technology [J], 2014, 8 (3):352-358.
X. L. Li, F. Lu, G. H. Tian, J. X. Qian, Application of artificial fish swarm algorithm for combinatorial optimization problem [J]. Journal of Shandong University (Engineering Edition), 2004 (05):64-67.
L. D. Qu, D. X. He, A chaos artificial fish swarm optimization algorithm [J]. computer engineering and application, 2010, 46 (22): 40-42.
X. L. Li, Z. J. Shao, J. X. Qian, An optimization model based on animal autonomy: fish swarm algorithm [J]. Theory and practice of system engineering, 2002 (11): 32-38.
M. Li, S. H. F, Forex Prediction Based on SVR Optimized by Artificial Fish Swarm Algorithm [C]//Intelligent Systems (GCIS), 2013 Fourth Global Congress on. IEEEE, 2013:47-52.
X. M. Ma, N. Liu, Adaptive field of view artificial fish swarm algorithm for shortest path problem [J]. Journal of communication, 2014, 35 (01): 1-6.