IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v148y2021ics1366554521000454.html
   My bibliography  Save this article

Analysing charging strategies for electric LGV in grocery delivery operation using agent-based modelling: An initial case study in the United Kingdom

Author

Listed:
  • Utomo, D.S.
  • Gripton, A.
  • Greening, P.

Abstract

This paper presents an agent-based simulation study aimed at evaluating the impact of different charging strategies on the performance of home grocery delivery operation using electric vans. In our previous work we established the quantity of orders that can be delivered using electric vans; in this paper we focus on the punctuality of the delivery. We present a baseline agent-based model imitating the operations of a real-world retailer. We then introduce electric vans into our model in order to ascertain how charging power and charging strategy influence the retailer’s operations. Even though electric vans cannot match the performance of diesel vehicles using the same fleet size, our simulation experiments suggest that, by considering the quantity of orders and the geographical distribution of its customers, an operator can determine a suitable charging strategy that can minimise late delivery. Additionally, by employing a suitable charging strategy, an operator might avoid making unnecessary investments and reduce the barriers for electric vehicle adoption.

Suggested Citation

  • Utomo, D.S. & Gripton, A. & Greening, P., 2021. "Analysing charging strategies for electric LGV in grocery delivery operation using agent-based modelling: An initial case study in the United Kingdom," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:transe:v:148:y:2021:i:c:s1366554521000454
    DOI: 10.1016/j.tre.2021.102269
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554521000454
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2021.102269?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. Querini, Florent & Benetto, Enrico, 2014. "Agent-based modelling for assessing hybrid and electric cars deployment policies in Luxembourg and Lorraine," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 149-161.
    2. Firdausiyah, N. & Taniguchi, E. & Qureshi, A.G., 2019. "Modeling city logistics using adaptive dynamic programming based multi-agent simulation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 74-96.
    3. Andrés Arias-Londoño & Oscar Danilo Montoya & Luis Fernando Grisales-Noreña, 2020. "A Chronological Literature Review of Electric Vehicle Interactions with Power Distribution Systems," Energies, MDPI, vol. 13(11), pages 1-23, June.
    4. Sakai, Takanori & Romano Alho, André & Bhavathrathan, B.K. & Chiara, Giacomo Dalla & Gopalakrishnan, Raja & Jing, Peiyu & Hyodo, Tetsuro & Cheah, Lynette & Ben-Akiva, Moshe, 2020. "SimMobility Freight: An agent-based urban freight simulator for evaluating logistics solutions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    5. K Hoad & S Robinson & R Davies, 2010. "Automated selection of the number of replications for a discrete-event simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(11), pages 1632-1644, November.
    6. Shafiei, Ehsan & Thorkelsson, Hedinn & Ásgeirsson, Eyjólfur Ingi & Davidsdottir, Brynhildur & Raberto, Marco & Stefansson, Hlynur, 2012. "An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland," Technological Forecasting and Social Change, Elsevier, vol. 79(9), pages 1638-1653.
    7. Maxwell Brown, 2013. "Catching the PHEVer: Simulating Electric Vehicle Diffusion with an Agent-Based Mixed Logit Model of Vehicle Choice," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(2), pages 1-5.
    8. Juuso Lindgren & Peter D. Lund, 2015. "Identifying bottlenecks in charging infrastructure of plug-in hybrid electric vehicles through agent-based traffic simulation," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 10(2), pages 110-118.
    9. Cortés-Murcia, David L. & Prodhon, Caroline & Murat Afsar, H., 2019. "The electric vehicle routing problem with time windows, partial recharges and satellite customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 184-206.
    10. Roorda, Matthew J. & Cavalcante, Rinaldo & McCabe, Stephanie & Kwan, Helen, 2010. "A conceptual framework for agent-based modelling of logistics services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(1), pages 18-31, January.
    11. An, Li, 2012. "Modeling human decisions in coupled human and natural systems: Review of agent-based models," Ecological Modelling, Elsevier, vol. 229(C), pages 25-36.
    12. Utomo, Dhanan Sarwo & Onggo, Bhakti Stephan & Eldridge, Stephen, 2018. "Applications of agent-based modelling and simulation in the agri-food supply chains," European Journal of Operational Research, Elsevier, vol. 269(3), pages 794-805.
    13. Murakami, Keisuke, 2017. "A new model and approach to electric and diesel-powered vehicle routing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 107(C), pages 23-37.
    14. Crainic, Teodor Gabriel & Perboli, Guido & Rosano, Mariangela, 2018. "Simulation of intermodal freight transportation systems: a taxonomy," European Journal of Operational Research, Elsevier, vol. 270(2), pages 401-418.
    15. Eppstein, Margaret J. & Grover, David K. & Marshall, Jeffrey S. & Rizzo, Donna M., 2011. "An agent-based model to study market penetration of plug-in hybrid electric vehicles," Energy Policy, Elsevier, vol. 39(6), pages 3789-3802, June.
    16. Pol Olivella-Rosell & Roberto Villafafila-Robles & Andreas Sumper & Joan Bergas-Jané, 2015. "Probabilistic Agent-Based Model of Electric Vehicle Charging Demand to Analyse the Impact on Distribution Networks," Energies, MDPI, vol. 8(5), pages 1-28, May.
    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. Cui, Shaohua & Gao, Kun & Yu, Bin & Ma, Zhenliang & Najafi, Arsalan, 2023. "Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).

    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. Mehdizadeh, Milad & Nordfjaern, Trond & Klöckner, Christian A., 2022. "A systematic review of the agent-based modelling/simulation paradigm in mobility transition," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    2. Tobias Buchmann & Patrick Wolf & Stefan Fidaschek, 2021. "Stimulating E-Mobility Diffusion in Germany (EMOSIM): An Agent-Based Simulation Approach," Energies, MDPI, vol. 14(3), pages 1-25, January.
    3. Gnann, Till & Plötz, Patrick & Kühn, André & Wietschel, Martin, 2015. "Modelling market diffusion of electric vehicles with real world driving data – German market and policy options," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 95-112.
    4. Wolf, Ingo & Schröder, Tobias & Neumann, Jochen & de Haan, Gerhard, 2015. "Changing minds about electric cars: An empirically grounded agent-based modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 269-285.
    5. Sen, Burak & Noori, Mehdi & Tatari, Omer, 2017. "Will Corporate Average Fuel Economy (CAFE) Standard help? Modeling CAFE's impact on market share of electric vehicles," Energy Policy, Elsevier, vol. 109(C), pages 279-287.
    6. Liu, Junbei & Zhuge, Chengxiang & Tang, Justin Hayse Chiwing G. & Meng, Meng & Zhang, Jie, 2022. "A spatial agent-based joint model of electric vehicle and vehicle-to-grid adoption: A case of Beijing," Applied Energy, Elsevier, vol. 310(C).
    7. Adedamola Adepetu & Srinivasan Keshav, 2017. "The relative importance of price and driving range on electric vehicle adoption: Los Angeles case study," Transportation, Springer, vol. 44(2), pages 353-373, March.
    8. Adepetu, Adedamola & Keshav, Srinivasan & Arya, Vijay, 2016. "An agent-based electric vehicle ecosystem model: San Francisco case study," Transport Policy, Elsevier, vol. 46(C), pages 109-122.
    9. Krupa, Joseph S. & Rizzo, Donna M. & Eppstein, Margaret J. & Brad Lanute, D. & Gaalema, Diann E. & Lakkaraju, Kiran & Warrender, Christina E., 2014. "Analysis of a consumer survey on plug-in hybrid electric vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 64(C), pages 14-31.
    10. Gnann, Till & Stephens, Thomas S. & Lin, Zhenhong & Plötz, Patrick & Liu, Changzheng & Brokate, Jens, 2018. "What drives the market for plug-in electric vehicles? - A review of international PEV market diffusion models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 158-164.
    11. Wolbertus, Rick & van den Hoed, Robert & Kroesen, Maarten & Chorus, Caspar, 2021. "Charging infrastructure roll-out strategies for large scale introduction of electric vehicles in urban areas: An agent-based simulation study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 262-285.
    12. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    13. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 190-203.
    14. Nie, Yu (Marco) & Ghamami, Mehrnaz & Zockaie, Ali & Xiao, Feng, 2016. "Optimization of incentive polices for plug-in electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 103-123.
    15. Huber, Robert & Bakker, Martha & Balmann, Alfons & Berger, Thomas & Bithell, Mike & Brown, Calum & Grêt-Regamey, Adrienne & Xiong, Hang & Le, Quang Bao & Mack, Gabriele & Meyfroidt, Patrick & Millingt, 2018. "Representation of decision-making in European agricultural agent-based models," Agricultural Systems, Elsevier, vol. 167(C), pages 143-160.
    16. Wang, Kai-Hua & Su, Chi-Wei & Xiao, Yidong & Liu, Lu, 2022. "Is the oil price a barometer of China's automobile market? From a wavelet-based quantile-on-quantile regression perspective," Energy, Elsevier, vol. 240(C).
    17. Zhang, Cen & Schmöcker, Jan-Dirk & Kuwahara, Masahiro & Nakamura, Toshiyuki & Uno, Nobuhiro, 2020. "A diffusion model for estimating adoption patterns of a one-way carsharing system in its initial years," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 135-150.
    18. 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).
    19. Melton, Noel & Axsen, Jonn & Goldberg, Suzanne, 2017. "Evaluating plug-in electric vehicle policies in the context of long-term greenhouse gas reduction goals: Comparing 10 Canadian provinces using the “PEV policy report card”," Energy Policy, Elsevier, vol. 107(C), pages 381-393.
    20. Noori, Mehdi & Zhao, Yang & Onat, Nuri C. & Gardner, Stephanie & Tatari, Omer, 2016. "Light-duty electric vehicles to improve the integrity of the electricity grid through Vehicle-to-Grid technology: Analysis of regional net revenue and emissions savings," Applied Energy, Elsevier, vol. 168(C), pages 146-158.

    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:transe:v:148:y:2021:i:c:s1366554521000454. 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/wps/find/journaldescription.cws_home/600244/description#description .

    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.