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Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 43

Vehicle Routing under Complex Access-to-Energy Constraints

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DOI: http://dx.doi.org/10.15439/2025F7738

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 577–586 ()

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Abstract. Photovoltaic platforms enable a single agent to simultaneously act as both a producer and a consumer of power, facilitating self-consumption strategies. This trend aligns with the goal of reducing CO2 emissions and is poised to significantly transform the structure of energy markets. It also introduces specific challenges---both tactical (e.g., pricing) and operational (e.g., routing, scheduling)---related to synchronizing energy production with consumption. In this work, we address the problem of efficiently routing a fleet of electric autonomous vehicles (EAVs), using energy that is either produced by a photovoltaic platform or purchased from the general power grid. We propose an exact Mixed-Integer Linear Programming (MILP) formulation of the problem, along with a heuristic approach that approximates the power production component of the model using surrogate representations.

References

  1. C. Artigues, E. Hébrard, A. Quilliot, H. Toussaint, Models and algorithms for natural disaster evacuation problems, Proc. 2019 FEDCSIS WCO Conf., p 143-146, 2019. doi.org/10.15439/2019F90
  2. Bendali F., Mole Kamga E., Mailfert J., Quilliot A., Toussaint H.: Synchronizing Energy Production and Vehicle Routing. RAIRO-O.R, 55 (4),pp. 2141-2163. (2021). https://doi.org/10.1051/ro/2021093.
  3. F. Bendali, J. Mailfert, E. Mole-Kamga, A. Quilliot, H. Toussaint, Pipelining dynamic programming process in order to synchronize energy production and consumption, Proc. 2020 FEDCSIS WCO Conf., p 303-306, 2020. doi.org/10.15439/978-83-955416-7-4.
  4. Drexl M.: Synchronization in vehicle routing–a survey; Transportation Science 46, pp. 297-316, (2012). https://dx.doi.org/10.1287/trsc.1110.0400
  5. Erdelic T., Caric T., Lalla-Ruiz E.: A Survey on the Electric Vehicle Routing Problem.Journal of Advanced Transportation (2019). https://doi.org/10.1155/2019/5075671.
  6. Erdogan S., Miller-Hooks E.. A green vehicle routing problem. Transportation Research Part E , 109, p 100–114, (2012). https://doi.org/10.1016/j.tre.2011.08.001
  7. S. Fidanova, O. Roeva, M. Ganzha, Ant colony optimization algorithm for fuzzy transport modelling, Proc. 2020 FEDCSIS WCO Conference, p 237-240, 2020. doi.org/10.15439/978-83-955416-7-4
  8. Franceschetti A., Demir E., Honhon D., Van Woensel T., Laporte G., and Stobbe M.: A metaheuristic for the time dependent pollution-routing problem. EJOR, 259 (3) :972 – 991, (2017). https://doi.org/10.1016/j.ejor.2016.11.026
  9. K. Gmyrek, M. Antkiewicz, P. Myszkowski: Genetic Algorithm for Planning and Scheduling Problem – StarCraft II Build Order case study; Proceedings of the 18th Conference on Computer Science and Intelligence Systems ACSIS, Vol. 35, pages 131–140 (2023). http://dx.doi.org/10.15439/2023F6015.
  10. Hung Y. F., Chien K. L.: A multi-class multi-level capacitated lot sizing model;Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(11), pages 1309-1318, (2000). DOI:10.1057/palgrave.jors.2601026.
  11. Irani S, Pruhs K.: Algorithmic problems in power management. ACM SIGACT News 36-2, p 63-76 (2005). DOI:10.1145/1067309.1067324.
  12. Koç C., Jabali O., Mendoza J., Laporte G.. The electric vehicle routing problem with shared charging stations. ITOR, 26, (2018). DOI:10.1111/itor.12620
  13. Kuo Y.. Simulated annealing to minimize fuel consumption for the time-dependent VRP. Comp. Indust. Eng., 59 :157–165, (2010). https://doi.org/10.1016/j.cie.2010.03.012
  14. Lajunen A.: Energy consumption and cost analysis of electric city buses. Transportation Research Part C, 38 :1–15, (2014). https://doi.org/10.1016/j.trc.2013.10.008
  15. Luthander R., Widen J., Nilsson D., Palm J.: Photovoltaic self-consumption in buildings. Applied Energy 142, p 80-94, (2015). https://doi.org/10.1016/j.apenergy.2014.12.028
  16. Macrina G., Di Puglia L., Guerriero F.: The Green-Vehicle Routing Problem: A Survey. Model. and Optim. in Logistics p 1-26 (2020). https://doi.org/10.1007/978-3-030-45308-4 1.
  17. Raylan M., Matos S., Frota Y., and Satoru Ochi L.: Green vehicle routing and scheduling problem with split delivery. Electronic Notes in Discrete Mathematics, 69 :13 – 20, 2018. https://doi.org/10.1016/j.endm.2018.07.003
  18. Sachenbacher M., Leucker M., Artmeier A., Haselmayr J.: Efficient energy routing for electric vehicles. Proc AAAI 2011 Int. Conf, (2011). https://doi.org/10.1609/aaai.v25i1.7803
  19. Schneider M., Stenger A., Goeke D.: The electric vehicle-routing problem with time windows and recharging stations. Transportation Science 48, p 500-520, (2014). DOI:10.1287/trsc.2013.0490
  20. K. Stoilova, T. Stoilov, Bi-level optimization application for urban traffic management, Proc. 2020 FEDCSIS WCO Conf., p 327-336, 2020. doi.org/10.15439/978-83-949419-5-6.
  21. Verma A.: Electric vehicle routing problem with time windows, recharging stations and battery swapping stations. Euro Journal of Transportation Logistics 7, p 415-451, (2018).