Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Cost-based multi-parameter logistics routing path optimization algorithm

1 School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
2 O'Neill School of Public and Environmental Affairs, Indiana University, Bloomington, USA
3 Control Technology Institute, Wuxi Institute of Technology, Wuxi, Jiangsu, China

The traditional path optimization problem is to consider the shortest path of the vehicle, but the shortest path does not effectively reduce the logistics cost. On the contrary, in the case of one-sided pursuit of the shortest path, it may cause some negative effects. This paper constructs a more realistic path optimization model on the path of traditional logistics distribution, and designs a path model based on simulated annealing algorithm which taking fuel consumption, cost, road gradient and condition of vehicle into account. The algorithm model of load capacity and other problems is used to verify the algorithm of the model through a simulation case of multiple distribution points. The experimental results show that the path optimization strategy considering the gradient of the road reduces the cost of the vehicle path, indicating the correctness of considering the vehicle load and road gradient factors in logistics transportation.
  Article Metrics

Keywords path optimization; simulated annealing; fuel consumption analysis; cost optimization; road condition analysis

Citation: Fulin Dang, Chunxue Wu, Yan Wu, Rui Li, Sheng Zhang, Huang Jiaying, Zhigang Liu. Cost-based multi-parameter logistics routing path optimization algorithm. Mathematical Biosciences and Engineering, 2019, 16(6): 6975-6989. doi: 10.3934/mbe.2019350


  • 1. Y. Gong, J. Zhang, O. Liu, et al., Optimizing the vehicle routing problem with time windows: A discrete particle swarm optimization approach, IEEE Trans. Syst. Man Cybernetics Part C (Appl. Rev.), 42 (2012), 254–267.
  • 2. A. L. Bouthillier, T. G. Crainic and P. Kropf, A guided cooperative search for the vehicle routing problem with time windows, IEEE Intell. Syst., 20 (2005), 36–42.
  • 3. J. Choi and K. Huhtala, Constrained global path optimization for articulated steering vehicles, IEEE Trans. Veh. Technol., 65 (2016), 1868–1879.
  • 4. Z. H. Hu, Multi-objective optimization model for emergency logistics distribution with multiple supply points and multiple resource categories, 2010 2nd International Conference on Industrial and Information Systems, 2010. Available from: https://ieeexplore.ieee.org/document/5565885.
  • 5. Y. Bouzembrak, H. Allaoui, G. Goncalves, et al., A multi-objective green supply chain network design, 2011 4th International Conference on Logistics, 2011. Available from: https://ieeexplore.ieee.org/document/5939315.
  • 6. D. Pamučar, S. Ljubojević, D. Kostadinović, et al., Cost and risk aggregation in multi-objective route planning for hazardous materials transportation-A neuro-fuzzy and artificial bee colony approach, Expert Syst. Appl., 65 (2016), 1–15.
  • 7. L. Wu, Z. He, Y. Chen, et al., Brainstorming-based ant colony optimization for vehicle routing with soft time windows, IEEE Access, 7 (2019), 19643–19652.
  • 8. H. C. W. Lau, T. M. Chan, W. T. Tsui, et al., Application of genetic algorithms to solve the multidepot vehicle routing problem, IEEE Trans. Autom. Sci. Eng., 7 (2010), 383–392.
  • 9. X. Shan, P. Hao, X. Chen, et al., Vehicle energy/emissions estimation based on vehicle trajectory reconstruction using sparse mobile sensor data, IEEE Trans. Int. Transp. Syst., 20 (2019), 716–726.
  • 10. Y. Guo, J. Cheng, S. Luo, et al., Robust dynamic multi-objective vehicle routing optimization method, IEEE/ACM Trans. Comp. Biol. Bioinf., 15 (2018), 1891–1903.
  • 11. D. Pamučar and G. Cirovic, Vehicle route selection with an adaptive neuro fuzzy inference system in uncertainty conditions, Decis. Making: Appl. Manage. Eng., 1 (2018), 13–37.
  • 12. G. Ćirović, D. Pamučar and D. Božanić, Green logistic vehicle routing problem: Routing light delivery vehicles in urban areas using a neuro-fuzzy model, Expert Syst. Appl., 41 (2014), 4245–4258.
  • 13. X. Tang, S. Bi and Y. A. Zhang, Distributed routing and charging scheduling optimization for internet of electric vehicles, IEEE Int. Things J. 6 (2019), 136–148.
  • 14. J. Clarke, V. Gascon and J. A. Ferland, A capacitated vehicle routing problem with synchronized pick-ups and drop-offs: The case of medication delivery and supervision in the DR congo, IEEE Trans. Eng. Manage., 64 (2017), 327–336.
  • 15. J. Wang, J. Wu and Y. Li, The driving safety field based on driver–vehicle–road interactions, IEEE Trans. Int. Transp. Syst., 16 (2015), 2203–2214.
  • 16. T. Hirayama, K. Mase, C. Miyajima, et al., Classification of Driver's neutral and cognitive distraction states based on peripheral vehicle behavior in Driver's gaze transition, IEEE Trans. Int. Veh., 1 (2016), 148–157.
  • 17. H. Sun, W. Li and Y. Xue, A hybrid intelligent algorithm for multiple capacitated vehicle routing problem, 2010 2nd IEEE International Conference on Information Management and Engineering, 2010. Available from: https://ieeexplore.ieee.org/document/5477635.
  • 18. P. Coussement, D. Bauwens, J. Maertens, et al., Direct combinatorial pathway optimization, ACS Synth. Biol., 6 (2017), 224–232.
  • 19. Z. Wang and C. Ling, On the geometric ergodicity of metropolis-hastings algorithms for lattice gaussian sampling, IEEE Trans. Inform. Theory, 64 (2018), 738–751.
  • 20. O. Chatterjee and S. Chakrabartty, Decentralized global optimization based on a growth transform dynamical system model, IEEE Trans. Neural Networks Learning Syst., 29 (2018), 6052–6061.
  • 21. M. Niendorf and A. R. Girard, Exact and approximate stability of solutions to traveling salesman problems, IEEE Trans. Cybernetics, 48 (2018), 583–595.
  • 22. İ. L. Sarioglu, O. P. Klein, H. Schroder, et al., Energy management for fuel-cell hybrid vehicles based on specific fuel consumption due to load shifting, IEEE Trans. Int. Transp. Syst., 13 (2012), 1772–1781.
  • 23. S. Wang, S. Djahel, Z. Zhang, et al., Next road rerouting: A multiagent system for mitigating unexpected urban traffic congestion, IEEE Trans. Int. Transp. Syst., 17 (2016), 2888–2899.
  • 24. X. Zuo, C. Chen, W. Tan, et al., Vehicle scheduling of an urban bus line via an improved multiobjective genetic algorithm, IEEE Trans. Int. Transp. Syst., 16 (2015), 1030–1041.
  • 25. L. Wang and J. Lu, A memetic algorithm with competition for the capacitated green vehicle routing problem, IEEE/CAA J. Autom. Sinica, 6 (2019), 516–526.
  • 26. X. Li and M. Li, Multiobjective local search algorithm-based decomposition for multiobjective permutation flow shop scheduling problem, IEEE Trans. Eng. Manage. 62 (2015), 544–557.
  • 27. J. Li, P. Song, K. Li, et al., A modified particle swarm optimization with adaptive selection operator and mutation operator, 2008 International Conference on Computer Science and Software Engineering, 2008. Available from: https://ieeexplore.ieee.org/document/4721968.
  • 28. A. Panichella, R. Oliveto, M. D. Penta, et al., Improving multi-objective test case selection by injecting diversity in genetic algorithms, IEEE Trans. Software Eng., 41 (2015), 358–383.
  • 29. A. Babin, N. Rizoug, T. Mesbahi, et al., Total cost of ownership improvement of commercial electric vehicles using battery sizing and intelligent charge method, IEEE Trans. Industry Appl., 54 (2018), 1691–1700.


Reader Comments

your name: *   your email: *  

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved