Advertisement

Metaheuristics for Periodic Electric Vehicle Routing Problem

Conference paper
  • 202 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1162)

Abstract

This paper proposes two metaheuristics based on large neighbourhood search for the PEVRP (Periodic Electric Vehicle Routing Problem). In the PEVRP a set of customers have to be visited, one times, on a given planning horizon. A list of possible visiting dates is associated with each customer and a fixed fleet of vehicles is available every day of the planning horizon. Solving the problem requires assigning a visiting date to each customer and defining the routes of the vehicles in each day of the planning horizon, such that the EVs could be charged during their trips at the depot and in the available external charging stations. The objective of the PEVRP is to minimize the total cost of routing and charging over the time horizon. The first proposed metaheuristic is a Large Neighbourhood Search, whose choice of destroy/repair operators has been determined according to the experimental results obtained in previous research. The second method is an Adaptive Large Neighborhood Search, which could be described as a Large Neighborhood Search algorithm with an adaptive layer, where a set of three destroy operators and three repair operators compete to modify the current solution in each iteration of the algorithm. The results show that LNS is very competitive compared to ALNS for which the adaptive aspect has not made it more competitive than the LNS.

Keywords

Periodic vehicle routing Electric vehicle Charging station Large Neighborhood Search Adaptive Large Neighborhood Search 

References

  1. 1.
    Archetti, C., Fernández, E., Huerta-Muñoz, D.L.: A two-phase solution algorithm for the flexible periodic vehicle routing problem. Comput. Oper. Res. 99, 27–37 (2018)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Azi, N., Gendreau, M., Potvin, J.Y.: An adaptive large neighborhood search for a vehicle routing problem with multiple routes. Comput. Oper. Res. 41, 167–173 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Baldacci, R., Bartolini, E., Mingozzi, A., Valletta, A.: An exact algorithm for the period routing problem. Oper. Res. 59(1), 228–241 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chentli, H., Ouafi, R., Cherif-Khettaf, R.W.: A selective adaptive large neighborhood search heuristic for the profitable tour problem with simultaneous pickup and delivery services. RAIRO-Oper. Res. 52(4–5), 1295–1328 (2018)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Christofides, B.J.: The period routing problem. Networks 14, 237–256 (1984)CrossRefGoogle Scholar
  6. 6.
    Dayarian, I., Crainic, T., Gendreau, M., Rei, W.: An adaptive large-neighborhood search heuristic for a multi-period vehicle routing problem. Transp. Res. Part E 95, 95–123 (2016)CrossRefGoogle Scholar
  7. 7.
    Erdelić, T., Carić, T.: A survey on the electric vehicle routing problem: variants and solution approaches. J. Adv. Transp. 2019 (2019)CrossRefGoogle Scholar
  8. 8.
    Felipe, M., Ortuno, T., Righini, G., Tirado, G.: A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transp. Res. Part E: Logistics Transp. Rev. 71, 111–128 (2014)CrossRefGoogle Scholar
  9. 9.
    Francis, P.M., Smilowitz, K.R., Tzur, M.: The period vehicle routing problem and its extensions. In: Golden, B., Raghavan, S., Wasil, E. (eds.) The Vehicle Routing Problem: Latest Advances and New Challenges. Operations Research/Computer Science Interfaces, vol. 43, pp. 73–102. Springer, Boston (2008).  https://doi.org/10.1007/978-0-387-77778-8_4CrossRefzbMATHGoogle Scholar
  10. 10.
    Ghilas, V., Demir, E., Woensel, T.V.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows and scheduled lines. Comput. Oper. Res. 72, 12–30 (2016)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Goeke, D., Schneider, M.: Routing a mixed fleet of electric and conventional vehicles. Eur. J. Oper. Res. 245(1), 81–99 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Goeke, D.: Granular tabu search for the pickup and delivery problem with time windows and electric vehicles. Eur. J. Oper. Res. 278, 821–836 (2019)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Hiermann, G., Puchinger, J., Ropke, S., Hartl, R.: The electric fleet size and mix vehicle routing problem with time windows and recharging stations. Eur. J. Oper. Res. 252(3), 995–1018 (2016)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Jie, W., Yang, J., Zhang, M., Huang, Y.: The two-echelon capacitated electric vehicle routing problem with battery swapping stations: formulation and efficient methodology. Eur. J. Oper. Res. 272(3), 879–904 (2019)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Kouider, T.O., Ramdane Cherif-Khettaf, W., Oulamara, A.: Large neighborhood search for periodic electric vehicle routing problem. In: Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, pp. 169–178. INSTICC, SciTePress (2019)Google Scholar
  16. 16.
    Kouider, T.O., Ramdane-Cherif-Khettaf, W., Oulamara, A.: Constructive heuristics for periodic electric vehicle routing problem. In: Proceedings of the 7th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, pp. 264–271. INSTICC, SciTePress (2018)Google Scholar
  17. 17.
    Mancini, S.: A real-life multi depot multi period vehicle routing problem with a heterogeneous fleet: formulation and adaptive large neighborhood search based matheuristic. Transp. Res. Part C 70, 100–112 (2016)CrossRefGoogle Scholar
  18. 18.
    Pelletier, S., Jabali, O., Laporte, G.: Goods distribution with electric vehicles: review and research perspectives, working paper, CIRRELT (2014)Google Scholar
  19. 19.
    Pisinger, D., Røpke, S.: Large Neighborhood Search, 2 edn., pp. 399–420. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp. Sci. 40, 455–472 (2006)CrossRefGoogle Scholar
  21. 21.
    Sassi, O., Ramdane-Cherif-Khettaf, W., Oulamara, A.: Iterated tabu search for the mix fleet vehicle routing problem with heterogenous electric vehicles. Adv. Intell. Syst. Comput. 359, 57–68 (2015)zbMATHGoogle Scholar
  22. 22.
    Sassi, O., Cherif-Khettaf, W.R., Oulamara, A.: Multi-start Iterated Local Search for the Mixed Fleet Vehicle Routing Problem with Heterogenous Electric Vehicles. In: Ochoa, G., Chicano, F. (eds.) EvoCOP 2015. LNCS, vol. 9026, pp. 138–149. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-16468-7_12CrossRefGoogle Scholar
  23. 23.
    Schiffer, M., Walther, G.: The electric location routing problem with time windows and partial recharging. Eur. J. Oper. Res. 260, 995–1013 (2017)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Schiffer, M., Walther, G.: Strategic planning of electric logistics fleet networks: a robust location-routing approach. Omega 80, 31–42 (2018)CrossRefGoogle Scholar
  25. 25.
    Schneider, M., Stenger, A., Goeke, D.: The electric vehicle routing problem with time windows and recharging stations. Transp. Sci. 75, 500–520 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Université de Lorraine, Lorraine Research Laboratory in Computer Science and its Applications, LORIA (UMR 7503)Vandœuvre-les-NancyFrance

Personalised recommendations