Skip to main content
SpringerLink
Log in

A distributed routing concept for vehicle routing problems

  • Original Paper
  • Published:
Logistics Research

Abstract

Traditional solution concepts for the vehicle routing problem (VRP) are pushed to their limits, when applied on dynamically changing vehicle routing scenarios—which are more close to reality than the static formulation. By contrast, the introduced distributed routing concept is designed to match packages and vehicles and to continuously make route decisions especially within a dynamic environment. In this autonomous control concept, each of these objects makes its own decisions. The developed algorithm was entitled Distributed Logistics Routing Protocol (DLRP). But in spite of the restricted suitability of the traditional VRP concepts for dynamic environments, they are still the benchmark for any VRP-similar task. Therefore, we first present a description of the developed DLRP. Then an adapted vehicle routing problem is defined, which both sides, static and dynamic concepts, can cope with. Finally, both concepts are compared using a tabu search algorithm as a well working instance of traditional VRP-concepts. For a quantitative comparison, four solutions are given for the same adapted problem: the optimal solution as a lower bound, the DLRP solution, a tabu search solution and a random-like solution as an upper bound.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Vahrenkamp R, Mattfeld D (2007) Logistiknetzwerke. Gabler, Wiesbaden

    Google Scholar 

  2. Fleischmann B, Gnutzmann S, Sandvoß E (2004) Dynamic vehicle routing based on online traffic information. Transp Sci 38(4):420–433

    Article  Google Scholar 

  3. Jaillet P, Wagner MR (2006) Online routing problems: value of advanced information as improved competitive ratios. Transp Sci 40(2):200–210

    Article  Google Scholar 

  4. Hiller B, Krumke SO, Rambau J (2006) Reoptimization gaps versus model errors in online-dispatching of service units for adac. Discrete Appl Math 154:1897–1907

    Article  MathSciNet  Google Scholar 

  5. Savelsbergh M, Sol M (1998) Drive: dynamic routing of independent vehicles. Oper Res 46:474–490

    Article  Google Scholar 

  6. Bent RW, Van Hentenryck P (2004) Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper Res 52(6):977–987

    Article  Google Scholar 

  7. Perkins CE (2001) Ad hoc networking. Addison-Wesley, Boston

    Google Scholar 

  8. Scholz-Reiter B, Rekersbrink H, Freitag M (2006) Internet routing protocols as an autonomous control approach for transport networks. In: Proceedings of the 5th CIRP international seminar on intelligent computation in manufacturing engineering, pp 341–345

  9. Scholz-Reiter B, Rekersbrink H, Freitag M (2006) Kooperierende Routingprotokolle zur Selbststeuerung von Transportprozessen. Industrie Management 22/3, pp 7–10

  10. Wenning B-L, Rekersbrink H, Timm-Giel A, Görg C, Scholz-Reiter B (2007) Autonomous control by means of distributed routing. In: Understanding Autonomous Cooperation & Control in Logistics—The Impact on Management, Information and Communication and Material Flow. Springer, Berlin, pp 325–336

    Google Scholar 

  11. Wenning B-L, Rekersbrink H, Becker M, Timm-Giel A, Görg C, Scholz-Reiter B (2007) Dynamic transport reference scenarios. In: Understanding autonomous cooperation & control in logistics—the impact on management, information and communication and material flow. Springer, Berlin, pp 337–350

  12. Dantzig GB, Ramser JH (1959) The truck dispatching problem. Manage Sci 6(1):80–91

    Article  MathSciNet  Google Scholar 

  13. Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper Res 35(2):254–265

    Article  MathSciNet  Google Scholar 

  14. Scholz-Reiter B, Rekersbrink H, Wenning, B-L, Makuschewitz T (2008) A survey of autonomous control algorithms by means of adapted vehicle routing problems. In: Proceedings of the 9th Biennial ASME conference on engineering systems design and analysis ESDA 08 (on CD), Haifa, Israel

  15. Laporte G (1992) The vehicle routing problem: an overview of exact and approximative algorithms. Eur J Oper Res 59:345–358

    Article  Google Scholar 

  16. Osman IH (1993) Metastrategy simulated annealing and tabu search for the vehicle routing problem. Ann Oper Res 41:421–451

    Article  Google Scholar 

Download references

Acknowledgments

This research is funded by the German Research Foundation as part of the Collaborative Research Centre 637 ‘Autonomous Cooperating Logistic Processes—A Paradigm Shift and its Limitations’.

Author information

Authors and Affiliations

  1. BIBA an der Universität Bremen, Hochschulring 20, 28359, Bremen, Germany

    Henning Rekersbrink, Thomas Makuschewitz & Bernd Scholz-Reiter

Authors
  1. Henning Rekersbrink
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas Makuschewitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bernd Scholz-Reiter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Henning Rekersbrink.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rekersbrink, H., Makuschewitz, T. & Scholz-Reiter, B. A distributed routing concept for vehicle routing problems. Logist. Res. 1, 45–52 (2009). https://doi.org/10.1007/s12159-008-0003-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12159-008-0003-4

Keywords

Search

Navigation