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July  2014, 10(3): 701-715. doi: 10.3934/jimo.2014.10.701

Cyber-physical logistics system-based vehicle routing optimization

1. 

Key Laboratory of Logistics Information and Simulation Technology, Hunan University, Changsha 410082

2. 

School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China, China

3. 

The Centre for Intelligent Electricity Networks, The University of Newcastle, NSW 2308, Australia, Australia

Received  March 2013 Revised  June 2013 Published  November 2013

Vehicle routing problem is a classic combinational optimization problem, which has been attracting research attentions in logistics and optimization area. Conventional static vehicle routing problem assumes the logistics information is accurate and timely, and does not take into account the uncertainties, which is therefore inadequate during practical applications. In this paper, a vehicle initial routing optimization model considering uncertainties is proposed, the vehicle capacity, customer time-window, and the maximum travelling distance as well as the road capacity are considered. In the cyber-physical logistics system background, a routing adjustment model is proposed to minimize the total distribution cost considering the road congestion, and the static and dynamic models are proposed for traffic information transmission network to quantitatively analyse the impact of the traffic information transmission delay on the vehicle routing optimization. The learnable genetic algorithm is adopted to solve the initial routing optimization model and the routing adjustment model. The simulation results have verified its effectiveness.
Citation: Mingyong Lai, Hongming Yang, Songping Yang, Junhua Zhao, Yan Xu. Cyber-physical logistics system-based vehicle routing optimization. Journal of Industrial & Management Optimization, 2014, 10 (3) : 701-715. doi: 10.3934/jimo.2014.10.701
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J. Yves-Potvin, X. Ying and B. Ilham, Vehicle routing and scheduling with dynamic travel times,, Computers & Operations Research, 33 (2006), 1129.   Google Scholar

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show all references

References:
[1]

R. Akella, H. Tang and B. M. McMillin, Analysis of information flow security in cyber-physical systems,, International Journal of Critical Infrastructure Protection, 3 (2010), 157.  doi: 10.1016/j.ijcip.2010.09.001.  Google Scholar

[2]

M. Burmester, E. Magkos and V. Chrissikopoulos, Modeling security in cyber-physical systems,, Critical Infrastructure Protection, 5 (2012), 118.  doi: 10.1016/j.ijcip.2012.08.002.  Google Scholar

[3]

M. Conti et al, Looking ahead in pervasive computing: challenges and opportunities in the era of cyber-physical convergence,, Pervasive and Mobile Computing, 8 (2012), 2.   Google Scholar

[4]

E. B. Cao and M. Y. Lai, A hybrid differential evolution algorithm to vehicle route problem with fuzzy demands,, Journal of Computational and Applied Mathematics, 231 (2009), 302.  doi: 10.1016/j.cam.2009.02.015.  Google Scholar

[5]

G. B. Dantzig and J. H. Ramser, The truck dispatching problem,, Management Science, 10 (1959), 80.  doi: 10.1287/mnsc.6.1.80.  Google Scholar

[6]

J. W. Fang, F. K. Yu and C. T. Yu, From wireless sensor networks towards cyber physical systems,, Pervasive and Mobile Computing, 7 (2011), 397.   Google Scholar

[7]

J. Q. Li, P. B. Mirchandani and D. Borenstein, Real-time vehicle rerouting problems with time windows,, European Journal of Operational Research, 194 (2009), 711.  doi: 10.1016/j.ejor.2007.12.037.  Google Scholar

[8]

J. Lee, S. Bohacek and J. P. Hespanha, et al, Modeling communication networks with hybrid systems,, IEEE/ACM Trans. Networking, 25 (2007), 630.  doi: 10.1109/TNET.2007.893090.  Google Scholar

[9]

D. Teodorovic and G. Pavkovic, The fuzzy set theory approach to the vehicle routing problem when demand at nodes is uncertain,, Fuzzy Sets and Systems, 82 (1996), 307.  doi: 10.1016/0165-0114(95)00276-6.  Google Scholar

[10]

L. N. Xing and F. Yao, Learnable genetic algorithm to double-layer CARP optimization problems,, Systems Engineering and Electronics, 34 (2012), 1187.   Google Scholar

[11]

J. Yves-Potvin, X. Ying and B. Ilham, Vehicle routing and scheduling with dynamic travel times,, Computers & Operations Research, 33 (2006), 1129.   Google Scholar

[12]

Y. S. Zheng and B. D. Liu, Fuzzy vehicle routing model with credibility measure and its hybrid intelligent algorithm,, Applied Mathematics and Computation, 176 (2006), 673.  doi: 10.1016/j.amc.2005.10.013.  Google Scholar

[13]

H. Zimmermann, OSI reference model-the ISO model of architecture for open systems interconnection,, IEEE Trans. Communication, 28 (1980), 425.  doi: 10.1109/TCOM.1980.1094702.  Google Scholar

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