Skip to main content
SpringerLink
Log in

Agent-Based Optimization

  • Chapter
  • First Online:
Population-Based Approaches to the Resource-Constrained and Discrete-Continuous Scheduling

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 108))

  • 615 Accesses

Abstract

Agents and agent-based approaches are an active research topis in artificial intelligence and expert systems.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Genesereth, M.R., Ketchpel, S.P.: Software agents. Commun. ACM 37(7), 48–53 (1994)

    Article  Google Scholar 

  2. Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)

    Article  Google Scholar 

  3. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson Education Inc., Prentice Hall (2010)

    MATH  Google Scholar 

  4. Bellifemine, F., Caire, G., Greenwood, D.: Developing Multi-agent Systems with JADE. Wiley, Chichester (2007)

    Book  Google Scholar 

  5. Wooldridge, M.: Agent-based computing. Interoper. Commun. Netw. 1, 71–98 (1998)

    Google Scholar 

  6. Niazi, M., Hussain, A.: Agent-based computing from multi-agent systems to agent-based models: a visual survey. Scientometrics 89(2), 479–499 (2011)

    Article  Google Scholar 

  7. Weiss, G. (ed.): Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge, MA (1999)

    Google Scholar 

  8. Wooldridge, M.: An Introduction to Multiagent Systems, 2nd edn. Wiley (2009)

    Google Scholar 

  9. Madejski, J.: Survey of the agent-based approach to intelligent manufacturing. J. Achiev. Mater. Manuf. Eng. 21(1), 67–70 (2007)

    Google Scholar 

  10. Balaji, P.G., Srinivasan, D.: An introduction to multi-agent systems. In: Innovations in Multi-agent Systems and Applications — 1, Studies in Computational Intelligence, vol. 310, pp. 1–27 (2010)

    Google Scholar 

  11. Vlassis N.: A concise introduction to multiagent systems and distributed artificial intelligence. In: Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool (2007)

    Google Scholar 

  12. Burke, E.K., Graham Kendall, G.: Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer, US (2014)

    Book  MATH  Google Scholar 

  13. Jennings, N.R., Wooldridge, M.: Applications of intelligent agents. In: Jennings, N.R., Wooldridge, M.J. (eds.) Agent Technology: Foundations, Applications, and Markets, pp. 3–28. Springer, Berlin (1998)

    Chapter  Google Scholar 

  14. Jennings, N.: The archon system and its applications. In: Proceedings of the 2nd International Working Conference on Cooperating Knowledge Based Systems (CKBS-94), pp. 13–29. Dake Centre, University of Keele, UK (1994)

    Google Scholar 

  15. Albert, M., Laengle, T., Woern, H., Capobianco, M., Brighenti, A.: Multi-agent systems for industrial diagnostics. In: Proceedings of 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, pp. 483–488, Washington, DC (2003)

    Google Scholar 

  16. Neagu, N., Dorer, K., Greenwood, D., Calisti, M.: LS/ATN: reporting on a successful agent-based solution for transport logistics optimization. In: Proceedings of the IEEE 2006 Workshop on Distributed Intelligent Systems (WDIS06), Prague (2006)

    Google Scholar 

  17. Greenwood, D., Vitaglione, G., Keller, L., Calisti, M.: Service level agreement management with adaptive coordination. In: Proceedings of the International Conference on Networking and Services (ICNS06), Silicon Valley, USA (2006)

    Google Scholar 

  18. Johnson, P.G., Balke, T., Kotthoff, L.: Integrating optimisation and agent-based modelling. In: ECMS — Proceedings 28th European Conference on Modelling and Simulation, pp. 775–781. Digitaldruck Pirrot GmbH, Germany (2014)

    Google Scholar 

  19. Parunak, H.V.D., Kindrick, J., Irish, B.W.: A conservative domain for neural connectivity and propagation. In: Proceedings of Proceedings of the 6th National Conference on Artificial Intelligence (AAAI’87). Distributed artificial intelligence, pp. 307–311. Pitman, London (1987)

    Google Scholar 

  20. Sirikijpanichkul, A., van Dam, K.H., Ferreira, L., Lukszo, Z.: Optimizing the location of intermodal freight hubs: an overview of agent based modelling approach. J. Transp. Syst. Eng. Inf. Technol. 7(4), 71–81 (2007)

    Google Scholar 

  21. Ouelhadj, D., Petrovic, S.: A survey of dynamic scheduling in manufacturing systems. J. Sched. 12(4), 417–431 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  22. Böcker, J., Lind, J., Zirkler, B.: Using a multi-agent approach to optimise the train coupling and sharing system. Eur. J. Oper. Res. 131(2), 242–252 (2010)

    Article  MATH  Google Scholar 

  23. Liang, W.Y., Huang, C.C.: Agent-based demand forecast in multi-echelon supply chain. Decis. Support Syst. 42(1), 390–407 (2006)

    Article  Google Scholar 

  24. Barbucha, D., Jędrzejowicz, P.: An agent-based approach to vehicle routing problem. In. J. Appl. Math. Comput. Sci. 4(2), 538–543 (2007)

    Google Scholar 

  25. Polyakovsky, S., M’Hallah, R.: An agent-based approach to the two dimensional guillotine bin packing problem. Eur. J. Oper. Res. 192(31), 767–781 (2009)

    Article  MATH  Google Scholar 

  26. Xie, X.F., Liu, J.: Multiagent optimization system for solving the traveling salesman problem (TSP). IEEE Trans. Syst. Man Cybern. Part B Cybern. 39(2), 489–502 (2009)

    Article  MathSciNet  Google Scholar 

  27. Barbati, M., Bruno, G., Genovese, A.: Applications of agent-based models for optimization problems: a literature review. Expert Syst. Appl. 39, 6020–6028 (2012)

    Article  Google Scholar 

  28. Knotts, G., Dror, M., Hartman, B.C.: Agent-based project scheduling. IIE Trans. 32(5), 387–401 (2000)

    Google Scholar 

  29. Chen, Y.M., Wang, S.C.: Framework of agent-based intelligence system with two stage decision-making process for distributed dynamic scheduling. Appl. Soft Comput. 7(1), 229–245 (2007)

    Article  MathSciNet  Google Scholar 

  30. Xiang, W., Lee, H.P.: Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Eng. Appl. Artif. Intell. 21(1), 73–85 (2008)

    Article  Google Scholar 

  31. Ramos, C.: An architecture and a negotiation protocol for the dynamic scheduling of manufacturing systems. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 8–13 (1994)

    Google Scholar 

  32. Davidsson, P., Holmgren, J., Persson, J.A.: On the integration of agent-based and mathematical optimization techniques. Lect. Notes Artif. Intell. 4496, 1–10 (2007)

    Google Scholar 

  33. Chen, R.S., Tu, M.A.: Development of an agent-based system for manufacturing control and coordination with ontology and RFID technology. Expert Syst. Appl. 36(4), 7581–7593 (2009)

    Article  Google Scholar 

  34. Persson, J.A., Davidsson, P., Johansson, S.J., Wernstedt, F.: Combining agent-based approaches and classical optimization techniques. In: Proceedings of the Third European Workshop on Multi-Agent Systems (EUMAS 2005), pp. 260–269 (2005)

    Google Scholar 

  35. Talukdar, S., Baerentzen, L., Gove, A., De Souza, P.: Asynchronous Teams: Co-operation Schemes for Autonomous, Computer-Based Agents. Technical Report EDRC 18-59-96, Carnegie Mellon University, Pittsburgh (1996)

    Google Scholar 

  36. Talukdar, S., Baerentzen, L., Gove, A., de Souza, P.: Asynchronous teams: cooperation schemes for autonomous agents. J. Heuristics 4(4), 295–332 (1998)

    Article  MATH  Google Scholar 

  37. Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: e-JABAT — An implementation of the web-based A-Team. In: Nguyen, N.T., Jain, L.C. (eds.) Intelligence Agents in the Evolution of Web and Applications. Studies in Computational Intelligence 167, 57–86 (2009)

    Google Scholar 

  38. Jędrzejowicz, P., Wierzbowska, I.: JADE-based A-Team environment. In: Computational Science — ICCS. Lecture Notes in Computer Science, vol. 3993, pp. 719–726 (2006)

    Google Scholar 

  39. Talukdar, S.N., de Souza, P.: Scale efficient organizations. In: IEEE International Conference on Systems, Man, and Cybernetics, Chicago, pp. 1458–1463 (1992)

    Google Scholar 

  40. Talukdar, S., Murthy, S., Akkiraju, R.: Asynchronous teams. In: Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 57, pp. 537–556 (2003)

    Google Scholar 

  41. Correa, R., Gomes, F.C., Oliveira, C., Pardalos, P.M.: A parallel implementation of an asynchronous team to the point-to-point connection problem. Parallel Comput. 29, 447–466 (2003)

    Article  MathSciNet  Google Scholar 

  42. Zhu, Q.: Topologies of agents interactions in knowledge intensive multi-agent systems for networked information services. Adv. Eng. Inform. 20, 31–45 (2006)

    Article  Google Scholar 

  43. Jędrzejowicz, P.: A-Teams and their applications. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. Lecture Notes in Computer Science(LNAI), vol. 5796, pp. 36–50 (2009)

    Google Scholar 

  44. Carle, M.A., Martel, A., Zufferey, N.: Collaborative Agent Teams (CAT) for Distributed Multi-Dimensional Optimization. CIRRELT, CIRRELT-2012-43, (2012)

    Google Scholar 

  45. Talukdar, S.N.: Collaboration rules for autonomous software agents. Decis. Support Syst. 24, 269–278 (1999)

    Article  Google Scholar 

  46. Rachlin, J., Goodwin, R., Murthy, S., Akkiraju, R., Wu, F., Kumaran, S., Das, R.: A-Teams: an agent architecture for optimization and decision-support. In: Papadimitriou, C., Singh, M.P., Müller, J.P. (eds.) ATAL 1998. Lecture Notes in Artificial Intelligence, vol. 1555, pp. 261–276 (1999)

    Google Scholar 

  47. Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak, E., Wierzbowska, I.: JADE-Based A-Team as a tool for implementing population-based algorithms. In: Chen, Y., Abraham, A. (eds.) Intelligent Systems Design and Applications, Jinan Shandong, China, pp. 144–149. IEEE, Los Alamitos (2006)

    Google Scholar 

  48. Talukdar, S.N., Ramesh, V.C.: A multi-agent technique for contingency constrained optimal power flows. IEEE Trans. Power Syst. 9(2), 855–861 (1994)

    Article  Google Scholar 

  49. Avila-Abascal, P., Talukdar, S.N.: Cooperative algorithms and abductive causal networks for the automatic generation of intelligent substation alarm processors. In: Proceedings of ISCAS’96 (1996)

    Google Scholar 

  50. Kao, J.H., Hemmerle, J.S., Prinz, F.B.: Collision avoidance using asynchronous teams. In: 1996 IEEE International Conference on Robotics and Automation, vol. 2, pp. 1093–1100. OMNI Press, USA (1996)

    Google Scholar 

  51. Murthy, S., Rachlin, J., Akkiraju, R., Wu, F.: Agent-based cooperative scheduling. In: Charniak, E.C. (ed.) Constraints and Agents, AAAI Technical Report WS-97-05, pp. 112–117 (1997)

    Google Scholar 

  52. Blum, J., Eskandarian, A.: Enhancing intelligent agent collaboration for flow optimization of railroad traffic. Transp. Res. 36(10), 919–930 (2002)

    Article  Google Scholar 

  53. Chen, S.Y., Talukdar, S. N., Sadeh N. M.: Job-Shop-Scheduling by a team of asynchronous agents. In: IJCAI-93 Workshop on Knowledge-Based Production, Scheduling and Control, Chambery, France (1993)

    Google Scholar 

  54. Aydin, M.E., Fogarty, T.C.: Teams of autonomous agents for job-shop scheduling problems: an experimental study. J. Intell. Manuf. 15, 455–462 (2004)

    Article  Google Scholar 

  55. Aydin, M.: Metaheuristic agent teams for job shop scheduling problems. In: Holonic and Multi-Agent Systems for Manufacturing. Lecture Notes in Computer Science, vol. 4659, pp. 185–194 (2007)

    Google Scholar 

  56. Rachlin, J., Wu, F., Murthy, S., Talukdar, S., Sturzenbecker, M., Akkiraju, R., Fuhrer, R., Aggarwal, A., Yeh, J., Henry, R., Jayaraman, R.: ForestView: a system for integrated scheduling in complex manufacturing domains. IBM Report (1996)

    Google Scholar 

  57. Lee, H., Murthy, S., Haider, W., Morse, D.: Primary production scheduling at steel making industries, IBM Report (1995)

    Google Scholar 

  58. Tsen, C.K.: Solving train scheduling problems using A-Teams. Ph.D. dissertation, Electrical and Computer Engineering Department, CMU, Pittsburgh, PA (1995)

    Google Scholar 

  59. Rabak, C.S., Sichman, J.S.: Using A-Teams to optimize automatic insertion of electronic components. Adv. Eng. Inform. 17, 95–106 (2003)

    Article  Google Scholar 

  60. Czarnowski, I., Jędrzejowicz, P.: Agent-based NON-distributed and distributed clustering. In: Perner, P. (ed.) Machine Learning and Dara Mining in Pattern Recognition. Lecture Notes in Artificial Intelligence, vol. 5632, pp. 347–360. Springer, Berlin, Heidelberg (2009)

    Chapter  Google Scholar 

  61. Jędrzejowicz, P., Wierzbowska, I. Parallel cooperating A-Teams solving instances of the euclidean planar traveling salesman problem. In: J. O’Shea et al. (eds.) Agent and Multi Agent Systems: Technologies and Applications. Lecture Notes in Artificial Intelligence, vol. 6682, pp. 456–465 (2011)

    Google Scholar 

  62. Carle, M.A., Martel, A., Zufferey, N.: The CAT metaheuristic for the solution of multi-period activity-based supply chain network design problems. Int. J. Prod. Econ. 139(2), 664–677 (2012)

    Article  Google Scholar 

  63. Barbucha, D.: Experimental Study of the Population Parameters Settings in Cooperative Multi-agent System Solving Instances of the VRP. In: Transactions on Computational Collective Intelligence IX. Lecture Notes in Computer Science, vol. 7770, pp. 1–28 (2013)

    Google Scholar 

  64. Barbucha, D.: A cooperative population learning algorithm for vehicle routing problem with time windows. Neurocomputing 146, 210–229 (2014)

    Article  Google Scholar 

  65. Barbucha, D.: Team of A-Teams approach for vehicle routing problem with time windows. In: Terrazas, G., Otero, F., Masegosa, A. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013), vol. 512, pp. 273–286. Springer International Publishing (2014)

    Google Scholar 

  66. Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: Influence of the working strategy on A-Team performance. In: Szczerbicki, E., Nguyen, N.T. (eds.) Smart Information and Knowledge Management. Studies in Computational Intelligence, vol. 260, pp. 83–102. Springer, Heidelberg (2010)

    Google Scholar 

  67. Bellifemine, F., Caire, G., Poggi, A., Rimassa, G.: JADE. A White Paper, Exp. 3(3), 6–20 (2003)

    Google Scholar 

  68. JADE (Java Agent DEvelopment framework). http://jade.tilab.com/

  69. Barbucha, D., I. Czarnowski, P. Jędrzejowicz, E. Ratajczak-Ropel, I. Wierzbowska: JABAT — an implementation of the A-Team concept. In: Proceedings of the International Multiconference on Computer Science and Information Technology, vol. 1, pp. 235–241. Polskie Towarzystwo Informatyczne, Wisła (2006)

    Google Scholar 

  70. Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: Parallel cooperating A-Teams, In: P.Jędrzejowicz et al. (eds.) Computational Collective Intelligence. Technologies and Applications. Lecture Notes in Artificial Intelligence, vol. 6923, pp. 322–331. Springer, Heidelberg (2011)

    Google Scholar 

  71. Jędrzejowicz, P., Ratajczak-Ropel, E.: A-Team for solving the resource availability cost problem. In: Nguyen, N.T., Hoang, K., Jędrzejowicz, P. (eds.) Computational Collective Intelligence Technologies and Applications. Lecture Notes in Artificial Intelligence, vol. 7654, pp. 443–452 (2012)

    Google Scholar 

  72. Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: Team of A-Teams — A study of the cooperation between program agents solving difficult optimization problems, Agent-Based Optimization. In: Czarnowski, I., Jędrzejowicz, P., Kacprzyk, J. (eds) Studies in Computational Intelligence, vol. 456, pp. 123–142. Springer, Heidelberg (2013)

    Google Scholar 

  73. Jędrzejowicz, P., Ratajczak-Ropel, E.: Reinforcement learning strategy for solving the resource-constrained project scheduling problem by a team of A-Teams. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) Intelligent Information and Database Systems. Lecture Notes in Artificial Intelligence, vol. 8398, pp. 197–206 (2014)

    Google Scholar 

  74. Ren, H., Wang, Y.: A survey of multi-agent methods for solving resource constrained project scheduling problems. In: Proceedings of International Conference on Management and Service Science, vol. 2011, pp. 1–4 (2011)

    Google Scholar 

  75. Jędrzejowicz, P., Ratajczak-Ropel, E.: Agent-Based Approach to Solving the Resource Constrained Project Scheduling Problem. Lecture Notes in Computer Science, vol. 4431, pp. 480–487 (2007)

    Google Scholar 

  76. Jędrzejowicz, P., Ratajczak-Ropel, E.: New generation A-Team for solving the resource constrained project scheduling. In: Proceedings of the Eleventh International Workshop on Project Management and Scheduling, pp. 156–159. Istanbul (2008)

    Google Scholar 

  77. Jędrzejowicz, P., Ratajczak-Ropel, E.: Solving the RCPSP/max problem by the team of agents. In: Hakansson, A., et al. (eds.) Agent and Multi-Agent Systems: Technologies an Applications. Lecture Notes in Artificial Intelligence, vol. 5559, pp. 734–743 (2009)

    Google Scholar 

  78. Jędrzejowicz, P., Ratajczak-Ropel, E.: Team of A-Teams for solving the resource-constrained project scheduling problem. In: Grana, M., Toro, C., Posada, J., Howlett, R., Lakhmi, C.J. (eds.) Advances in Knowledge Based and Intelligent Information and Engineering Systems. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 1201–1210, (2012)

    Google Scholar 

  79. Jędrzejowicz, P., Ratajczak-Ropel, E.: Reinforcement learning strategies for A-Team solving the resource-constrained project scheduling problem. Neurocomputing 146, 301–307 (2014)

    Article  Google Scholar 

  80. Jędrzejowicz, P., Ratajczak-Ropel, E.: Reinforcement Learning Strategy for Solving the MRCPSP by a Team of Agents. In: Neves-Silva, R., Jain, L.C., Howlett, R.J. (eds.) Intelligent Decision Technologies, Proceedings of the 7th KES International Conference on Intelligent Decision Technologies (KES-IDT 2015), pp. 537–548. Springer International Publishing, Switzerland (2015)

    Google Scholar 

  81. Jędrzejowicz, P., Ratajczak-Ropel, E.: PLA Based Strategy for Solving RCPSP by a Team of Agents. J. Univ. Comput. Sci. 22(6), 856–873 (2016)

    MathSciNet  Google Scholar 

  82. Jędrzejowicz P., Ratajczak-Ropel E.: Dynamic cooperative interaction strategy for solving RCPSP by a team of agents. In: Nguyen N.T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) Computational Collective Intelligence. Lecture Notes in Artificial Intelligence, vol. 9875, pp. 454–463 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Ewa Ratajczak-Ropel

Authors
  1. Ewa Ratajczak-Ropel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ewa Ratajczak-Ropel .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Ratajczak-Ropel, E. (2018). Agent-Based Optimization. In: Population-Based Approaches to the Resource-Constrained and Discrete-Continuous Scheduling. Studies in Systems, Decision and Control, vol 108. Springer, Cham. https://doi.org/10.1007/978-3-319-62893-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62893-6_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62892-9

  • Online ISBN: 978-3-319-62893-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation