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The Mixed-Fleet Vehicle Routing Problem with Low Emission Zones

Author

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  • Bruglieri, M.
  • Çatay, B.
  • Keskin, M.
  • Mancini, S.
  • Pisacane, O.

Abstract

Many cities initiated new measures to restrict the access of internal combustion engine vehicles (ICEVs) to urban centers because of increasing concerns in societies regarding carbon emissions and noise. A common restriction is referred to as low emission zone (LEZ) where ICEVs are either banned from entering or required to pay a daily toll to enter, whereas green vehicles such as electric vehicles (EVs) are exempt from any access restriction and toll payment. Therefore, delivery companies operating in LEZs face new challenges to determine their fleet configurations and make route plans. In this paper, we tackle this issue by adopting an optimization-based approach, which deals with routing a mixed fleet of vehicles that serves customers located inside and outside the LEZ. The fleet consists of ICEVs and EVs that are allowed to recharge en route. The objective is to minimize total operating costs that comprises the charging cost of EVs, fuel cost of ICEVs, and tolls paid by ICEVs that enter the LEZ. We present the mixed integer linear programming formulation of the problem and use it to solve small-size instances. For solving large-size instances, we develop an Adaptive Large Neighborhood Search method that benefits from new problem-specific mechanisms. An extensive experimental campaign is carried out on a set of benchmark instances derived from the literature and a case study based on real data is provided. Numerical results validate the effectiveness of the proposed method and provide managerial insights.

Suggested Citation

  • Bruglieri, M. & Çatay, B. & Keskin, M. & Mancini, S. & Pisacane, O., 2025. "The Mixed-Fleet Vehicle Routing Problem with Low Emission Zones," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 201(C).
    • Handle: RePEc:eee:transe:v:201:y:2025:i:c:s1366554525002716
    DOI: 10.1016/j.tre.2025.104230
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    References listed on IDEAS

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    1. Dönmez, Sercan & Koç, Çağrı & Altıparmak, Fulya, 2022. "The mixed fleet vehicle routing problem with partial recharging by multiple chargers: Mathematical model and adaptive large neighborhood search," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    2. Stefan Ropke & David Pisinger, 2006. "An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows," Transportation Science, INFORMS, vol. 40(4), pages 455-472, November.
    3. Malladi, Satya S. & Christensen, Jonas M. & Ramírez, David & Larsen, Allan & Pacino, Dario, 2022. "Stochastic fleet mix optimization: Evaluating electromobility in urban logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    4. Yu, Vincent F. & Anh, Pham Tuan & Baldacci, Roberto, 2023. "A robust optimization approach for the vehicle routing problem with cross-docking under demand uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    5. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert, 2019. "The electric vehicle routing problem with energy consumption uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 225-255.
    6. Ropke, Stefan & Pisinger, David, 2006. "A unified heuristic for a large class of Vehicle Routing Problems with Backhauls," European Journal of Operational Research, Elsevier, vol. 171(3), pages 750-775, June.
    7. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2012. "An adaptive large neighborhood search heuristic for the Pollution-Routing Problem," European Journal of Operational Research, Elsevier, vol. 223(2), pages 346-359.
    8. Li, Yuan & Chen, Haoxun & Prins, Christian, 2016. "Adaptive large neighborhood search for the pickup and delivery problem with time windows, profits, and reserved requests," European Journal of Operational Research, Elsevier, vol. 252(1), pages 27-38.
    9. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2014. "The bi-objective Pollution-Routing Problem," European Journal of Operational Research, Elsevier, vol. 232(3), pages 464-478.
    10. Goeke, Dominik & Schneider, Michael, 2015. "Routing a mixed fleet of electric and conventional vehicles," European Journal of Operational Research, Elsevier, vol. 245(1), pages 81-99.
    11. Michael Schneider & Andreas Stenger & Dominik Goeke, 2014. "The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations," Transportation Science, INFORMS, vol. 48(4), pages 500-520, November.
    12. Hiermann, Gerhard & Hartl, Richard F. & Puchinger, Jakob & Vidal, Thibaut, 2019. "Routing a mix of conventional, plug-in hybrid, and electric vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 235-248.
    13. Cui, Shaohua & Ma, Xiaolei & Zhang, Mingheng & Yu, Bin & Yao, Baozhen, 2022. "The parallel mobile charging service for free-floating shared electric vehicle clusters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    14. Goeke, D. & Schneider, M., 2015. "Routing a Mixed Fleet of Electric and Conventional Vehicles," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65939, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    15. Zandieh, Fatemeh & Farid Ghannadpour, Seyed & Mahdavi Mazdeh, Mohammad, 2023. "Integrated ground vehicle and drone routing with simultaneous surveillance coverage for evading intentional disruption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 178(C).
    16. Nan Ding & Manman Li & Jianming Hao, 2023. "A Two-Phase Approach to Routing a Mixed Fleet with Intermediate Depots," Mathematics, MDPI, vol. 11(8), pages 1-21, April.
    17. Lin, Na & Akkerman, Renzo & Kanellopoulos, Argyris & Hu, Xiangpei & Wang, Xuping & Ruan, Junhu, 2023. "Vehicle routing with heterogeneous service types: Optimizing post-harvest preprocessing operations for fruits and vegetables in short food supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    18. Herbert Kopfer & Benedikt Vornhusen, 2019. "Energy vehicle routing problem for differently sized and powered vehicles," Journal of Business Economics, Springer, vol. 89(7), pages 793-821, September.
    19. Guy Desaulniers & Fausto Errico & Stefan Irnich & Michael Schneider, 2016. "Exact Algorithms for Electric Vehicle-Routing Problems with Time Windows," Operations Research, INFORMS, vol. 64(6), pages 1388-1405, December.
    20. Wen, Liang & Çatay, Bülent & Eglese, Richard, 2014. "Finding a minimum cost path between a pair of nodes in a time-varying road network with a congestion charge," European Journal of Operational Research, Elsevier, vol. 236(3), pages 915-923.
    21. Hiermann, Gerhard & Puchinger, Jakob & Ropke, Stefan & Hartl, Richard F., 2016. "The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations," European Journal of Operational Research, Elsevier, vol. 252(3), pages 995-1018.
    22. Macrina, Giusy & Laporte, Gilbert & Guerriero, Francesca & Di Puglia Pugliese, Luigi, 2019. "An energy-efficient green-vehicle routing problem with mixed vehicle fleet, partial battery recharging and time windows," European Journal of Operational Research, Elsevier, vol. 276(3), pages 971-982.
    23. Jie, Wanchen & Yang, Jun & Zhang, Min & Huang, Yongxi, 2019. "The two-echelon capacitated electric vehicle routing problem with battery swapping stations: Formulation and efficient methodology," European Journal of Operational Research, Elsevier, vol. 272(3), pages 879-904.
    24. Montoya, Alejandro & Guéret, Christelle & Mendoza, Jorge E. & Villegas, Juan G., 2017. "The electric vehicle routing problem with nonlinear charging function," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 87-110.
    25. Schneider, M. & Stenger, A. & Goeke, D., 2014. "The Electric Vehicle Routing Problem with Time Windows and Recharging Stations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 62382, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    26. Amit Verma, 2018. "Electric vehicle routing problem with time windows, recharging stations and battery swapping stations," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(4), pages 415-451, December.
    27. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "Thirty years of heterogeneous vehicle routing," European Journal of Operational Research, Elsevier, vol. 249(1), pages 1-21.
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