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Volume 2018 (2018), Article ID 5754908, 12 pages
https://doi.org/10.1155/2018/5754908
Research Article

A Hybrid Tabu Search Algorithm for a Real-World Open Vehicle Routing Problem Involving Fuel Consumption Constraints

1School of Information Engineering, China University of Geosciences, Beijing, Beijing 100083, China
2School of Business, Nankai University, Tianjin 300071, China
3The Research Center of Logistics, Nankai University, Tianjin 300071, China

Correspondence should be addressed to Jianhua Xiao; moc.361@8002oaixhj

Received 4 October 2017; Accepted 1 February 2018; Published 28 February 2018

Academic Editor: Danilo Comminiello

Copyright © 2018 Yunyun Niu 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. L. Schrage, “Formulation and structure of more complex/realistic routing and scheduling problems,” Networks, vol. 11, no. 2, pp. 229–232, 1981. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Raff, “Routing and scheduling of vehicles and crews: the state of the art,” Computers and Operations Research, vol. 10, no. 2, pp. 63–211, 1983. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Brandao, “A tabu search algorithm for the open vehicle routing problem,” European Journal of Operational Research, vol. 157, no. 3, pp. 552–564, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  4. U. Derigs and K. Reuter, “A simple and efficient tabu search heuristic for solving the open vehicle routing problem,” Journal of the Operational Research Society, vol. 60, no. 12, pp. 1658–1669, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. E. E. Zachariadis and C. T. Kiranoudis, “An open vehicle routing problem metaheuristic for examining wide solution neighborhoods,” Computers and Operations Research, vol. 37, no. 4, pp. 712–723, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. S. A. Mirhassani and N. Abolghasemi, “A particle swarm optimization algorithm for open vehicle routing problem,” Expert Systems with Applications, vol. 38, no. 9, pp. 11547–11551, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. X.-Y. Li, P. Tian, and S. C. H. Leung, “An ant colony optimization metaheuristic hybridized with tabu search for open vehicle routing problems,” Journal of the Operational Research Society, vol. 60, no. 7, pp. 1012–1025, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Yu, C. Ding, and K. Zhu, “A hybrid GA-TS algorithm for open vehicle routing optimization of coal mines material,” Expert Systems with Applications, vol. 38, no. 8, pp. 10568–10573, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. P. P. Repoussis, C. D. Tarantilis, O. Braysy, and G. Ioannou, “A hybrid evolution strategy for the open vehicle routing problem,” Computers and Operations Research, vol. 37, no. 3, pp. 443–455, 2010. View at Publisher · View at Google Scholar
  10. R. Russell, W.-C. Chiang, and D. Zepeda, “Integrating multi-product production and distribution in newspaper logistics,” Computers & Operations Research, vol. 35, no. 5, pp. 1576–1588, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. C. D. Tarantilis and C. T. Kiranoudis, “Distribution of fresh meat,” Journal of Food Engineering, vol. 51, no. 1, pp. 85–91, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Atefi, M. Salari, L. C. Coelho, and J. Renaud, “The open vehicle routing problem with decoupling points,” European Journal of Operational Research, vol. 265, no. 1, pp. 316–327, 2018. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. C. Erbao, L. Mingyong, and Y. Hongming, “Open vehicle routing problem with demand uncertainty and its robust strategies,” Expert Systems with Applications, vol. 41, no. 7, pp. 3569–3575, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. K. Fleszar, I. H. Osman, and K. S. Hindi, “A variable neighbourhood search algorithm for the open vehicle routing problem,” European Journal of Operational Research, vol. 195, no. 3, pp. 803–809, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. E. Demir, T. Bektaş, and G. Laporte, “A comparative analysis of several vehicle emission models for road freight transportation,” Transportation Research Part D: Transport and Environment, vol. 16, no. 5, pp. 347–357, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. T. Bektaş and G. Laporte, “The pollution-routing problem,” Transportation Research Part B: Methodological, vol. 45, no. 8, pp. 1232–1250, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. Ç. Koç, T. Bektaş, O. Jabali, and G. Laporte, “The fleet size and mix pollution-routing problem,” Transportation Research Part B: Methodological, vol. 70, pp. 239–254, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. E. Demir, T. Bektaş, and G. Laporte, “A review of recent research on green road freight transportation,” European Journal of Operational Research, vol. 237, no. 3, pp. 775–793, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Barth, T. Younglove, and G. Scora, “Development of a Heavy-duty Diesel Modal Emissions and Fuel Consumption Model,” Tech. Rep. UCB-ITSPRR-2005-1, California PATH Program, Institute of transportation Studies, University of California at Berkeley, California, Calif, USA, 2005. View at Google Scholar
  20. M. Scora and G. Barth, “Comprehensive Modal Emission Model (CMEM), Version 3.01, User Guide,” Tech. Rep., 2006, http://www.cert.ucr.edu/cmem/docs/CMEM_User_Guide_v3.01d.pdf. View at Google Scholar
  21. M. Barth and K. Boriboonsomsin, “Real-world carbon dioxide impacts of traffic congestion,” Transportation Research Record, no. 2058, pp. 163–171, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. Man, Trucks in Distribution Transport, http://www.mantruckandbus.co.uk/en/trucks/start_trucks.html.
  23. E. Demir, T. Bektas, and G. Laporte, “An adaptive large neighborhood search heuristic for the Pollution-Routing PROblem,” European Journal of Operational Research, vol. 223, no. 2, pp. 346–359, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  24. G. Zhang, H. Rong, F. Neri, and M. J. Pérez-Jiménez, “An optimization spiking neural P system for approximately solving combinatorial optimization problems,” International Journal of Neural Systems, vol. 24, no. 5, pp. 1–16, 2014. View at Google Scholar
  25. G. Zhang, H. Rong, J. Cheng, and Y. Qin, “A population-membrane-system-inspired evolutionary algorithm for distribution network reconfiguration,” Chinese Journal of Electronics, vol. 23, no. 3, pp. 437–441, 2014. View at Google Scholar
  26. X. Zhang, J. Li, and L. Zhang, “A multi-objective membrane algorithm guided by the skin membrane,” Natural Computing, vol. 15, no. 4, pp. 597–610, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  27. J. Xu, “Probe machine,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 7, pp. 1405–1416, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. G. Zhang, J. Cheng, M. Gheorghe, and Q. Meng, “A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems,” Applied Soft Computing, vol. 13, no. 3, pp. 1528–1542, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. G. Zhang, M. J. Prez-Jimnez, and M. Gheorghe, Real-Life Applications with Membrane Computing, Springer, Berlin, Germany, 2017.