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Mathematical Problems in Engineering
Volume 2018, Article ID 8961505, 9 pages
https://doi.org/10.1155/2018/8961505
Research Article

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

1College of Business Administration, Hunan University, No. 11 Lushan South Road, Changsha 410082, China
2Business School, Hunan University of Science and Technology, Taoyuan Road, Xiangtan 411201, China
3Liverpool Business School, Liverpool John Moores University, Redmonds Building, Brownlow Hill, Liverpool L3 5UX, UK

Correspondence should be addressed to Mi-Yuan Shan; moc.361@jgnauyimnahs

Received 16 November 2017; Accepted 28 February 2018; Published 11 April 2018

Academic Editor: Salvatore Strano

Copyright © 2018 Xiao-Hong Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. M. F. M. AbdAllah, D. L. Essam, and R. A. Sarker, “On solving periodic re-optimization dynamic vehicle routing problems,” Applied Soft Computing, vol. 55, no. 6, pp. 1–12, 2017. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Qian and R. Eglese, “Fuel emissions optimization in vehicle routing problems with time-varying speeds,” European Journal of Operational Research, vol. 248, no. 3, pp. 840–848, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  3. M. Marinaki and Y. Marinakis, “A glowworm swarm optimization algorithm for the vehicle routing problem with stochastic demands,” Expert Systems with Applications, vol. 46, no. 3, pp. 145–163, 2016. View at Publisher · View at Google Scholar · View at Scopus
  4. W. Lefever, E.-H. Aghezzaf, and K. Hadj-Hamou, “A convex optimization approach for solving the single-vehicle cyclic inventory routing problem,” Computers & Operations Research, vol. 72, no. 8, pp. 97–106, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Zhao, L. Leng, Z. Qian, and W. Wang, “A discrete hybrid invasive weed optimization algorithm for the capacitated vehicle routing problem,” Procedia Computer Science, vol. 91, no. 7, pp. 978–987, 2016. View at Google Scholar
  6. J. A. Sicilia, C. Quemada, B. Royo, and D. Escuín, “An optimization algorithm for solving the rich vehicle routing problem based on Variable Neighborhood Search and Tabu Search metaheuristics,” Journal of Computational and Applied Mathematics, vol. 291, no. 2, Article ID 10105, pp. 468–477, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  7. F. Errico, G. Desaulniers, M. Gendreau, W. Rei, and L.-M. Rousseau, “A priori optimization with recourse for the vehicle routing problem with hard time windows and stochastic service times,” European Journal of Operational Research, vol. 249, no. 1, pp. 55–66, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. N. Norouzi, M. Sadegh-Amalnick, and M. Alinaghiyan, “Evaluating of the particle swarm optimization in a periodic vehicle routing problem,” Measurement, vol. 62, no. 2, pp. 162–169, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. L. J. Tan, F. Y. Lin, and H. Wang, “Adaptive comprehensive learning bacterial foraging optimization and its application on vehicle routing problem with time windows,” Neurocomputing, vol. 151, no. 3, pp. 1208–1215, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. E. Jabir, V. V. Panicker, and R. Sridharan, “Multi-objective optimization model for a green vehicle routing problem,” Procedia - Social and Behavioral Sciences, vol. 189, no. 3, pp. 33–39, 2015. View at Publisher · View at Google Scholar
  11. Q. Ding, X. Hu, L. Sun, and Y. Wang, “An improved ant colony optimization and its application to vehicle routing problem with time windows,” Neurocomputing, vol. 98, no. 12, pp. 101–107, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. F. P. Goksal, I. Karaoglan, and F. Altiparmak, “A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery,” Computers & Industrial Engineering, vol. 65, no. 1, pp. 39–53, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Marinakis, G.-R. Iordanidou, and M. Marinaki, “Particle Swarm Optimization for the vehicle routing problem with stochastic demands,” Applied Soft Computing, vol. 13, no. 4, pp. 1693–1704, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. K. V. Narasimha, E. Kivelevitch, B. Sharma, and M. Kumar, “An ant colony optimization technique for solving min-max multi-depot vehicle routing problem,” Swarm and Evolutionary Computation, vol. 13, pp. 63–73, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Marinakis and M. Marinaki, “A bumble bees mating optimization algorithm for the open vehicle routing problem,” Swarm and Evolutionary Computation, vol. 15, pp. 80–94, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. E. Osaba and F. Díaz, “Design and implementation of a combinatorial optimization multi-population meta-heuristic for solving vehicle routing problems,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, no. 2, pp. 89-90, 2016. View at Publisher · View at Google Scholar
  17. A. Fraile, E. Larrodé, Á. Alberto Magreñán, and J. A. Sicilia, “Decision model for siting transport and logistic facilities in urban environments: A methodological approach,” Journal of Computational and Applied Mathematics, vol. 291, no. 10, pp. 478–487, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Turkensteen, “The accuracy of carbon emission and fuel consumption computations in green vehicle routing,” European Journal of Operational Research, vol. 262, no. 2, pp. 647–659, 2017. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Zhang, Y. Zhao, W. Xue, and J. Li, “Vehicle routing problem with fuel consumption and carbon emission,” International Journal of Production Economics, vol. 170, pp. 234–242, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. H. Li, J. Yuan, T. Lv, and X. Chang, “The two-echelon time-constrained vehicle routing problem in linehaul-delivery systems considering carbon dioxide emissions,” Transportation Research Part D: Transport and Environment, vol. 49, no. 12, pp. 231–245, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. Q. Lin, Q. Zhu, P. Huang, J. Chen, Z. Ming, and J. Yu, “A novel hybrid multi-objective immune algorithm with adaptive differential evolution,” Computers & Operations Research, vol. 62, no. 10, pp. 95–111, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Shang, B. Du, H. Ma, L. Jiao, Y. Xue, and R. Stolkin, “Immune clonal algorithm based on directed evolution for multi-objective capacitated arc routing problem,” Applied Soft Computing, vol. 49, no. 12, pp. 748–758, 2016. View at Publisher · View at Google Scholar · View at Scopus
  23. Z. Liang, R. Song, Q. Lin et al., “A double-module immune algorithm for multi-objective optimization problems,” Applied Soft Computing, vol. 35, no. 10, pp. 161–174, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. J. A. Sicilia, B. Royo, C. Quemada, M. J. Oliveros, and E. Larrodé, “A decision support system to long haul freight transportation by means of ant colony optimization,” DYNA Ingeniería e Industria, vol. 90, no. 1, pp. 105–113, 2015. View at Publisher · View at Google Scholar