Skip to main content
Log in

A new branching strategy for time constrained routing problems with application to backhauling

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

In this paper, we explore a new branching strategy for branch-and-bound approaches based on column generation for the vehicle routing problems with time windows. This strategy involves branching on resource variables (time or capacity) rather than on network flow variables. We also examine criteria for selecting network nodes for branching. To test the effectiveness of the branching strategy, we conduct computational experiments on time window constrained vehicle routing problems where backhauling is permitted only after all the shipments to clients have been made. The branching method proved very effective. In cases where time was the more binding constraint, time-based branching succeeded in decreasing the number of nodes explored by two thirds and the total computation time by more than half when compared to flow-based branching. The computational results also show that the overall algorithm was successful in optimally solving problems with up to 100 customers. It produced an average cost decrease of almost 7% when backhauling was permitted as compared to the cost involved when the client and the distributor routes were distinct.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. L.H. Appelgren, A column generation algorithm for a ship scheduling problem, Transportation Science 3(1969)53–58.

    Google Scholar 

  2. L.H. Appelgren, Integer programming methods for a vessel scheduling problem, Transportation Science 5(1971)64–78.

    Google Scholar 

  3. D. Casco, B. Golden and E. Wasil, Vehicle routing with backhauls: models, algorithms, and case studies, in:Vehicle Routing: Methods and Studies, ed. B.L. Golden and A.A. Assad (Elsevier Science, 1988) pp. 127–147.

  4. V. Chvátal,Linear Programming (Freeman, 1983).

  5. I. Deif and L. Bodin, Extension of the Clarke and Wright algorithm for solving the vhicle routing problem with backhauling,Conference on Computer Software Uses in Transportation and Logistics Management, ed. A.E. Kidder, Babson Park, MA (1984) pp.75–96.

    Google Scholar 

  6. M. Desrochers, J. Desrosiers and M. Solomon, A new optimization algorithm for the vehicle routing problem with time windows, Operations Research 40(1992)342–354.

    MathSciNet  Google Scholar 

  7. J. Desrosiers, Y. Dumas, M. Solomon and F. Soumis, Time constrained routing and scheduling, GERAD, École des Hautes Études Commerciales, Montréal, Canada (1992), to appear inHandbooks in Operations Research and Management Science (North-Holland).

  8. J. Desrosiers, F. Soumis, M. Desrochers and M. Sauvé, Routing and scheduling with time windows solved by network relaxation and branch-and-bound on time variables,Computer Scheduling of Public Transport 2, ed. J.-M. Rousseau (Elsevier Science, 1985) pp.451–469.

  9. M. Dror, Note on the complexity of the shortest path models for column generation in VRPTW, Operations Research 42(1994)977–978.

    Google Scholar 

  10. P. Hansen, B. Jaumard and P. Poggi de Aragāo, Un algorithme primal de programmation linéaire généralisée pour les programmes mixtes, Comptes Rendus de l'Académie des Sciences de Paris 313(1991)557–560.

    Google Scholar 

  11. M. Goetschalckx and C. Jacobs-Blecha, The vehicle routing problem with backhauls, European Journal of Operational Research 42(1989)39–51.

    Article  MathSciNet  Google Scholar 

  12. B.L. Golden and A.A. Assad, The simple backhaul problem, Internal memo, Distribution Systems Technologies Inc., Columbia, MD (1984).

    Google Scholar 

  13. N. Maculan, P. Michelon and G. Plateau, Column-generation in linear programming with bounding variable constraints and its application in integer programming, Working Paper ES-268/92, Federal University of Rio de Janeiro, Brazil (1992).

    Google Scholar 

  14. R.E. Marsten, The design of the XMP linear programming library, ACM Transaction on Mathematical Software 7(1981)481–497.

    Article  Google Scholar 

  15. B. Sansó, M. Desrochers, J. Desrosiers, Y. Dumas and F. Soumis, Modeling and solving routing and scheduling problems: GENCOL User Guide, Preliminary Version, GERAD, École des Hautes Études Commerciales, Montréal, Canada (1990).

    Google Scholar 

  16. M. Solomon, Algorithms for the vehicle routing and scheduling problem with time window constraints, Operations Research 35(1987)254–265.

    MathSciNet  Google Scholar 

  17. C. Yano, T. Chan, L. Richter, T. Cutler, K. Murty and D. McGettigan, Vehicle routing at quality stores, Interfaces 17(1987)52–63.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gélinas, S., Desrochers, M., Desrosiers, J. et al. A new branching strategy for time constrained routing problems with application to backhauling. Ann Oper Res 61, 91–109 (1995). https://doi.org/10.1007/BF02098283

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02098283

Keywords

AMS(MOS) subject classification

Navigation