IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v262y2017i2p647-659.html
   My bibliography  Save this article

The accuracy of carbon emission and fuel consumption computations in green vehicle routing

Author

Listed:
  • Turkensteen, Marcel

Abstract

Many recent green vehicle routing papers have presented mathematical models designed to minimize fuel consumption and the environmentally damaging carbon emissions in the routing decisions related to route choice, the load on the vehicle, and the speed. A popular model for computing such impact is the Comprehensive Modal Emissions Model (CMEM) that can compute fuel consumption and carbon emissions for a vehicle with a given speed and load. However, the CMEM requires that input parameters are specified for every second of a haul. To avoid this, computations are used in which the vehicle is assumed to travel at a fixed speed. This paper investigates the extent to which such fixed speed computations in the CMEM are suitable for actual driving conditions with fluctuating speeds. In our numerical experiments, we find that the CMEM results under fixed speeds are sometimes less than half of those computed under realistic driving conditions, depending on the type of conditions. This indicates that we cannot take for granted that fixed speed computations are sufficiently accurate to be used in green routing and validation of these computations is necessary.

Suggested Citation

  • Turkensteen, Marcel, 2017. "The accuracy of carbon emission and fuel consumption computations in green vehicle routing," European Journal of Operational Research, Elsevier, vol. 262(2), pages 647-659.
    • Handle: RePEc:eee:ejores:v:262:y:2017:i:2:p:647-659
    DOI: 10.1016/j.ejor.2017.04.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221717303387
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2017.04.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Harris, Irina & Naim, Mohamed & Palmer, Andrew & Potter, Andrew & Mumford, Christine, 2011. "Assessing the impact of cost optimization based on infrastructure modelling on CO2 emissions," International Journal of Production Economics, Elsevier, vol. 131(1), pages 313-321, May.
    2. Dekker, Rommert & Bloemhof, Jacqueline & Mallidis, Ioannis, 2012. "Operations Research for green logistics – An overview of aspects, issues, contributions and challenges," European Journal of Operational Research, Elsevier, vol. 219(3), pages 671-679.
    3. Florian Kellner & Andreas Otto, 2012. "Allocating CO 2 emissions to shipments in road freight transportation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 22(4), pages 451-479, January.
    4. Ubeda, S. & Arcelus, F.J. & Faulin, J., 2011. "Green logistics at Eroski: A case study," International Journal of Production Economics, Elsevier, vol. 131(1), pages 44-51, May.
    5. Bektas, Tolga & Laporte, Gilbert, 2011. "The Pollution-Routing Problem," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1232-1250, September.
    6. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "The impact of depot location, fleet composition and routing on emissions in city logistics," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 81-102.
    7. Barth, Matthew & Younglove, Theodore & Scora, George, 2005. "Development of a Heavy-Duty Diesel Modal Emissions and Fuel Consumption Model," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt67f0v3zf, Institute of Transportation Studies, UC Berkeley.
    8. 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.
    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. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2014. "The fleet size and mix pollution-routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 239-254.
    11. Soysal, Mehmet & Bloemhof-Ruwaard, Jacqueline M. & Haijema, Rene & van der Vorst, Jack G.A.J., 2015. "Modeling an Inventory Routing Problem for perishable products with environmental considerations and demand uncertainty," International Journal of Production Economics, Elsevier, vol. 164(C), pages 118-133.
    12. W Maden & R Eglese & D Black, 2010. "Vehicle routing and scheduling with time-varying data: A case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 515-522, March.
    13. Franceschetti, Anna & Honhon, Dorothée & Van Woensel, Tom & Bektaş, Tolga & Laporte, Gilbert, 2013. "The time-dependent pollution-routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 265-293.
    14. Qian, Jiani & Eglese, Richard, 2016. "Fuel emissions optimization in vehicle routing problems with time-varying speeds," European Journal of Operational Research, Elsevier, vol. 248(3), pages 840-848.
    15. Kramer, Raphael & Subramanian, Anand & Vidal, Thibaut & Cabral, Lucídio dos Anjos F., 2015. "A matheuristic approach for the Pollution-Routing Problem," European Journal of Operational Research, Elsevier, vol. 243(2), pages 523-539.
    16. Barth, Matthew & Boriboonsomsin, Kanok, 2009. "Traffic Congestion and Greenhouse Gases," University of California Transportation Center, Working Papers qt3vz7t3db, University of California Transportation Center.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Xu & Sun, Huijun & Zhang, Si & Lv, Ying & Li, Tongfei, 2022. "Bike sharing rebalancing problem with variable demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    2. Yu, Yang & Wang, Sihan & Wang, Junwei & Huang, Min, 2019. "A branch-and-price algorithm for the heterogeneous fleet green vehicle routing problem with time windows," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 511-527.
    3. Zhuowu Zhang & Emrah Demir & Robert Mason & Carla Cairano-Gilfedder, 2023. "Understanding freight drivers' behavior and the impact on vehicles' fuel consumption and CO2e emissions," Operational Research, Springer, vol. 23(4), pages 1-35, December.
    4. Liu, Yonggang & Chen, Qianyou & Li, Jie & Zhang, Yuanjian & Chen, Zheng & Lei, Zhenzhen, 2023. "Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles," Energy, Elsevier, vol. 274(C).
    5. Qiu, Rui & Xu, Jiuping & Ke, Ruimin & Zeng, Ziqiang & Wang, Yinhai, 2020. "Carbon pricing initiatives-based bi-level pollution routing problem," European Journal of Operational Research, Elsevier, vol. 286(1), pages 203-217.
    6. Emna Marrekchi & Walid Besbes & Diala Dhouib & Emrah Demir, 2021. "A review of recent advances in the operations research literature on the green routing problem and its variants," Annals of Operations Research, Springer, vol. 304(1), pages 529-574, September.
    7. Jin Li & Feng Wang & Yu He, 2020. "Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    8. Raeesi, Ramin & Zografos, Konstantinos G., 2019. "The multi-objective Steiner pollution-routing problem on congested urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 457-485.
    9. Panagiotis Fafoutellis & Eleni G. Mantouka & Eleni I. Vlahogianni, 2020. "Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    10. Shang, Wen-Long & Zhang, Mengxiao & Wu, Guoyuan & Yang, Lan & Fang, Shan & Ochieng, Washington, 2023. "Estimation of traffic energy consumption based on macro-micro modelling with sparse data from Connected and Automated Vehicles," Applied Energy, Elsevier, vol. 351(C).
    11. Anil K. Madhusudhanan & Xiaoxiang Na & David Cebon, 2021. "A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles," Energies, MDPI, vol. 14(7), pages 1-15, April.
    12. Yurii Gutarevych & Vasyl Mateichyk & Jonas Matijošius & Alfredas Rimkus & Igor Gritsuk & Oleksander Syrota & Yevheniy Shuba, 2020. "Improving Fuel Economy of Spark Ignition Engines Applying the Combined Method of Power Regulation," Energies, MDPI, vol. 13(5), pages 1-19, March.
    13. Behnke, Martin & Kirschstein, Thomas & Bierwirth, Christian, 2021. "A column generation approach for an emission-oriented vehicle routing problem on a multigraph," European Journal of Operational Research, Elsevier, vol. 288(3), pages 794-809.
    14. Zhipeng Ma & Bo Nørregaard Jørgensen & Zheng Ma, 2024. "A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI Methods," Energies, MDPI, vol. 17(2), pages 1-19, January.
    15. Chiang, Wen-Chyuan & Li, Yuyu & Shang, Jennifer & Urban, Timothy L., 2019. "Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization," Applied Energy, Elsevier, vol. 242(C), pages 1164-1175.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohammad Asghari & Seyed Mohammad Javad Mirzapour Al-E-Hashem, 2021. "Green vehicle routing problem: A state-of-the-art review," Post-Print hal-03182944, HAL.
    2. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).
    3. Huang, Yixiao & Zhao, Lei & Van Woensel, Tom & Gross, Jean-Philippe, 2017. "Time-dependent vehicle routing problem with path flexibility," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 169-195.
    4. Bektaş, Tolga & Ehmke, Jan Fabian & Psaraftis, Harilaos N. & Puchinger, Jakob, 2019. "The role of operational research in green freight transportation," European Journal of Operational Research, Elsevier, vol. 274(3), pages 807-823.
    5. Ehmke, Jan Fabian & Campbell, Ann M. & Thomas, Barrett W., 2018. "Optimizing for total costs in vehicle routing in urban areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 242-265.
    6. Emna Marrekchi & Walid Besbes & Diala Dhouib & Emrah Demir, 2021. "A review of recent advances in the operations research literature on the green routing problem and its variants," Annals of Operations Research, Springer, vol. 304(1), pages 529-574, September.
    7. Xiao, Yiyong & Konak, Abdullah, 2016. "The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 146-166.
    8. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "The impact of depot location, fleet composition and routing on emissions in city logistics," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 81-102.
    9. Qiu, Rui & Xu, Jiuping & Ke, Ruimin & Zeng, Ziqiang & Wang, Yinhai, 2020. "Carbon pricing initiatives-based bi-level pollution routing problem," European Journal of Operational Research, Elsevier, vol. 286(1), pages 203-217.
    10. Brunner, Carlos & Giesen, Ricardo & Klapp, Mathias A. & Flórez-Calderón, Luz, 2021. "Vehicle routing problem with steep roads," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 1-17.
    11. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2014. "A review of recent research on green road freight transportation," European Journal of Operational Research, Elsevier, vol. 237(3), pages 775-793.
    12. José-Fernando Camacho-Vallejo & Lilian López-Vera & Alice E. Smith & José-Luis González-Velarde, 2022. "A tabu search algorithm to solve a green logistics bi-objective bi-level problem," Annals of Operations Research, Springer, vol. 316(2), pages 927-953, September.
    13. Zhang, Shuai & Gajpal, Yuvraj & Appadoo, S.S. & Abdulkader, M.M.S., 2018. "Electric vehicle routing problem with recharging stations for minimizing energy consumption," International Journal of Production Economics, Elsevier, vol. 203(C), pages 404-413.
    14. Cheng, Chun & Yang, Peng & Qi, Mingyao & Rousseau, Louis-Martin, 2017. "Modeling a green inventory routing problem with a heterogeneous fleet," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 97-112.
    15. Fukasawa, Ricardo & He, Qie & Song, Yongjia, 2016. "A disjunctive convex programming approach to the pollution-routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 61-79.
    16. Kramer, Raphael & Subramanian, Anand & Vidal, Thibaut & Cabral, Lucídio dos Anjos F., 2015. "A matheuristic approach for the Pollution-Routing Problem," European Journal of Operational Research, Elsevier, vol. 243(2), pages 523-539.
    17. Micheli, Guido J.L. & Mantella, Fabio, 2018. "Modelling an environmentally-extended inventory routing problem with demand uncertainty and a heterogeneous fleet under carbon control policies," International Journal of Production Economics, Elsevier, vol. 204(C), pages 316-327.
    18. Raeesi, Ramin & Zografos, Konstantinos G., 2019. "The multi-objective Steiner pollution-routing problem on congested urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 457-485.
    19. Xiao, Yiyong & Zuo, Xiaorong & Huang, Jiaoying & Konak, Abdullah & Xu, Yuchun, 2020. "The continuous pollution routing problem," Applied Mathematics and Computation, Elsevier, vol. 387(C).
    20. Onur Can Saka & Sinan Gürel & Tom Van Woensel, 2017. "Using cost change estimates in a local search heuristic for the pollution routing problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(2), pages 557-587, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:262:y:2017:i:2:p:647-659. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.