Skip to main content
SpringerLink
Log in

An Efficient Route Minimization Algorithm for the Vehicle Routing Problem with Time Windows Based on Agent Negotiation

  • Conference paper
PRIMA 2013: Principles and Practice of Multi-Agent Systems (PRIMA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8291))

Abstract

We present an efficient route minimization algorithm for the vehicle routing problem with time windows. The algorithm uses a generic agent decomposition of the problem featuring a clear separation between the local planning performed by the individual vehicles and the abstract global coordination achieved by negotiation — differentiating the presented algorithm from the traditional centralized algorithms. Novel negotiation semantics is introduced on the global coordination planning level allowing customers to be temporarily ejected from the emerging solution being constructed. This allows the algorithm to efficiently backtrack in situations when the currently processed customer cannot be feasibly allocated to the emerging solution. Over the relevant widely-used benchmarks the algorithm equals the best known solutions achieved by the centralized algorithms in 90.7% of the cases with an overall relative error of 0.3%, outperforming the previous comparable agent-based algorithms.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bachem, A., Hochstättler, W., Malich, M.: The simulated trading heuristic for solving vehicle routing problems. Technical report, Discrete Applied Mathenatics (1996)

    Google Scholar 

  2. Brafman, R.I., Domshlak, C.: From one to many: Planning for loosely coupled multi-agent systems. In: Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pp. 28–35 (2008)

    Google Scholar 

  3. Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows, part I route construction and local search algorithms. Transportation Science 39(1), 104–118 (2005)

    Article  Google Scholar 

  4. Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows, part II metaheuristics. Transportation Science 39(1), 119–139 (2005)

    Article  Google Scholar 

  5. Campbell, A.M., Savelsbergh, M.: Efficient insertion heuristics for vehicle routing and scheduling problems. Transportation Science 38, 369–378 (2004)

    Article  Google Scholar 

  6. Dan, Z., Cai, L., Zheng, L.: Improved multi-agent system for the vehicle routing problem with time windows. Tsinghua Science Technology 14(3), 407–412 (2009)

    Article  Google Scholar 

  7. Davidsson, P., Henesey, L., Ramstedt, L., Törnquist, J., Wernstedt, F.: An analysis of agent-based approaches to transport logistics. Transportation Research Part C: Emerging Technologies 13(4), 255–271 (2005)

    Article  Google Scholar 

  8. Davis, R., Smith, R.G.: Negotiation as a metaphor for distributed problem solving. Artificial Intelligence 20, 63–109 (1983)

    Article  Google Scholar 

  9. Desaulniers, G., Lessard, F., Hadjar, A.: Tabu search, partial elementarity, and generalized k-path inequalities for the vehicle routing problem with time windows. Transportation Science 42(3), 387–404 (2008)

    Article  Google Scholar 

  10. Fischer, K., Müller, J.P., Pischel, M.: Cooperative transportation scheduling: an application domain for dai. Journal of Applied Artificial Intelligence 10, 1–33 (1995)

    Article  Google Scholar 

  11. Gehring, H., Homberger, J.: A two-phase hybrid metaheuristic for the vehicle routing problem with time windows. European Journal of Operational Research 162(1), 220–238 (2005)

    Article  MATH  Google Scholar 

  12. Kalina, P., Vokřínek, J.: Parallel solver for vehicle routing and pickup and delivery problems with time windows based on agent negotiation. In: 2012 IEEE Conference on Systems, Man, and Cybernetics (SMC), pp. 1558–1563 (2012)

    Google Scholar 

  13. Kalina, P., Vokřínek, J., Mařík, V.: The art of negotiation: Developing efficient agent-based algorithms for solving vehicle routing problem with time windows. In: Mařík, V., Lastra, J.L.M., Skobelev, P. (eds.) HoloMAS 2013. LNCS, vol. 8062, pp. 187–198. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  14. Kohout, R., Erol, K.: In-time agent-based vehicle routing with a stochastic improvement heuristic. In: 11th Conference on Innovative Applications of Artificial Intelligence. AAAI/MIT Press (1999)

    Google Scholar 

  15. Komenda, A., Novák, P., Pěchouček, M.: Domain-independent multi-agent plan repair. Journal of Network and Computer Applications (in print, 2013)

    Google Scholar 

  16. Komenda, A., Vokrinek, J., Cap, M., Pechoucek, M.: Developing multiagent algorithms for tactical missions using simulation. IEEE Intelligent Systems 28(1), 42–49 (2013)

    Article  Google Scholar 

  17. Komenda, A., Vokřínek, J., Pěchouček, M.: Plan representation and execution in multi-actor scenarios by means of social commitments. Web Intelligence and Agent Systems 9(2), 123–133 (2011)

    Google Scholar 

  18. Leong, H.W., Liu, M.: A multi-agent algorithm for vehicle routing problem with time window. In: Proceedings of the 2006 ACM Symposium on Applied Computing, SAC 2006, pp. 106–111. ACM, New York (2006)

    Google Scholar 

  19. Lim, A., Zhang, X.: A two-stage heuristic with ejection pools and generalized ejection chains for the vehicle routing problem with time windows. INFORMS Journal on Computing 19(3), 443–457 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  20. Liu, F.-H., Shen, S.-Y.: The fleet size and mix vehicle routing problem with time windows. Operational Research Society 50, 721–732 (1999)

    MATH  Google Scholar 

  21. Lu, Q., Dessouky, M.M.: A new insertion-based construction heuristic for solving the pickup and delivery problem with hard time windows. European Journal of Operational Research 175, 672–687 (2005)

    Article  Google Scholar 

  22. Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical Report C3P Report 826, California Institute of Technology (1989)

    Google Scholar 

  23. Nagata, Y.: Edge assembly crossover for the capacitated vehicle routing problem. In: Cotta, C., van Hemert, J. (eds.) EvoCOP 2007. LNCS, vol. 4446, pp. 142–153. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  24. Nagata, Y., Bräysy, O., Dullaert, W.: A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows. Comput. Oper. Res. 37(4), 724–737 (2010)

    Article  MATH  Google Scholar 

  25. Prescott-Gagnon, E., Desaulniers, G., Rousseau, L.-M.: A branch-and-price-based large neighborhood search algorithm for the vehicle routing problem with time windows. Netw. 54(4), 190–204 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  26. Ren, Y., Dessouky, M., Ordóñez, F.: The multi-shift vehicle routing problem with overtime. Comput. Oper. Res. 37(11), 1987–1998 (2010)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  28. Vokřínek, J., Komenda, A., Pěchouček, M.: Abstract architecture for task-oriented multi-agent problem solving. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 41(1), 31–40 (2011)

    Article  Google Scholar 

  29. Wang, F., Tao, Y., Shi, N.: A survey on vehicle routing problem with loading constraints. In: Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization, CSO 2009, vol. 2, pp. 602–606. IEEE Computer Society, Washington, DC (2009)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligent Systems Group, Czech Technical University in Prague, Czech Republic

    Petr Kalina

  2. Agent Technology Center, Czech Technical University in Prague, Czech Republic

    Jiří Vokřínek

  3. Department of Cybernetics, Czech Technical University in Prague, Czech Republic

    Vladimír Mařík

Authors
  1. Petr Kalina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiří Vokřínek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vladimír Mařík
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dipartimento di Informatica,, Università degli Studi di Torino, via Pessinetto 12,, 10149, Torino, Italy

    Guido Boella

  2. Division of Mathematical Sciences, Nanyang Technological University, Singapore

    Edith Elkind

  3. Department of Information Science, University of Otago, Dunedin, New Zealand

    Bastin Tony Roy Savarimuthu  & Martin K. Purvis  & 

  4. Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands

    Frank Dignum

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kalina, P., Vokřínek, J., Mařík, V. (2013). An Efficient Route Minimization Algorithm for the Vehicle Routing Problem with Time Windows Based on Agent Negotiation. In: Boella, G., Elkind, E., Savarimuthu, B.T.R., Dignum, F., Purvis, M.K. (eds) PRIMA 2013: Principles and Practice of Multi-Agent Systems. PRIMA 2013. Lecture Notes in Computer Science(), vol 8291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44927-7_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-44927-7_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-44926-0

  • Online ISBN: 978-3-642-44927-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation