IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2016i7p607-d72912.html
   My bibliography  Save this article

The Shortest Path Problems in Battery-Electric Vehicle Dispatching with Battery Renewal

Author

Listed:
  • Minfang Huang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Jing-Quan Li

    (California PATH, University of California, Berkeley, Richmond, CA 94804, USA)

Abstract

Electric vehicles play a key role for developing an eco-sustainable transport system. One critical component of an electric vehicle is its battery, which can be quickly charged or exchanged before it runs out. The problem of electric vehicle dispatching falls into the category of the shortest path problem with resource renewal. In this paper, we study the shortest path problems in (1) electric transit bus scheduling and (2) electric truck routing with time windows. In these applications, a fully-charged battery allows running a limited operational distance, and the battery before depletion needs to be quickly charged or exchanged with a fully-charged one at a battery management facility. The limited distance and battery renewal result in a shortest path problem with resource renewal. We develop a label-correcting algorithm with state space relaxation to find optimal solutions. In the computational experiments, real-world road geometry data are used to generate realistic travel distances, and other types of data are obtained from the real world or randomly generated. The computational results show that the label-correcting algorithm performs very well.

Suggested Citation

  • Minfang Huang & Jing-Quan Li, 2016. "The Shortest Path Problems in Battery-Electric Vehicle Dispatching with Battery Renewal," Sustainability, MDPI, vol. 8(7), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:7:p:607-:d:72912
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/7/607/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/8/7/607/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yagcitekin, Bunyamin & Uzunoglu, Mehmet, 2016. "A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account," Applied Energy, Elsevier, vol. 167(C), pages 407-419.
    2. Marius M. Solomon, 1987. "Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints," Operations Research, INFORMS, vol. 35(2), pages 254-265, April.
    3. Martin Desrochers & François Soumis, 1989. "A Column Generation Approach to the Urban Transit Crew Scheduling Problem," Transportation Science, INFORMS, vol. 23(1), pages 1-13, February.
    4. Stefan Irnich & Daniel Villeneuve, 2006. "The Shortest-Path Problem with Resource Constraints and k -Cycle Elimination for k (ge) 3," INFORMS Journal on Computing, INFORMS, vol. 18(3), pages 391-406, August.
    5. Roberti, R. & Wen, M., 2016. "The Electric Traveling Salesman Problem with Time Windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 32-52.
    6. Wang, Ying-Wei & Lin, Chuah-Chih, 2009. "Locating road-vehicle refueling stations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(5), pages 821-829, September.
    7. Desaulniers, Guy & Lavigne, June & Soumis, Francois, 1998. "Multi-depot vehicle scheduling problems with time windows and waiting costs," European Journal of Operational Research, Elsevier, vol. 111(3), pages 479-494, December.
    8. Bode, Claudia & Irnich, Stefan, 2014. "The shortest-path problem with resource constraints with (k,2)-loop elimination and its application to the capacitated arc-routing problem," European Journal of Operational Research, Elsevier, vol. 238(2), pages 415-426.
    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. Yuping Lin & Kai Zhang & Zuo-Jun Max Shen & Lixin Miao, 2019. "Charging Network Planning for Electric Bus Cities: A Case Study of Shenzhen, China," Sustainability, MDPI, vol. 11(17), pages 1-27, August.
    2. Salsabil Gherairi, 2019. "Hybrid Electric Vehicle: Design and Control of a Hybrid System (Fuel Cell/Battery/Ultra-Capacitor) Supplied by Hydrogen," Energies, MDPI, vol. 12(7), pages 1-19, April.
    3. Guangwen Zhang & Zhongxing Du & Yaqun He & Haifeng Wang & Weining Xie & Tao Zhang, 2019. "A Sustainable Process for the Recovery of Anode and Cathode Materials Derived from Spent Lithium-Ion Batteries," Sustainability, MDPI, vol. 11(8), pages 1-11, April.
    4. M. E. Kooten Niekerk & J. M. Akker & J. A. Hoogeveen, 2017. "Scheduling electric vehicles," Public Transport, Springer, vol. 9(1), pages 155-176, July.
    5. Liang Gong & Yinzhen Li & Dejie Xu, 2019. "Combinational Scheduling Model Considering Multiple Vehicle Sizes," Sustainability, MDPI, vol. 11(19), pages 1-14, September.

    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. Rosemary T. Berger & Collette R. Coullard & Mark S. Daskin, 2007. "Location-Routing Problems with Distance Constraints," Transportation Science, INFORMS, vol. 41(1), pages 29-43, February.
    2. Christian Tilk & Ann-Kathrin Rothenbächer & Timo Gschwind & Stefan Irnich, 2016. "Asymmetry Helps: Dynamic Half-Way Points for Solving Shortest Path Problems with Resource Constraints Faster," Working Papers 1615, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    3. Tilk, Christian & Rothenbächer, Ann-Kathrin & Gschwind, Timo & Irnich, Stefan, 2017. "Asymmetry matters: Dynamic half-way points in bidirectional labeling for solving shortest path problems with resource constraints faster," European Journal of Operational Research, Elsevier, vol. 261(2), pages 530-539.
    4. Pradhananga, Rojee & Taniguchi, Eiichi & Yamada, Tadashi & Qureshi, Ali Gul, 2014. "Bi-objective decision support system for routing and scheduling of hazardous materials," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 135-148.
    5. Zäpfel, Günther & Bögl, Michael, 2008. "Multi-period vehicle routing and crew scheduling with outsourcing options," International Journal of Production Economics, Elsevier, vol. 113(2), pages 980-996, June.
    6. Baldacci, Roberto & Mingozzi, Aristide & Roberti, Roberto, 2012. "Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints," European Journal of Operational Research, Elsevier, vol. 218(1), pages 1-6.
    7. Maximilian Schiffer & Michael Schneider & Grit Walther & Gilbert Laporte, 2019. "Vehicle Routing and Location Routing with Intermediate Stops: A Review," Transportation Science, INFORMS, vol. 53(2), pages 319-343, March.
    8. Qie He & Stefan Irnich & Yongjia Song, 2018. "Branch-Cut-and-Price for the Vehicle Routing Problem with Time Windows and Convex Node Costs," Working Papers 1804, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    9. Range, Troels Martin, 2013. "Exploiting Set-Based Structures to Accelerate Dynamic Programming Algorithms for the Elementary Shortest Path Problem with Resource Constraints," Discussion Papers on Economics 17/2013, University of Southern Denmark, Department of Economics.
    10. Taş, D. & Gendreau, M. & Dellaert, N. & van Woensel, T. & de Kok, A.G., 2014. "Vehicle routing with soft time windows and stochastic travel times: A column generation and branch-and-price solution approach," European Journal of Operational Research, Elsevier, vol. 236(3), pages 789-799.
    11. Shyam S. G. Perumal & Jesper Larsen & Richard M. Lusby & Morten Riis & Tue R. L. Christensen, 2022. "A column generation approach for the driver scheduling problem with staff cars," Public Transport, Springer, vol. 14(3), pages 705-738, October.
    12. Bhusiri, Narath & Qureshi, Ali Gul & Taniguchi, Eiichi, 2014. "The trade-off between fixed vehicle costs and time-dependent arrival penalties in a routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 1-22.
    13. Henriette Koch & Andreas Bortfeldt & Gerhard Wäscher, 2018. "A hybrid algorithm for the vehicle routing problem with backhauls, time windows and three-dimensional loading constraints," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 1029-1075, October.
    14. Qie He & Stefan Irnich & Yongjia Song, 2019. "Branch-and-Cut-and-Price for the Vehicle Routing Problem with Time Windows and Convex Node Costs," Transportation Science, INFORMS, vol. 53(5), pages 1409-1426, September.
    15. Guy Desaulniers & François Lessard & Ahmed Hadjar, 2008. "Tabu Search, Partial Elementarity, and Generalized k -Path Inequalities for the Vehicle Routing Problem with Time Windows," Transportation Science, INFORMS, vol. 42(3), pages 387-404, August.
    16. Lee, Jongsung & Kim, Byung-In & Johnson, Andrew L. & Lee, Kiho, 2014. "The nuclear medicine production and delivery problem," European Journal of Operational Research, Elsevier, vol. 236(2), pages 461-472.
    17. 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.
    18. Martin Wölck & Stephan Meisel, 2022. "Branch-and-Price Approaches for Real-Time Vehicle Routing with Picking, Loading, and Soft Time Windows," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2192-2211, July.
    19. Perumal, S.S.G. & Dollevoet, T.A.B. & Huisman, D. & Lusby, R.M. & Larsen, J. & Riis, M., 2020. "Solution Approaches for Vehicle and Crew Scheduling with Electric Buses," Econometric Institute Research Papers EI-2020-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    20. Nikolaus Furian & Michael O’Sullivan & Cameron Walker & Eranda Çela, 2021. "A machine learning-based branch and price algorithm for a sampled vehicle routing problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 693-732, September.

    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:gam:jsusta:v:8:y:2016:i:7:p:607-:d:72912. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.