Coordinating Competitive Agents in Dynamic Airport Resource Scheduling

  • Xiaoyu Mao
  • Adriaan ter Mors
  • Nico Roos
  • Cees Witteveen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4687)


In real-life multi-agent planning problems, long-term plans will often be invalidated by changes in the environment during or after the planning process. When this happens, short-term operational planning and scheduling methods have to be applied in order to deal with these changed situations. In addition to the dynamic environment, in such planning systems we also have to be aware of sometimes conflicting interests of different parties, which render a centralized approach undesirable. In this paper we investigate two agent-based scheduling architectures where stakeholders are modelled as autonomous agents. We discuss this approach in the context of an interesting airport planning problem: the planning and scheduling of deicing and anti-icing activities. To coordinate the competition between agents over scarce resources, we have developed two mechanisms: one mechanism based on decommitment penalties, and one based on a more traditional (Vickrey) auction. Experiments show that the auction-based mechanism best respects the preferences of the individual agents, whereas the decommitment mechanism ensures a fairer distribution of delay over the agents.


Schedule Problem Coordination Mechanism Auction Mechanism Delay Cost Vickrey Auction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Smith, D.E., Frank, J., Jónsson, A.K.: Bridging the gap between planning and scheduling. Knowl. Eng. Rev. 15(1), 47–83 (2000)CrossRefGoogle Scholar
  2. 2.
    Nisan, N., Ronen, A.: Algorithmic mechanism design. In: STOC 1999. Proceedings of the Thirty-First Annual ACM Symposium on Theory of Computing, pp. 129–140. ACM Press, New York (1999)CrossRefGoogle Scholar
  3. 3.
    Andelman, N., Azar, Y., Sorani, M.: Truthful approximation mechanisms for scheduling selfish related machines. In: Diekert, V., Durand, B. (eds.) STACS 2005. LNCS, vol. 3404, pp. 69–82. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Auletta, V., Prisco, R.D., Penna, P., Persiano, G.: Deterministic truthful approximation mechanisms for scheduling related machines. In: Diekert, V., Habib, M. (eds.) STACS 2004. LNCS, vol. 2996, pp. 608–619. Springer, Heidelberg (2004)Google Scholar
  5. 5.
    Kovács, A.: Fast monotone 3-approximation algorithm for scheduling related machines. In: Brodal, G.S., Leonardi, S. (eds.) ESA 2005. LNCS, vol. 3669, pp. 616–627. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Angel, E., Bampis, E., Pascual, F.: Truthful algorithms for scheduling selfish tasks on parallel machines. Theor. Comput. Sci. 369(1-3), 157–168 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Immorlica, N., Li, L., Mirrokni, V.S., Schulz, A.: Coordination mechanisms for selfish scheduling. In: Deng, X., Ye, Y. (eds.) WINE 2005. LNCS, vol. 3828, pp. 55–69. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Vermeulen, I., Bohte, S., Somefun, D., Poutré, J.L.: Improving patient schedules by multi-agent pareto appointment exchanging. In: CEC/EEE 2006. Proceedings of 2006 IEEE International Conference on E-Commerce Technology, San Francisco, California, p. 9 (June 26-29, 2006)Google Scholar
  9. 9.
    Paulussen, T.O., Jennings, N.R., Decker, K.S., Heinzl, A.: Distributed patient scheduling in hospitals. In: IJCIA 2003, pp. 1224–1232. Morgan Kaufmann, San Francisco (2003)Google Scholar
  10. 10.
    Attanasio, A., Ghiani, G., Grandinetti, L., Guerriero, F.: Auction algorithms for decentralized parallel machine scheduling. Parallel Comput. 32(9), 701–709 (2006)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Lewin, R.: Embracing Complexity: Exploring the Application of Complex Adaptive Systems to Business. Ernst & Young (1996)Google Scholar
  12. 12.
    Parkes, D.C., Ungar, L.H.: An auction-based method for decentralized train scheduling. In: Proceedings of the Fifth International Conference on Autonomous Agents, Montreal, Canada, pp. 43–50. ACM Press, New York (2001)CrossRefGoogle Scholar
  13. 13.
    ’t Hoen, P.J., Poutre, J.A.L.: A decommitment strategy in a competitive multi-agent transportation setting. In: AAMAS 2003, pp. 1010–1011. ACM Press, New York (2003)CrossRefGoogle Scholar
  14. 14.
    Sandholm, T., Lesser, V.: Leveled commitment contracts and strategic breach. Games and Economic Behaviour 25, 212–270 (2001)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Collins, J., Tsvetovas, M., Sundareswara, R., van Tonder, J., Gini, M., Mobasher, B.: Evaluating risk: flexibility and feasibility in multi-agent contracting. In: Agents 1999. Proceedings of the Third International Conference on Autonomous Agents, Seattle, WA, USA, pp. 350–351. ACM Press, Seattle, WA (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Xiaoyu Mao
    • 1
    • 2
  • Adriaan ter Mors
    • 1
    • 3
  • Nico Roos
    • 2
  • Cees Witteveen
    • 3
  1. 1.Almende B.V, 3016 DJ RotterdamThe Netherlands
  2. 2.MICC/IKAT, Universiteit MaastrichtThe Netherlands
  3. 3.EWI, Technische Universiteit DelftThe Netherlands

Personalised recommendations