Skip to main content

Advertisement

Log in

Reducing the carbon footprint in a vehicle routing problem by pooling resources from different companies

  • Published:
NETNOMICS: Economic Research and Electronic Networking Aims and scope Submit manuscript

Abstract

In this study, we propose that pooling resources would reduce both the carbon footprint and economic costsin the vehicle routing problem with time windows. A mathematical formulation for the vehicle routing problem considering the carbon footprint as a constraint is proposed. The model is approached with the scatter search metaheuristic and analyzed from the perspective of game theory to evaluate the stability of the coalition after pooling. We define a theoretical case for four suppliers on an instance partition from Solomon’s library using several scenarios from individual participation to a full coalition. For each of these scenarios, we realize a sweep of the objective space. The results show that the more resources are shared, the greater the benefit. The best savings and contributions are achieved by operating in complete cooperation. These savings were distributed as fairly as possible to maintain a stable coalition using the Shapley value.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Ballot, E., & Fontane, F. (2010). Reducing transportation CO2 emissions through pooling of supply networks: perspectives from a case study in French retail chains. Production Planning & Control, 21(6), 640–650.

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Belfiore, P., & Yoshida Yoshizaki, H.T. (2009). Scatter search for a real-life heterogeneous fleet vehicle routing problem with time windows and split deliveries in Brazil. European Journal of Operational Research, 199(3), 750–758.

    Article  Google Scholar 

  4. Bogner, J., Pipatti, R., Hashimoto, S., Diaz, C., Mareckova, K., Diaz, L., & et al. (2008). Mitigation of global greenhouse gas emissions from waste: conclusions and strategies from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. Working Group III (Mitigation). Waste Management & Research, 26(1), 11–32. doi:10.1177/0734242X07088433.

    Article  Google Scholar 

  5. Ćirović, G., Pamučar, D., & Božanić, D. (2014). Green logistic vehicle routing problem: routing light delivery vehicles in urban areas using a neuro-fuzzy model. Expert Systems with Applications, 41(9), 4245–4258. doi:10.1016/j.eswa.2014.01.005.

    Article  Google Scholar 

  6. Corberan, A., Fernandez, E., Laguna, M., & Marti, R. (2002). Heuristic solutions to the problem of routing school buses with multiple objectives. Journal of the Operational Research Society, 53(4), 427–435.

    Article  Google Scholar 

  7. Demir, E., Bektaş, T., & Laporte, G. (2014). The bi-objective pollution-routing problem. European Journal of Operational Research, 232(3), 464–478. doi:10.1016/j.ejor.2013.08.002.

    Article  Google Scholar 

  8. EcoTransIT World: Ecological Transport Information Tool for Worldwide Transports. (2010). http://www.ecotransit.org/download/EcoTransIT_World_Methodology_Data_100521.pdf. Accessed 17 Apr 2015.

  9. Erdogan, 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 

  10. Figliozzi, M. (2010). Vehicle routing problem for emissions minimization. Transportation Research Record: Journal of the Transportation Research Board, 2197, 1–7.

    Article  Google Scholar 

  11. Frota Neto, J.Q., Bloemhof-Ruwaard, J.M., Van Nunen, J., & Van Heck, E. (2008). Designing and evaluating sustainable logistics networks. International Journal of Production Economics, 111(2), 195–208.

    Article  Google Scholar 

  12. Georgiadis, P., & Vlachos, D. (2004). The effect of environmental parameters on product recovery. European Journal of Operational Research, 157(2), 449–464.

    Article  Google Scholar 

  13. World Business Council for Sustainable Development, World Resources Institute. (2008). The greenhouse gas protocol. A corporate accounting and reporting standard. Geneva, Switzerland: World Business Council for Sustainable Development; Washington, DC: World Resources Institute.

  14. Glover, F. (1998). A template for scatter search and path relinking. Artificial Evolution: Lecture Notes in Computer Science, 1363, 1–51.

    Article  Google Scholar 

  15. Gore, A., Guggenheim, D., David, L., Bender, L., Burns, S.Z., Skoll, J., & et al. (2006). An inconvenient truth. Paramount Pictures.

  16. Govindan, K., Kaliyan, M., Kannan, D., & Haq, A.N. (2014). Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. International Journal of Production Economics, 147(Part B), 555–568. doi:10.1016/j.ijpe.2013.08.018.

    Article  Google Scholar 

  17. Heugues, M. (2012). International environmental cooperation: a new eye on the greenhouse gas emissions’ control. Annals of Operations Research, 220, 239–262. doi:10.1007/s10479-012-1156-8.

    Article  Google Scholar 

  18. Jemai, J., Zekri, M., & Mellouli, K. (2012). An NSGA-II algorithm for the green vehicle routing problem. In J.-K. Hao, & M. Middendorf (Eds.), Evolutionary computation in combinatorial optimization (pp. 37–48). Berlin: Springer.

  19. Johnson, K.C. (2010). Circumventing the weight-versus-footprint tradeoffs in vehicle fuel economy regulation. Transportation Research Part D: Transport and Environment, 15(8), 503– 506.

    Article  Google Scholar 

  20. Laguna, M. (2009). Scatter search and path relinking. Evolutionary Multi-Criterion Optimization: Lecture Notes in Computer Science, 5467, 1.

    Article  Google Scholar 

  21. Lin, C., Choy, K.L., Ho, G.T.S., Chung, S.H., & Lam, H.Y. (2014). Survey of green vehicle routing problem: past and future trends. Expert Systems with Applications, 41(4, Part 1), 1118–1138. doi:10.1016/j.eswa.2013.07.107.

    Article  Google Scholar 

  22. Lin, C., Choy, K.L., Ho, G.T.S., & Ng, T.W. (2014). A genetic algorithm-based optimization model for supporting green transportation operations. Expert Systems with Applications, 41(7), 3284–3296. doi:10.1016/j.eswa.2013.11.032.

    Article  Google Scholar 

  23. Marti, R., Laguna, M., & Glover, F. (2003). Principles of scatter search: basic design and advanced strategies. European Journal of Operational Research, 169(2), 359–372.

    Article  Google Scholar 

  24. Nagurney, A., & Nagurney, L.S. (2010). Sustainable supply chain network design: a multicriteria perspective. International Journal of Sustainable Engineering, 3(3), 189–197. doi:10.1080/19397038.2010.491562.

    Article  Google Scholar 

  25. Nash, J. (1951). Non-cooperative games. The Annals of Mathematics, 54(2), 286–295.

    Article  Google Scholar 

  26. Pan, S., Ballot, E., & Fontane, F. (2013). The reduction of greenhouse gas emissions from freight transport by pooling supply chains. International Journal of Production Economics, 143(1), 86–94. doi:10.1016/j.ijpe.2010.10.023.

    Article  Google Scholar 

  27. Pradenas, L., Oportus, B., & Parada, V. (2013). Mitigation of Greenhouse gas emissions in vehicle routing problems with backhauling. Expert Systems With Applications, 40(8), 2985–2991.

    Article  Google Scholar 

  28. Rief, D., & van Dinther, C. (2010). Negotiation for cooperation in logistics networks: an experimental study. Group Decision and Negotiation, 19(3), 211–226. doi:10.1007/s10726-010-9193-7.

  29. Roth, A., & Kaberger, T. (2002). Making transport systems sustainable. Journal of Cleaner Production, 10(4), 361–371.

    Article  Google Scholar 

  30. Russell, R.A., & Chiang, W.C. (2006). Scatter search for the vehicle routing problem with time windows. European Journal of Operational Research, 169(2), 602–622.

    Article  Google Scholar 

  31. Shapley, L., & Scarf, H. (1974). On cores and indivisibility. Journal of Mathematical Economics, 1(1), 23–37.

    Article  Google Scholar 

  32. Solomon, M.M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35(2), 254–265.

    Article  Google Scholar 

  33. Solomon, M.M. (2005). Solomon VRPTW Benchmark Problems [En ligne]. - 24 March 2005. - 20 August 2011. - http://w.cba.neu.edu/~msolomon/problems.htm.

  34. Sundarakani, B., de Souza, R., Goh, M., Wagner, S.M., & Manikandan, S. (2010). Modeling carbon footprints across the supply chain. International Journal of Production Economics, 128(1), 43–50.

    Article  Google Scholar 

  35. Talbi, E.-G. (2009). Metaheuristics: from design to implementation. New York: Wiley.

    Book  Google Scholar 

  36. Toth, P., & Vigo, D. (2014). Vehicle routing: problems, methods, and applications, 2nd edn. Philadelphia: SIAM - Society for Industrial and Applied Mathematics.

    Book  Google Scholar 

  37. Wang, X., & Kopfer, H. (2014). Collaborative transportation planning of less-than-truckload freight. OR Spectrum, 36(2), 357–380. doi:10.1007/s00291-013-0331-x.

    Article  Google Scholar 

  38. Zecca, A., & Chiari, L. (2010). Fossil-fuel constraints on global warming. Energy Policy, 38(1), 1–3.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lorena Pradenas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sanchez, M., Pradenas, L., Deschamps, JC. et al. Reducing the carbon footprint in a vehicle routing problem by pooling resources from different companies. Netnomics 17, 29–45 (2016). https://doi.org/10.1007/s11066-015-9099-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11066-015-9099-2

Keywords

Navigation