• Previous Article
    Globally convergent homotopy method for designing piecewise linear deterministic contractual function
  • JIMO Home
  • This Issue
  • Next Article
    Single-machine scheduling and due date assignment with rejection and position-dependent processing times
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
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

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

[1]

Yaw Chang, Lin Chen. Solve the vehicle routing problem with time windows via a genetic algorithm. Conference Publications, 2007, 2007 (Special) : 240-249. doi: 10.3934/proc.2007.2007.240

[2]

Jiao-Yan Li, Xiao Hu, Zhong Wan. An integrated bi-objective optimization model and improved genetic algorithm for vehicle routing problems with temporal and spatial constraints. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-18. doi: 10.3934/jimo.2018200

[3]

Erfan Babaee Tirkolaee, Alireza Goli, Mani Bakhsi, Iraj Mahdavi. A robust multi-trip vehicle routing problem of perishable products with intermediate depots and time windows. Numerical Algebra, Control & Optimization, 2017, 7 (4) : 417-433. doi: 10.3934/naco.2017026

[4]

Tao Zhang, W. Art Chaovalitwongse, Yue-Jie Zhang, P. M. Pardalos. The hot-rolling batch scheduling method based on the prize collecting vehicle routing problem. Journal of Industrial & Management Optimization, 2009, 5 (4) : 749-765. doi: 10.3934/jimo.2009.5.749

[5]

Mingyong Lai, Xiaojiao Tong. A metaheuristic method for vehicle routing problem based on improved ant colony optimization and Tabu search. Journal of Industrial & Management Optimization, 2012, 8 (2) : 469-484. doi: 10.3934/jimo.2012.8.469

[6]

Ming-Yong Lai, Chang-Shi Liu, Xiao-Jiao Tong. A two-stage hybrid meta-heuristic for pickup and delivery vehicle routing problem with time windows. Journal of Industrial & Management Optimization, 2010, 6 (2) : 435-451. doi: 10.3934/jimo.2010.6.435

[7]

Linet Ozdamar, Dilek Tuzun Aksu, Elifcan Yasa, Biket Ergunes. Disaster relief routing in limited capacity road networks with heterogeneous flows. Journal of Industrial & Management Optimization, 2018, 14 (4) : 1367-1380. doi: 10.3934/jimo.2018011

[8]

Nurhadi Siswanto, Stefanus Eko Wiratno, Ahmad Rusdiansyah, Ruhul Sarker. Maritime inventory routing problem with multiple time windows. Journal of Industrial & Management Optimization, 2019, 15 (3) : 1185-1211. doi: 10.3934/jimo.2018091

[9]

Anh Son Ta, Le Thi Hoai An, Djamel Khadraoui, Pham Dinh Tao. Solving Partitioning-Hub Location-Routing Problem using DCA. Journal of Industrial & Management Optimization, 2012, 8 (1) : 87-102. doi: 10.3934/jimo.2012.8.87

[10]

Chia-Chun Hsu, Hsun-Jung Cho, Shu-Cherng Fang. Solving routing and wavelength assignment problem with maximum edge-disjoint paths. Journal of Industrial & Management Optimization, 2017, 13 (2) : 1065-1084. doi: 10.3934/jimo.2016062

[11]

Xuefeng Wang. The heterogeneous fleet location routing problem with simultaneous pickup and delivery and overloads. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1147-1166. doi: 10.3934/dcdss.2019079

[12]

Huai-Che Hong, Bertrand M. T. Lin. A note on network repair crew scheduling and routing for emergency relief distribution problem. Journal of Industrial & Management Optimization, 2019, 15 (4) : 1729-1731. doi: 10.3934/jimo.2018119

[13]

Giuseppe Buttazzo, Serena Guarino Lo Bianco, Fabrizio Oliviero. Optimal location problems with routing cost. Discrete & Continuous Dynamical Systems - A, 2014, 34 (4) : 1301-1317. doi: 10.3934/dcds.2014.34.1301

[14]

Ahmed Tarajo Buba, Lai Soon Lee. Differential evolution with improved sub-route reversal repair mechanism for multiobjective urban transit routing problem. Numerical Algebra, Control & Optimization, 2018, 8 (3) : 351-376. doi: 10.3934/naco.2018023

[15]

Didem Cinar, José António Oliveira, Y. Ilker Topcu, Panos M. Pardalos. A priority-based genetic algorithm for a flexible job shop scheduling problem. Journal of Industrial & Management Optimization, 2016, 12 (4) : 1391-1415. doi: 10.3934/jimo.2016.12.1391

[16]

A. Cascone, Alessia Marigo, B. Piccoli, L. Rarità. Decentralized optimal routing for packets flow on data networks. Discrete & Continuous Dynamical Systems - B, 2010, 13 (1) : 59-78. doi: 10.3934/dcdsb.2010.13.59

[17]

Wen-Li Li, Jing-Jing Wang, Xiang-Kui Zhang, Peng Yi. A novel road dynamic simulation approach for vehicle driveline experiments. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1035-1052. doi: 10.3934/dcdss.2019071

[18]

T. W. Leung, Chi Kin Chan, Marvin D. Troutt. A mixed simulated annealing-genetic algorithm approach to the multi-buyer multi-item joint replenishment problem: advantages of meta-heuristics. Journal of Industrial & Management Optimization, 2008, 4 (1) : 53-66. doi: 10.3934/jimo.2008.4.53

[19]

Lan Luo, Zhe Zhang, Yong Yin. Simulated annealing and genetic algorithm based method for a bi-level seru loading problem with worker assignment in seru production systems. Journal of Industrial & Management Optimization, 2017, 13 (5) : 0-0. doi: 10.3934/jimo.2019134

[20]

Ruiqiang Guo, Lu Song. Optical chaotic secure algorithm based on space laser communication. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1355-1369. doi: 10.3934/dcdss.2019093

2018 Impact Factor: 1.025

Metrics

  • PDF downloads (34)
  • HTML views (0)
  • Cited by (4)

[Back to Top]