IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v203y2013i1p351-37010.1007-s10479-012-1118-1.html
   My bibliography  Save this article

Data mining-based dispatching system for solving the local pickup and delivery problem

Author

Listed:
  • Weiwei Chen
  • Jie Song
  • Leyuan Shi
  • Liang Pi
  • Peter Sun

Abstract

The Local Pickup and Delivery Problem (LPDP) has drawn much attention, and optimization models and algorithms have been developed to address this problem. However, for real world applications, the large-scale and dynamic nature of the problem causes difficulties in getting good solutions within acceptable time through standard optimization approaches. Meanwhile, actual dispatching solutions made by field experts in transportation companies contain embedded dispatching rules. This paper introduces a Data Mining-based Dispatching System (DMDS) to first learn dispatching rules from historical data and then generate dispatch solutions, which are shown to be as good as those generated by expert dispatchers in the intermodal freight industry. Three additional benefits of DMDS are: (1) it provides a simulation platform for strategic decision making and analysis; (2) the learned dispatching rules are valuable to combine with an optimization algorithm to improve the solution quality for LPDPs; (3) by adding optimized solutions to the training data, DMDS is capable to generate better-than-actuals solutions very quickly. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Weiwei Chen & Jie Song & Leyuan Shi & Liang Pi & Peter Sun, 2013. "Data mining-based dispatching system for solving the local pickup and delivery problem," Annals of Operations Research, Springer, vol. 203(1), pages 351-370, March.
    • Handle: RePEc:spr:annopr:v:203:y:2013:i:1:p:351-370:10.1007/s10479-012-1118-1
    DOI: 10.1007/s10479-012-1118-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-012-1118-1
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-012-1118-1?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. Wang, Xiubin & Regan, Amelia C., 2002. "Local truckload pickup and delivery with hard time window constraints," Transportation Research Part B: Methodological, Elsevier, vol. 36(2), pages 97-112, February.
    2. Dumas, Yvan & Desrosiers, Jacques & Soumis, Francois, 1991. "The pickup and delivery problem with time windows," European Journal of Operational Research, Elsevier, vol. 54(1), pages 7-22, September.
    3. Warren B. Powell & Tassio A. Carvalho, 1998. "Dynamic Control of Logistics Queueing Networks for Large-Scale Fleet Management," Transportation Science, INFORMS, vol. 32(2), pages 90-109, May.
    4. Ann Melissa Campbell & Martin Savelsbergh, 2004. "Efficient Insertion Heuristics for Vehicle Routing and Scheduling Problems," Transportation Science, INFORMS, vol. 38(3), pages 369-378, August.
    5. Nanry, William P. & Wesley Barnes, J., 2000. "Solving the pickup and delivery problem with time windows using reactive tabu search," Transportation Research Part B: Methodological, Elsevier, vol. 34(2), pages 107-121, February.
    6. Desrosiers, Jacques & Soumis, Francois & Desrochers, Martin & SauveGerad, Michel, 1986. "Methods for routing with time windows," European Journal of Operational Research, Elsevier, vol. 23(2), pages 236-245, February.
    7. Olli Bräysy & Michel Gendreau, 2005. "Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms," Transportation Science, INFORMS, vol. 39(1), pages 104-118, February.
    8. Andrew Lim & Fan Wang & Zhou Xu, 2006. "A Transportation Problem with Minimum Quantity Commitment," Transportation Science, INFORMS, vol. 40(1), pages 117-129, February.
    9. Olli Bräysy & Michel Gendreau, 2005. "Vehicle Routing Problem with Time Windows, Part II: Metaheuristics," Transportation Science, INFORMS, vol. 39(1), pages 119-139, February.
    10. Selwyn Piramuthu & Narayan Raman & Michael Shaw, 1998. "Decision support system for scheduling a Flexible Flow System: Incorporation of feature construction," Annals of Operations Research, Springer, vol. 78(0), pages 219-234, January.
    11. Leyuan Shi & Sigurdur Ólafsson, 2000. "Nested Partitions Method for Global Optimization," Operations Research, INFORMS, vol. 48(3), pages 390-407, June.
    12. Clyde Holsapple & Anita Lee & Jim Otto, 1997. "A machine learning method for multi-expert decision support," Annals of Operations Research, Springer, vol. 75(0), pages 171-188, January.
    13. Warren B. Powell & Joel A. Shapiro & Hugo P. Simão, 2002. "An Adaptive Dynamic Programming Algorithm for the Heterogeneous Resource Allocation Problem," Transportation Science, INFORMS, vol. 36(2), pages 231-249, 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. John A. Aloysius & Hartmut Hoehle & Soheil Goodarzi & Viswanath Venkatesh, 2018. "Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes," Annals of Operations Research, Springer, vol. 270(1), pages 25-51, November.
    2. Hongyan Dai & Peng Liu, 2020. "Workforce planning for O2O delivery systems with crowdsourced drivers," Annals of Operations Research, Springer, vol. 291(1), pages 219-245, August.
    3. Tao Wu & Zhe Liang & Canrong Zhang, 2018. "Analytics Branching and Selection for the Capacitated Multi-Item Lot Sizing Problem with Nonidentical Machines," INFORMS Journal on Computing, INFORMS, vol. 30(2), pages 236-258, May.
    4. Asil Oztekin, 2018. "Creating a marketing strategy in healthcare industry: a holistic data analytic approach," Annals of Operations Research, Springer, vol. 270(1), pages 361-382, November.

    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. Fröhlich von Elmbach, Alexander & Scholl, Armin & Walter, Rico, 2019. "Minimizing the maximal ergonomic burden in intra-hospital patient transportation," European Journal of Operational Research, Elsevier, vol. 276(3), pages 840-854.
    2. Braekers, Kris & Caris, An & Janssens, Gerrit K., 2014. "Exact and meta-heuristic approach for a general heterogeneous dial-a-ride problem with multiple depots," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 166-186.
    3. Jeffrey W. Ohlmann & Michael J. Fry & Barrett W. Thomas, 2008. "Route Design for Lean Production Systems," Transportation Science, INFORMS, vol. 42(3), pages 352-370, August.
    4. Baals, Julian & Emde, Simon & Turkensteen, Marcel, 2023. "Minimizing earliness-tardiness costs in supplier networks—A just-in-time truck routing problem," European Journal of Operational Research, Elsevier, vol. 306(2), pages 707-741.
    5. Koch, Sebastian & Klein, Robert, 2020. "Route-based approximate dynamic programming for dynamic pricing in attended home delivery," European Journal of Operational Research, Elsevier, vol. 287(2), pages 633-652.
    6. Goel, Asvin & Gruhn, Volker, 2008. "A General Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 191(3), pages 650-660, December.
    7. Alberto Ceselli & Giovanni Righini & Matteo Salani, 2009. "A Column Generation Algorithm for a Rich Vehicle-Routing Problem," Transportation Science, INFORMS, vol. 43(1), pages 56-69, February.
    8. Wang, Zheng, 2018. "Delivering meals for multiple suppliers: Exclusive or sharing logistics service," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 496-512.
    9. Krajewska, Marta Anna & Kopfer, Herbert, 2009. "Transportation planning in freight forwarding companies: Tabu search algorithm for the integrated operational transportation planning problem," European Journal of Operational Research, Elsevier, vol. 197(2), pages 741-751, September.
    10. Zhang, Ruiyou & Yun, Won Young & Moon, Il Kyeong, 2011. "Modeling and optimization of a container drayage problem with resource constraints," International Journal of Production Economics, Elsevier, vol. 133(1), pages 351-359, September.
    11. Olli Bräysy & Wout Dullaert & Geir Hasle & David Mester & Michel Gendreau, 2008. "An Effective Multirestart Deterministic Annealing Metaheuristic for the Fleet Size and Mix Vehicle-Routing Problem with Time Windows," Transportation Science, INFORMS, vol. 42(3), pages 371-386, August.
    12. Jean-Yves Potvin, 2009. "State-of-the Art Review ---Evolutionary Algorithms for Vehicle Routing," INFORMS Journal on Computing, INFORMS, vol. 21(4), pages 518-548, November.
    13. Liu, Ran & Xie, Xiaolan & Garaix, Thierry, 2014. "Hybridization of tabu search with feasible and infeasible local searches for periodic home health care logistics," Omega, Elsevier, vol. 47(C), pages 17-32.
    14. Ma, Hong & Cheang, Brenda & Lim, Andrew & Zhang, Lei & Zhu, Yi, 2012. "An investigation into the vehicle routing problem with time windows and link capacity constraints," Omega, Elsevier, vol. 40(3), pages 336-347.
    15. Asvin Goel, 2009. "Vehicle Scheduling and Routing with Drivers' Working Hours," Transportation Science, INFORMS, vol. 43(1), pages 17-26, February.
    16. Naji-Azimi, Zahra & Salari, Majid & Renaud, Jacques & Ruiz, Angel, 2016. "A practical vehicle routing problem with desynchronized arrivals to depot," European Journal of Operational Research, Elsevier, vol. 255(1), pages 58-67.
    17. Asbach, Lasse & Dorndorf, Ulrich & Pesch, Erwin, 2009. "Analysis, modeling and solution of the concrete delivery problem," European Journal of Operational Research, Elsevier, vol. 193(3), pages 820-835, March.
    18. Nicolas Rincon-Garcia & Ben J. Waterson & Tom J. Cherrett, 2018. "Requirements from vehicle routing software: perspectives from literature, developers and the freight industry," Transport Reviews, Taylor & Francis Journals, vol. 38(1), pages 117-138, January.
    19. Lu, Quan & Dessouky, Maged M., 2006. "A new insertion-based construction heuristic for solving the pickup and delivery problem with time windows," European Journal of Operational Research, Elsevier, vol. 175(2), pages 672-687, December.
    20. Schmid, Verena & Doerner, Karl F. & Laporte, Gilbert, 2013. "Rich routing problems arising in supply chain management," European Journal of Operational Research, Elsevier, vol. 224(3), pages 435-448.

    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:spr:annopr:v:203:y:2013:i:1:p:351-370:10.1007/s10479-012-1118-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.