IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v232y2014i3p464-478.html
   My bibliography  Save this article

The bi-objective Pollution-Routing Problem

Author

Listed:
  • Demir, Emrah
  • Bektaş, Tolga
  • Laporte, Gilbert

Abstract

The bi-objective Pollution-Routing Problem is an extension of the Pollution-Routing Problem (PRP) which consists of routing a number of vehicles to serve a set of customers, and determining their speed on each route segment. The two objective functions pertaining to minimization of fuel consumption and driving time are conflicting and are thus considered separately. This paper presents an adaptive large neighborhood search algorithm (ALNS), combined with a speed optimization procedure, to solve the bi-objective PRP. Using the ALNS as the search engine, four a posteriori methods, namely the weighting method, the weighting method with normalization, the epsilon-constraint method and a new hybrid method (HM), are tested using a scalarization of the two objective functions. The HM combines adaptive weighting with the epsilon-constraint method. To evaluate the effectiveness of the algorithm, new sets of instances based on real geographic data are generated, and a library of bi-criteria PRP instances is compiled. Results of extensive computational experiments with the four methods are presented and compared with one another by means of the hypervolume and epsilon indicators. The results show that HM is highly effective in finding good-quality non-dominated solutions on PRP instances with 100 nodes.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:232:y:2014:i:3:p:464-478
    DOI: 10.1016/j.ejor.2013.08.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221713006486
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2013.08.002?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

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Laumanns, Marco & Thiele, Lothar & Zitzler, Eckart, 2006. "An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method," European Journal of Operational Research, Elsevier, vol. 169(3), pages 932-942, March.
    4. Bektas, Tolga & Laporte, Gilbert, 2011. "The Pollution-Routing Problem," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1232-1250, September.
    5. repec:cdl:uctcwp:qt4fx9g4gn is not listed on IDEAS
    6. Forkenbrock, David J., 2001. "Comparison of external costs of rail and truck freight transportation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(4), pages 321-337, May.
    7. Forkenbrock, David J., 1999. "External costs of intercity truck freight transportation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(7-8), pages 505-526.
    8. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
    9. repec:cdl:itsrrp:qt67f0v3zf is not listed on IDEAS
    10. 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.
    11. Crainic, Teodor Gabriel, 2000. "Service network design in freight transportation," European Journal of Operational Research, Elsevier, vol. 122(2), pages 272-288, April.
    12. Tight, M.R. & Bristow, A.L. & Pridmore, A. & May, A.D., 2005. "What is a sustainable level of CO2 emissions from transport activity in the UK in 2050?," Transport Policy, Elsevier, vol. 12(3), pages 235-244, May.
    13. Paquette, Julie & Cordeau, Jean-François & Laporte, Gilbert & Pascoal, Marta M.B., 2013. "Combining multicriteria analysis and tabu search for dial-a-ride problems," Transportation Research Part B: Methodological, Elsevier, vol. 52(C), pages 1-16.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Franceschetti, Anna & Demir, Emrah & Honhon, Dorothée & Van Woensel, Tom & Laporte, Gilbert & Stobbe, Mark, 2017. "A metaheuristic for the time-dependent pollution-routing problem," European Journal of Operational Research, Elsevier, vol. 259(3), pages 972-991.
    3. 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.
    4. Luo, Zhixing & Qin, Hu & Zhang, Dezhi & Lim, Andrew, 2016. "Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 85(C), pages 69-89.
    5. 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.
    6. Zhang, Jianghua & Zhao, Yingxue & Xue, Weili & Li, Jin, 2015. "Vehicle routing problem with fuel consumption and carbon emission," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 234-242.
    7. 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.
    8. 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.
    9. Surendra Reddy Kancharla & Gitakrishnan Ramadurai, 2019. "Multi-depot Two-Echelon Fuel Minimizing Routing Problem with Heterogeneous Fleets: Model and Heuristic," Networks and Spatial Economics, Springer, vol. 19(3), pages 969-1005, September.
    10. 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.
    11. Mo, Pengli & Yao, Yu & D’Ariano, Andrea & Liu, Zhiyuan, 2023. "The vehicle routing problem with underground logistics: Formulation and algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    12. Zhao, Lei & Bi, Xinhua & Li, Gendao & Dong, Zhaohui & Xiao, Ni & Zhao, Anni, 2022. "Robust traveling salesman problem with multiple drones: Parcel delivery under uncertain navigation environments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    13. Yossiri Adulyasak & Jean-François Cordeau & Raf Jans, 2014. "Optimization-Based Adaptive Large Neighborhood Search for the Production Routing Problem," Transportation Science, INFORMS, vol. 48(1), pages 20-45, February.
    14. Maria João Santos & Pedro Amorim & Alexandra Marques & Ana Carvalho & Ana Póvoa, 2020. "The vehicle routing problem with backhauls towards a sustainability perspective: a review," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 358-401, July.
    15. Cárdenas-Barrón, Leopoldo Eduardo & González-Velarde, José Luis & Treviño-Garza, Gerardo & Garza-Nuñez, Dagoberto, 2019. "Heuristic algorithm based on reduce and optimize approach for a selective and periodic inventory routing problem in a waste vegetable oil collection environment," International Journal of Production Economics, Elsevier, vol. 211(C), pages 44-59.
    16. 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.
    17. Gilbert Laporte, 2016. "Scheduling issues in vehicle routing," Annals of Operations Research, Springer, vol. 236(2), pages 463-474, January.
    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. 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.
    20. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    Statistics

    Access and download statistics

    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:232:y:2014:i:3:p:464-478. 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.