IDEAS home Printed from https://ideas.repec.org/a/inm/ortrsc/v49y2015i2p223-238.html
   My bibliography  Save this article

A Multistage Very Large-Scale Neighborhood Search for the Vehicle Routing Problem with Soft Time Windows

Author

Listed:
  • Sébastien Mouthuy

    (Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium)

  • Florence Massen

    (Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium)

  • Yves Deville

    (Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium)

  • Pascal Van Hentenryck

    (NICTA and the Australian National University, Canberra ACT 0200, Australia)

Abstract

This paper considers the vehicle routing problem with soft time windows, a challenging routing problem where customers' time windows may be violated at a certain cost. The vehicle routing problem with soft time windows has a lexicographic objective function, aimed at minimizing first the number of routes, then the number of violated time windows, and finally the total routing distance. We present a multistage very large-scale neighborhood search for this problem. Each stage corresponds to a variable neighborhood descent over a parameterizable very large-scale neighborhood. These neighborhoods contain an exponential number of neighbors, as opposed to classical local search neighborhoods. Often, searching very large-scale neighborhoods can produce local optima of a higher quality than polynomial-sized neighborhoods can. Furthermore, we use a sophisticated heuristic to determine service start times allowing us to minimize the number of violated time windows. We test our approach on a number of different problem types, and compare the results to the relevant state of the art. The experimental results show that our algorithm improves best known solutions on 53% of the most studied instances. Many of these improvements stem from a reduction of the number of vehicles, a critical objective in vehicle routing problems.

Suggested Citation

  • Sébastien Mouthuy & Florence Massen & Yves Deville & Pascal Van Hentenryck, 2015. "A Multistage Very Large-Scale Neighborhood Search for the Vehicle Routing Problem with Soft Time Windows," Transportation Science, INFORMS, vol. 49(2), pages 223-238, May.
  • Handle: RePEc:inm:ortrsc:v:49:y:2015:i:2:p:223-238
    DOI: 10.1287/trsc.2014.0558
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/trsc.2014.0558
    Download Restriction: no

    File URL: https://libkey.io/10.1287/trsc.2014.0558?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
    ---><---

    References listed on IDEAS

    as
    1. Calvete, Herminia I. & Gale, Carmen & Oliveros, Maria-Jose & Sanchez-Valverde, Belen, 2007. "A goal programming approach to vehicle routing problems with soft time windows," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1720-1733, March.
    2. Qureshi, A.G. & Taniguchi, E. & Yamada, T., 2009. "An exact solution approach for vehicle routing and scheduling problems with soft time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(6), pages 960-977, November.
    3. Fagerholt, Kjetil, 2001. "Ship scheduling with soft time windows: An optimisation based approach," European Journal of Operational Research, Elsevier, vol. 131(3), pages 559-571, June.
    4. Paul M. Thompson & Harilaos N. Psaraftis, 1993. "Cyclic Transfer Algorithm for Multivehicle Routing and Scheduling Problems," Operations Research, INFORMS, vol. 41(5), pages 935-946, October.
    5. S Abdullah & S Ahmadi & E K Burke & M Dror & B McCollum, 2007. "A tabu-based large neighbourhood search methodology for the capacitated examination timetabling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(11), pages 1494-1502, November.
    6. Russell Bent & Pascal Van Hentenryck, 2004. "A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows," Transportation Science, INFORMS, vol. 38(4), pages 515-530, November.
    7. Ioannou, George & Kritikos, Manolis & Prastacos, Gregory, 2003. "A problem generator-solver heuristic for vehicle routing with soft time windows," Omega, Elsevier, vol. 31(1), pages 41-53, February.
    8. Yiannis A. Koskosidis & Warren B. Powell & Marius M. Solomon, 1992. "An Optimization-Based Heuristic for Vehicle Routing and Scheduling with Soft Time Window Constraints," Transportation Science, INFORMS, vol. 26(2), pages 69-85, May.
    9. T. Ibaraki & S. Imahori & M. Kubo & T. Masuda & T. Uno & M. Yagiura, 2005. "Effective Local Search Algorithms for Routing and Scheduling Problems with General Time-Window Constraints," Transportation Science, INFORMS, vol. 39(2), pages 206-232, May.
    10. W-C Chiang & R A Russell, 2004. "A metaheuristic for the vehicle-routeing problem with soft time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1298-1310, December.
    11. Christophe Duhamel & Jean-Yves Potvin & Jean-Marc Rousseau, 1997. "A Tabu Search Heuristic for the Vehicle Routing Problem with Backhauls and Time Windows," Transportation Science, INFORMS, vol. 31(1), pages 49-59, February.
    12. Azi, Nabila & Gendreau, Michel & Potvin, Jean-Yves, 2007. "An exact algorithm for a single-vehicle routing problem with time windows and multiple routes," European Journal of Operational Research, Elsevier, vol. 178(3), pages 755-766, May.
    13. Homberger, Jorg & Gehring, Hermann, 2005. "A two-phase hybrid metaheuristic for the vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 162(1), pages 220-238, April.
    14. Éric Taillard & Philippe Badeau & Michel Gendreau & François Guertin & Jean-Yves Potvin, 1997. "A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows," Transportation Science, INFORMS, vol. 31(2), pages 170-186, May.
    15. Hu, Chao-Fang & Teng, Chang-Jun & Li, Shao-Yuan, 2007. "A fuzzy goal programming approach to multi-objective optimization problem with priorities," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1319-1333, February.
    16. 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.
    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. Manuel Ostermeier & Andreas Holzapfel & Heinrich Kuhn & Daniel Schubert, 2022. "Integrated zone picking and vehicle routing operations with restricted intermediate storage," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(3), pages 795-832, September.
    2. Kuhn, Heinrich & Schubert, Daniel & Holzapfel, Andreas, 2021. "Integrated order batching and vehicle routing operations in grocery retail – A General Adaptive Large Neighborhood Search algorithm," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1003-1021.

    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. 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.
    2. 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.
    3. Andrew Lim & Xingwen Zhang, 2007. "A Two-Stage Heuristic with Ejection Pools and Generalized Ejection Chains for the Vehicle Routing Problem with Time Windows," INFORMS Journal on Computing, INFORMS, vol. 19(3), pages 443-457, August.
    4. Matteo Salani & Maria Battarra, 2018. "The opportunity cost of time window violations," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(4), pages 343-361, December.
    5. R A Russell & T L Urban, 2008. "Vehicle routing with soft time windows and Erlang travel times," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1220-1228, September.
    6. Z Fu & R Eglese & L Y O Li, 2008. "A unified tabu search algorithm for vehicle routing problems with soft time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(5), pages 663-673, May.
    7. Hideki Hashimoto & Mutsunori Yagiura & Shinji Imahori & Toshihide Ibaraki, 2013. "Recent progress of local search in handling the time window constraints of the vehicle routing problem," Annals of Operations Research, Springer, vol. 204(1), pages 171-187, April.
    8. Aderemi Oluyinka Adewumi & Olawale Joshua Adeleke, 2018. "A survey of recent advances in vehicle routing problems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 155-172, February.
    9. 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.
    10. Olli Bräysy & Michel Gendreau, 2005. "Vehicle Routing Problem with Time Windows, Part II: Metaheuristics," Transportation Science, INFORMS, vol. 39(1), pages 119-139, February.
    11. K H Kim & M J Lee, 2007. "Scheduling trucks in local depots for door-to-door delivery services," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(9), pages 1195-1202, September.
    12. Xiaoxu Wei & Zhouru Xiao & Yongsheng Wang, 2024. "Solving the Vehicle Routing Problem with Time Windows Using Modified Rat Swarm Optimization Algorithm Based on Large Neighborhood Search," Mathematics, MDPI, vol. 12(11), pages 1-33, May.
    13. T. Ibaraki & S. Imahori & M. Kubo & T. Masuda & T. Uno & M. Yagiura, 2005. "Effective Local Search Algorithms for Routing and Scheduling Problems with General Time-Window Constraints," Transportation Science, INFORMS, vol. 39(2), pages 206-232, May.
    14. Calvete, Herminia I. & Gale, Carmen & Oliveros, Maria-Jose & Sanchez-Valverde, Belen, 2007. "A goal programming approach to vehicle routing problems with soft time windows," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1720-1733, March.
    15. Liu, Fuh-Hwa Franklin & Shen, Sheng-Yuan, 1999. "A route-neighborhood-based metaheuristic for vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 118(3), pages 485-504, November.
    16. Schneider, M., 2016. "The vehicle-routing problem with time windows and driver-specific times," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65941, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    17. Daniel Schubert & André Scholz & Gerhard Wäscher, 2017. "Integrated Order Picking and Vehicle Routing with Due Dates," FEMM Working Papers 170007, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    18. 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.
    19. Daniel Schubert & André Scholz & Gerhard Wäscher, 2018. "Integrated order picking and vehicle routing with due dates," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 1109-1139, October.
    20. Sungwon Lee & Taesung Hwang, 2018. "Estimating Emissions from Regional Freight Delivery under Different Urban Development Scenarios," Sustainability, MDPI, vol. 10(4), pages 1-14, April.

    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:inm:ortrsc:v:49:y:2015:i:2:p:223-238. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.