Applied Intelligence

, Volume 34, Issue 1, pp 47–63 | Cite as

Schedule coordination through egalitarian recurrent multi-unit combinatorial auctions

  • Javier Murillo
  • Víctor Muñoz
  • Dídac BusquetsEmail author
  • Beatriz López


When selfish industries are competing for limited shared resources, they need to coordinate their activities to handle possible conflicting situations. Moreover, this coordination should not affect the activities already planned by the industries, since this could have negative effects on their performance. Although agents may have buffers that allow them to delay the use of resources, these are of a finite capacity, and therefore cannot be used indiscriminately. Thus, we are faced with the problem of coordinating schedules that have already been generated by the agents. To address this task, we propose to use a recurrent auction mechanism to mediate between the agents. Through this auction mechanism, the agents can express their interest in using the resources, thus helping the scheduler to find the best distribution. We also introduce a priority mechanism to add fairness to the coordination process. The proposed coordination mechanism has been applied to a waste water treatment system scenario, where different industries need to discharge their waste. We have simulated the behavior of the system, and the results show that using our coordination mechanism the waste water treatment plant can successfully treat most of the discharges, while the production activity of the industries is almost not affected by it.


Auction mechanisms Schedule coordination Egalitarism 


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  1. 1.
    Arrow KJ, Sen AK, Suzumura K (eds) (2002) Handbook of social choice and welfare. North-Holland, Amsterdam Google Scholar
  2. 2.
    Brucker P, Heitmann S, Hurink J (2003) Flow-shop problems with intermediate buffers. OR Spectr 25:549–574 zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Chevaleyre Y, Dunne P, Endriss U, Lang J, Lemaître M, Maudet N, Padget J, Phelps S, Rodríguez-Aguilar J, Sousa P (2006) Issues in multiagent resource allocation. Informatica 30:3–31 zbMATHGoogle Scholar
  4. 4.
    Chevaleyre Y, Endriss U, Estivie S, Maudet N (2007) Reaching envy-free states in distributed negotiation settings. In: Veloso M (ed) Proceedings of the 20th international joint conference on artificial intelligence (IJCAI-2007), Hyderabad, India, January 2007. pp 1239–1244. Poster paper Google Scholar
  5. 5.
    Clearwater SH (ed) (1996) Market-based control: a paradigm for distributed resource allocation. World Scientific, River Edge. ISBN 981-02-2254-8 Google Scholar
  6. 6.
    Cox J, Durfee E (ed) (2003) Discovering and exploiting synergy between hierarchical planning agents. In: Proc of the second international joint conference on autonomous agents and multiagent systems, pp 281–288 Google Scholar
  7. 7.
    Cox JS, Durfee EH, Bartold T (2005) A distributed framework for solving the multiagent plan coordination problem. In: AAMAS ’05: proceedings of the fourth international joint conference on autonomous agents and multiagent systems, New York, NY, USA, 2005. ACM Press, Cambridge, pp 821–827. ISBN 1-59593-093-0 CrossRefGoogle Scholar
  8. 8.
    Cramton P, Shoham Y, Steinberg R (eds) (2006) Combinatorial auctions. MIT Press, Cambridge zbMATHGoogle Scholar
  9. 9.
    Dias MB, Stentz A (2000) A free market architecture for distributed control of a multirobot system. In: Proceedings of the 6th international conference on intelligent autonomous systems, pp 115–122 Google Scholar
  10. 10.
    Endriss U, Maudet N, Sadri F, Toni F (2003) Resource allocation in egalitarian agent societies. In: Herzig A, Chaib-draa B, Mathieu P (eds) Secondes journées francophones sur les modèles formels d’interaction (MFI-2003). Cépaduès-Éditions, Paris, pp 101–110 Google Scholar
  11. 11.
    Gerkey BP, Matarić MJ (2002) Sold!: Auction methods for multi-robot coordination. IEEE Trans Robot Automat 18(5):758–768 CrossRefGoogle Scholar
  12. 12.
    GLPK. GLPK (GNU Linear Programming Kit),
  13. 13.
    Jeppsson U, Rosen C, Alex J, Copp J, Gernaey K, Pons M-N, Vanroellegem PA (2006) Towards a benchmark simulation model for plant-wide control strategy performance evaluation of wwtps. Water Sci Technol 53(1):287–295 CrossRefGoogle Scholar
  14. 14.
    Kalagnanam J, Parkes D (2005) Auctions, bidding, and exchange design. In: Handbook of supply chain analysis in the e-business era. Kluwer Academic Publishers, Dordrecht Google Scholar
  15. 15.
    Kelly T (2005) Generalized knapsack solvers for multi-unit combinatorial auctions: analysis and application to computational resource allocation. LNAI 3435:73–86 Google Scholar
  16. 16.
    Krauss S (2001) Strategic negotiation in multiagent environments. MIT Press, Cambridge Google Scholar
  17. 17.
    Lee J-S, Szymanski B (2006) Auctions as a dynamic pricing mechanism for e-services. In: Service enterprise integration. Kluwer, Dordrecht, pp 131–156 Google Scholar
  18. 18.
    Modi P, Shen W, Tambe M, Yokoo M (2005) Adopt: Asynchronous distributed constraint optimization with quality guarantees. Artif Intell J 161:149–180 zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Muñoz V, Murillo J (2008) CABRO: Winner determination algorithm for single-unit combinatorial auctions. In: Artificial intelligence research and development (Proceedings of CCIA 2008). IOS Press, Utrecht, pp 303–312 Google Scholar
  20. 20.
    Payne T, David E, Jennings NR, Sharifi M (2006) Auction mechanisms for efficient advertisement selection on public displays. In: Brewka G, Coradeschi S, Perini A, Traverso P (eds) ECAI. IOS Press, Utrecht, pp 285–289 Google Scholar
  21. 21.
    Rendón-Sallard T, Sànchez-Marrè M, Devesa F, Poch M (2006) Simulating scenarios for decision-making in river basin systems through a multi-agent system. In Polit M, Talbert T, López B, Meléndez J (eds) Proceedings of CCIA 2006 Google Scholar
  22. 22.
    REPAST. Repast agent simulation toolkit,
  23. 23.
    Salido MA, Barber F (2006) Distributed csps by graph partitioning. Appl Math Comput 183:491–498 zbMATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    Tonino H, Bos A, Weerdt MD, Witteveen C (2002) Plan coordination by revision in collective agent-based systems. Artif Intell 142(2):121–145 zbMATHCrossRefGoogle Scholar
  25. 25.
    Wellman MP (1993) A market-oriented programming environment and its application to distributed multicommodity flow problems. J Artif Intell Res 1:1–23 zbMATHGoogle Scholar
  26. 26.
    Zweben M, Fox M (1994) Intelligent scheduling. Morgan-Kaufmann, San Mateo Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Javier Murillo
    • 1
  • Víctor Muñoz
    • 1
  • Dídac Busquets
    • 1
    Email author
  • Beatriz López
    • 1
  1. 1.Institut d’Informàtica i AplicacionsUniversitat de GironaGironaSpain

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