Skip to main content

Advertisement

SpringerLink
Log in

A hybrid metaheuristic algorithm to solve the electric vehicle routing problem with battery recharging stations for sustainable environmental and energy optimization

  • Original Paper
  • Published:
Energy Systems Aims and scope Submit manuscript

Abstract

Air pollution due to the usage of combustion vehicles, the increase in oil costs, and its exhaustion make it necessary to replace traditional vehicles with electrically powered cars. Zero-emission vehicles and Electric Vehicles (EVs) are critical technologies to attain deep reductions in greenhouse gases from transportation. Researchers are becoming progressively concerned about the destruction it is producing to the environment, and EVs are identified to play a part in equalizing the balance. In the Capacitated Electric Vehicle Routing Problem (CE-VRP), the vehicles have a limited delivery capacity and rely completely on their limited battery capacity. Besides, all vehicle has a limited driving range and must recharge their battery at some customer’s locations. In this paper, a “Hybrid Variable Neighbourhood Search (HVNS)” is proposed to solve the CE-VRP. The results provide indications on the ideal size of the fleet, and on the total distance traveled while minimizing the associated costs. The computational results on the reference cases confirm that the HVNS can detect good quality solutions compared to previous work, an increase in total associated cost for the majority of the instances given, this proves that the HVNS algorithm is suitable to solve the CE-VRP with a recharging station.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

source: Euchi et al. [35]

Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Moons, S., Braekers, K., Ramaekers, K., Caris, A., Arda, Y.: The value of integrating order picking and vehicle routing decisions in a B2C e-commerce environment. Int. J. Prod. Res. 57(20), 6405–6423 (2019)

    Article  Google Scholar 

  2. Omri, A., Euchi, J., Hasaballah, A.H., Al-Tit, A.: Determinants of environmental sustainability: evidence from Saudi Arabia. Sci. Total Environ. 657, 1592–1601 (2019)

    Article  Google Scholar 

  3. Euchi, J., Sadok, A.: Hybrid genetic-sweep algorithm to solve the vehicle routing problem with drones. Phys. Commun. 44, 101236 (2021)

    Article  Google Scholar 

  4. Kliewer, N., Ehmke, J.F., Mattfeld, D.C.: Computational mobility, transportation, and logistics. Bus. Inf. Syst. Eng. 59, 133–134 (2017)

    Article  Google Scholar 

  5. Cleophas, C., Ehmke, J.F.: When are deliveries profitable? Bus Inf Syst Eng 6, 153–163 (2014)

    Article  Google Scholar 

  6. Hiermann, G., Puchinger, J., Ropke, S., Hartl, R.F.: The electric fleet size and mix vehicle routing problem with time windows and recharging stations. Eur. J. Oper. Res. 252(3), 995–1018 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  7. Clarke, G., Wright, J.: Scheduling of vehicles from a central depot to a number of delivery points. Oper. Res. 12(4), 568–581 (1964)

    Article  Google Scholar 

  8. Braekers, K., Ramaekers, K., Van Nieuwenhuyse, I.: The vehicle routing problem: state of the art classification and review. Comput. Ind. Eng. 99, 300–313 (2016)

    Article  Google Scholar 

  9. Pollaris, H., Janssens, G.K., Braekers, K., Caris, A., Limbourg, S.: The impact of axle weight constraints on the deployment of a mixed-fleet in vehicle routing decisions. IJTTE Int. J. Traffic Transport Eng. 11(3), 392–410 (2021)

    Google Scholar 

  10. Tasan, A.S., Gen, M.: A genetic algorithm-based approach to vehicle routing problem with simultaneous pick-up and deliveries. Comput. Ind. Eng. 62(3), 755–761 (2012)

    Article  Google Scholar 

  11. Euchi, J.: Complex vehicle transport problems: taxonomy, new variants, challenges, and solution methodology. Int. J. Logist. Econ. Global. 6(4), 332–355 (2017)

    Google Scholar 

  12. Zhang, S., Chen, M., Zhang, W.: A novel location-routing problem in electric vehicle transportation with stochastic demands. J. Clean. Prod. 221, 567–581 (2019)

    Article  Google Scholar 

  13. Euchi, J.: The vehicle routing problem with private fleet and multiple common carriers: solution with hybrid metaheuristic algorithm. Veh. Commun. 9, 97–108 (2017)

    Google Scholar 

  14. Euchi, J.: Genetic scatter search algorithm to solve the one-commodity pickup and delivery vehicle routing problem. J. Model. Manage. 12(1), 2–18 (2017)

    Article  Google Scholar 

  15. Vidal, T., Laporte, G., Matl, P.: A concise guide to existing and emerging vehicle routing problem variants. Eur. J. Oper. Res. 286, 401–416 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  16. Marques, A., Soares, R., Santos, M.J., Amorim, P.: Integrated planning of inbound and outbound logistics with a rich vehicle routing problem with backhauls. Omega 92, 102172 (2020)

    Article  Google Scholar 

  17. Moghdani, R., Salimifard, K., Demir, E., Benyettou, A.: The green vehicle routing problem: a systematic literature review. J. Clean. Prod. 279, 123691 (2021)

    Article  Google Scholar 

  18. Asghari, M., Al-e-hashem Mirzapour, J.M.: Green vehicle routing problem: a state-of-the-art review. Int. J. Prod. Econ. 231, 107899 (2021)

    Article  Google Scholar 

  19. Erdoğan, S., Miller-Hooks, E.: A green vehicle routing problem. Transport. Res. E Logist. Transport. Rev. 48(1), 100–114 (2012)

    Article  Google Scholar 

  20. Hooshmand, F., MirHassani, S.A.: Time dependent green VRP with alternative fuel powered vehicles. Energy Syst. 10(3), 721–756 (2019)

    Article  Google Scholar 

  21. Conrad, R.G., Figliozzi, M.A.: The recharging vehicle routing problem. In: Proc. of the 61st Annual Conference and Expo of the Institute of Industrial Engineers (2011)

  22. Schneider, M., Stenger, A., Goeke, D.: The electric vehicle-routing problem with time windows and recharging stations. Transport. Sci. 48(4), 500–520 (2014)

    Article  Google Scholar 

  23. Yang, J., Sun, H.: Battery swap station location-routing problem with capacitated electric vehicles. Comput. Oper. Res. 55, 217–232 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  24. Bruglieri, M., Pezzella, F., Pisacane, O., Suraci, S.: A variable neighborhood search branching for the electric vehicle routing problem with time windows. Electron. Notes Discrete Math. 47, 221–228 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  25. Zhen, L., Xu, Z., Ma, C., Xiao, L.: Hybrid electric vehicle routing problem with mode selection. Int. J. Prod. Res. 58(2), 562–576 (2020)

    Article  Google Scholar 

  26. Yao, E., Liu, T., Lu, T., Yang, Y.: Optimization of electric vehicle scheduling with multiple vehicle types in public transport. Sustain. Cities Soc. 52, 101862 (2020)

    Article  Google Scholar 

  27. Trachanatzi, D., Rigakis, M., Marinaki, M., Marinakis, Y.: A teaching–learning-based optimization algorithm for the environmental prize-collecting vehicle routing problem. Energy Syst. 1–28 (2021)

  28. Euchi, J., Kallel, A.: Internalization of external congestion and CO2 emissions costs related to road transport: the case of Tunisia. Renew. Sustain. Energy Rev. 142, 110858 (2021)

    Article  Google Scholar 

  29. Sassi, O., Cherif-Khettaf, W.R., Oulamara, A.: Multi-start iterated local search for the mixed fleet vehicle routing problem with heterogeneous electric vehicles. In: Ochoa, G., Chicano, F. (eds.) Evolutionary computation in combinatorial optimization, vol. 9026, pp. 138–149. Springer International Publishing, New York (2015)

    Chapter  Google Scholar 

  30. Juan, A.A., Goentzel, J., Bektaş, T.: Routing fleets with multiple driving ranges: is it possible to use greener fleet configurations? Appl. Soft Comput. 21, 84–94 (2014)

    Article  Google Scholar 

  31. Frade, I., Ribeiro, A., Gonçalves, G., Antunes, A.P.: Optimal location of charging stations for electric vehicles in a neighborhood in Lisbon, Portugal. Transport. Res. Rec. J. Transport. Res. Board 2252(1), 91–98 (2011)

    Article  Google Scholar 

  32. Andrews, M., Dogru, M.K., Hobby, J. D., Jin, Y., Tucci, G.H.: Modeling and optimization for electric vehicle charging infrastructure. In: IEEE Innovative Smart Grid Technologies Conference (2013)

  33. Villegas, J.G., Gueret, C., Mendoza, J.E., Montoya, A.: The technician routing and scheduling problem with conventional and electric vehicle. Working paper. https://hal.archives-ouvertes.fr/hal-01813887 (2018)

  34. Folkestad, T., Brurberg, K.G., Nordhuus, K.M., Tveiten, C.K., Guttormsen, A.B., Os, I., Beitland, S.: Acute kidney injury in burn patients admitted to the intensive care unit: a systematic review and meta-analysis. Crit. Care 24(1), 2 (2020)

    Article  Google Scholar 

  35. Euchi, J., Yassine, A., Chabchoub, H.: On the performance of artificial ant colony to solve the dynamic vehicle routing problem. In: 4th International Conference on Logistics, Hammamet, pp. 38–43 (2011)

  36. Solomon, M. M.: Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper. Res. 35(2), 254–265 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  37. Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Economics, Quantitative Methods and Informatics, Higher Institute of Business Administration, University of Gafsa, Gafsa, Tunisia

    Jalel Euchi

  2. OLID Laboratory, Higher Institute of Industrial Management of Sfax, University of Sfax, Sfax, Tunisia

    Jalel Euchi

  3. Normandie Université, UNIHAVRE, LMAH, FR CNRS 3335, ISCN, ISEL, 76600, Le Havre, France

    Adnan Yassine

Authors
  1. Jalel Euchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adnan Yassine
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jalel Euchi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Euchi, J., Yassine, A. A hybrid metaheuristic algorithm to solve the electric vehicle routing problem with battery recharging stations for sustainable environmental and energy optimization. Energy Syst 14, 243–267 (2023). https://doi.org/10.1007/s12667-022-00501-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12667-022-00501-y

Keywords

Search

Navigation