Skip to main content

A meta-heuristic for capacitated green vehicle routing problem

Abstract

The capacitated green vehicle routing problem is considered in this paper as a new variant of the vehicle routing problem. In this problem, alternative fuel-powered vehicles (AFVs) are used for distributing products. AFVs are assumed to have low fuel tank capacity. Therefore, during their distribution process, AFVs are required to visit alternative fuel stations (AFSs) for refueling. The design of the vehicle routes for AFVs becomes difficult due to the limited loading capacity, the low fuel tank capacity and the scarce availability of AFSs. Two solution methods, the two-phase heuristic algorithm and the meta-heuristic based on ant colony system, are proposed to solve the problem. The numerical experiment is performed on the randomly generated problem instances to evaluate the performance of the proposed algorithms.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Abdulkader, M. M., Gajpal, Y., & ElMekkawy, T. Y. (2015). Hybridized ant colony algorithm for the multi compartment vehicle routing problem. Applied Soft Computing, 37, 196–203.

    Article  Google Scholar 

  2. Barán, B., & Schaerer, M. (2003). A multiobjective ant colony system for vehicle routing problem with time windows. In Paper presented at the applied informatics.

  3. Bard, J. F., Huang, L., Dror, M., & Jaillet, P. (1998). A branch and cut algorithm for the VRP with satellite facilities. IIE Transactions, 30(9), 821–834.

    Google Scholar 

  4. Bektaş, T., & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45(8), 1232–1250.

    Article  Google Scholar 

  5. Bell, J. E., & McMullen, P. R. (2004). Ant colony optimization techniques for the vehicle routing problem. Advanced Engineering Informatics, 18(1), 41–48.

    Article  Google Scholar 

  6. Colorni, A., Dorigo, M., & Maniezzo, V. (1991). Distributed optimization by ant colonies. In Paper presented at the proceedings of the first European conference on artificial life.

  7. Crevier, B., Cordeau, J.-F., & Laporte, G. (2007). The multi-depot vehicle routing problem with inter-depot routes. European Journal of Operational Research, 176(2), 756–773.

    Article  Google Scholar 

  8. Demir, E., Bektaş, T., & Laporte, G. (2012). An adaptive large neighborhood search heuristic for the pollution-routing problem. European Journal of Operational Research, 223(2), 346–359.

    Article  Google Scholar 

  9. Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Bradford, Cambirdge: Massachusetts Institute of Technology, MIT Press.

    Google Scholar 

  10. Erdoğan, S., & Miller-Hooks, E. (2012). A green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 48(1), 100–114.

    Article  Google Scholar 

  11. Felipe, Á., Ortuño, M. T., Righini, G., & Tirado, G. (2014). A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transportation Research Part E: Logistics and Transportation Review, 71, 111–128.

    Article  Google Scholar 

  12. Fraer, R., Dinh, H., Chandler, K., & Buchholz, B. (2005). Operating experience and teardown analysis for engines operated on biodiesel blends (B20). SAE Technical Paper, 2, 005-001.

    Google Scholar 

  13. Franceschetti, A., Honhon, D., Van Woensel, T., Bektaş, T., & Laporte, G. (2013). The time-dependent pollution-routing problem. Transportation Research Part B: Methodological, 56, 265–293.

    Article  Google Scholar 

  14. Gajpal, Y., & Abad, P. (2009a). An ant colony system (ACS) for vehicle routing problem with simultaneous delivery and pickup. Computers and Operations Research, 36(12), 3215–3223.

    Article  Google Scholar 

  15. Gajpal, Y., & Abad, P. (2009b). Multi-ant colony system (MACS) for a vehicle routing problem with backhauls. European Journal of Operational Research, 196(1), 102–117.

    Article  Google Scholar 

  16. Gutin, G., Yeo, A., & Zverovich, A. (2002). Traveling salesman should not be greedy: Domination analysis of greedy-type heuristics for the TSP. Discrete Applied Mathematics, 117(1), 81–86.

    Article  Google Scholar 

  17. IEA (2014). CO\(_{2}\) emissions from fuel combustion highlights 2014. Paris: International Energy Agency, pp 10.

  18. IEA (2015). CO\(_{2}\) emissions from fuel combustion highlights 2015. Paris: International Energy Agency, pp 7–11.

  19. Kek, A. G., Cheu, R. L., & Meng, Q. (2008). Distance-constrained capacitated vehicle routing problems with flexible assignment of start and end depots. Mathematical and Computer Modelling, 47(1), 140–152.

    Article  Google Scholar 

  20. Koç, Ç., Bektaş, T., Jabali, O., & Laporte, G. (2014). The fleet size and mix pollution-routing problem. Transportation Research Part B: Methodological, 70, 239–254.

    Article  Google Scholar 

  21. Kuo, Y. (2010). Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Computers and Industrial Engineering, 59(1), 157–165.

    Article  Google Scholar 

  22. Lin, C., Choy, K. L., Ho, G. T., Chung, S., & Lam, H. (2014). Survey of green vehicle routing problem: Past and future trends. Expert Systems with Applications, 41(4), 1118–1138.

    Article  Google Scholar 

  23. Mehrez, A., & Stern, H. I. (1985). Optimal refueling strategies for a mixed—Vehicle fleet. Naval Research Logistics Quarterly, 32(2), 315–328.

    Article  Google Scholar 

  24. Mehrez, A., Stern, H. I., & Ronen, D. (1983). Vehicle fleet refueling strategies to maximize operational range. Naval Research Logistics Quarterly, 30(2), 319–342.

    Article  Google Scholar 

  25. Sbihi, A., & Eglese, R. W. (2007). Combinatorial optimization and green logistics. 4OR, 5(2), 99–116.

    Article  Google Scholar 

  26. Schneider, M., Stenger, A., & Goeke, D. (2014). The electric vehicle-routing problem with time windows and recharging stations. Transportation Science, 48(4), 500–520.

    Article  Google Scholar 

  27. Stützle, T., & Hoos, H. H. (2000). MAX–MIN ant system. Future Generation Computer Systems, 16(8), 889–914.

    Article  Google Scholar 

  28. Tarantilis, C. D., Zachariadis, E. E., & Kiranoudis, C. T. (2008). A hybrid guided local search for the vehicle-routing problem with intermediate replenishment facilities. INFORMS Journal on Computing, 20(1), 154–168.

    Article  Google Scholar 

  29. Xiao, Y., Zhao, Q., Kaku, I., & Xu, Y. (2012). Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers and Operations Research, 39(7), 1419–1431.

    Article  Google Scholar 

Download references

Acknowledgements

This research is partially supported by University Start-up Research Grant from Asper School of Business, University of Manitoba, Canada.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yuvraj Gajpal.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, S., Gajpal, Y. & Appadoo, S.S. A meta-heuristic for capacitated green vehicle routing problem. Ann Oper Res 269, 753–771 (2018). https://doi.org/10.1007/s10479-017-2567-3

Download citation

Keywords

  • Vehicle routing
  • Alternative fuel-powered vehicle operations
  • Fuel tank capacity limitation
  • Capacitated vehicle