Logistics Research

, Volume 1, Issue 1, pp 45–52 | Cite as

A distributed routing concept for vehicle routing problems

  • Henning Rekersbrink
  • Thomas Makuschewitz
  • Bernd Scholz-Reiter
Original Paper

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.

Keywords

Vehicle routing problem (VRP) Autonomous control Distributed logistics routing protocol (DLRP) Tabu search Optimisation Routing algorithm Transport logistic 

References

  1. 1.
    Vahrenkamp R, Mattfeld D (2007) Logistiknetzwerke. Gabler, WiesbadenGoogle Scholar
  2. 2.
    Fleischmann B, Gnutzmann S, Sandvoß E (2004) Dynamic vehicle routing based on online traffic information. Transp Sci 38(4):420–433CrossRefGoogle Scholar
  3. 3.
    Jaillet P, Wagner MR (2006) Online routing problems: value of advanced information as improved competitive ratios. Transp Sci 40(2):200–210CrossRefGoogle Scholar
  4. 4.
    Hiller B, Krumke SO, Rambau J (2006) Reoptimization gaps versus model errors in online-dispatching of service units for adac. Discrete Appl Math 154:1897–1907MathSciNetCrossRefGoogle Scholar
  5. 5.
    Savelsbergh M, Sol M (1998) Drive: dynamic routing of independent vehicles. Oper Res 46:474–490CrossRefGoogle Scholar
  6. 6.
    Bent RW, Van Hentenryck P (2004) Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper Res 52(6):977–987CrossRefGoogle Scholar
  7. 7.
    Perkins CE (2001) Ad hoc networking. Addison-Wesley, BostonGoogle Scholar
  8. 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 341–345Google Scholar
  9. 9.
    Scholz-Reiter B, Rekersbrink H, Freitag M (2006) Kooperierende Routingprotokolle zur Selbststeuerung von Transportprozessen. Industrie Management 22/3, pp 7–10Google Scholar
  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 325–336Google Scholar
  11. 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 337–350Google Scholar
  12. 12.
    Dantzig GB, Ramser JH (1959) The truck dispatching problem. Manage Sci 6(1):80–91MathSciNetCrossRefGoogle Scholar
  13. 13.
    Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper Res 35(2):254–265MathSciNetCrossRefGoogle Scholar
  14. 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, IsraelGoogle Scholar
  15. 15.
    Laporte G (1992) The vehicle routing problem: an overview of exact and approximative algorithms. Eur J Oper Res 59:345–358CrossRefGoogle Scholar
  16. 16.
    Osman IH (1993) Metastrategy simulated annealing and tabu search for the vehicle routing problem. Ann Oper Res 41:421–451CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Henning Rekersbrink
    • 1
  • Thomas Makuschewitz
    • 1
  • Bernd Scholz-Reiter
    • 1
  1. 1.BIBA an der Universität BremenBremenGermany

Personalised recommendations