Wasp-like Agents for Distributed Factory Coordination

  • Vincent A. Cicirello
  • Stephen F. Smith


Agent-based approaches to manufacturing scheduling and control have gained increasing attention in recent years. Such approaches are attractive because they offer increased robustness against the unpredictability of factory operations. But the specification of local coordination policies that give rise to efficient global performance and effectively adapt to changing circumstances remains an interesting challenge. In this paper, we present a new approach to this coordination problem, drawing on various aspects of a computational model of how wasp colonies coordinate individual activities and allocate tasks to meet the collective needs of the nest.

We focus specifically on the problem of configuring parallel multi-purpose machines in a factory to best satisfy product demands over time. Wasp-like computational agents that we call routing wasps act as overall machine proxies. These agents use a model of wasp task allocation behavior, coupled with a model of wasp dominance hierarchy formation, to determine which new jobs should be accepted into the machine's queue. If you view our system from a market-oriented perspective, the policies that the routing wasps independently adapt for their respective machines can be likened to policies for deciding when to bid and when not to bid for arriving jobs.

We benchmark the performance of our system on the real-world problem of assigning trucks to paint booths in a simulated vehicle paintshop. The objective of this problem is to minimize the number of paint color changes accrued by the system, assuming no a priori knowledge of the color sequence or color distribution of trucks arriving in the system. We demonstrate that our system outperforms the bidding mechanism originally implemented for the problem as well as another related adaptive bidding mechanism.

distributed scheduling dynamic scheduling biologically inspired system factory coordination 


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  1. 1.
    A. Bauer, B. Bullnheimer, R. F. Hartl, and C. Strauss, “An ant colony optimization approach for the single machine total tardiness problem,” in CEC99: Proceedings of the Congress on Evolutionary Computation, 1999, pp. 1445-1450.Google Scholar
  2. 2.
    T. Beaumariage and K. Kempf, “Attractors in manufacturing systems with chaotic tendencies,”Presentation at INFORMS-95, New Orleans, TB07.3.html, 1995.Google Scholar
  3. 3.
    R. Beckers, O. E. Holland, and J. L. Deneubourg, “From local actions to global tasks: Stigmergy and collective robotics,” in R. A. Brooks and P. Maes, (eds.), Artificial Life IV: Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems, 1994, pp. 181-189.Google Scholar
  4. 4.
    E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Santa Fe Institute Studies in the Sciences of Complexity, Oxford University Press: Oxford, 1999.Google Scholar
  5. 5.
    E. Bonabeau, M. Dorigo, and G. Theraulaz, “Inspiration for optimization from social insect behaviour,” Nature, vol. 406, pp. 39-42, 2000.Google Scholar
  6. 6.
    E. Bonabeau, A. Sobkowski, G. Theraulaz, and J. L. Deneubourg, “Adaptive task allocation inspired by a model of division of labor in social insects,” in D. Lundh and B. Olsson, (eds.): Bio Computation and Emergent Computing, World Scientific, pp. 36-45, 1997.Google Scholar
  7. 7.
    E. Bonabeau, G. Theraulaz, and J. L. Deneubourg, “Fixed response thresholds and the regulation of division of labor in insect societies,” Bull. Math. Biol., vol. 60, pp. 753-807, 1998.Google Scholar
  8. 8.
    J. Braslaw, Personal communication, Material Sciences Department, Ford Research, 2001.Google Scholar
  9. 9.
    B. Bullnheimer, R. F. Hartl, and C. Strauss, “An improved ant system algorithm for the vehicle routing problem,” Ann. Oper. Res., vol. 89, pp. 319-328, 1999.Google Scholar
  10. 10.
    S. Bussmann, “Agent-oriented programming of manufacturing control tasks,” in Proceedings of the 3rd International Conference on Multi-Agent Systems (ICMAS-1998), 1998, pp. 57-63.Google Scholar
  11. 11.
    M. Campos, E. Bonabeau, G. Theraulaz, and J. Deneubourg, “Dynamic scheduling and division of labor in social insects,” Adapt. Behav., vol. 8, no. 2, pp. 83-96, 2000.Google Scholar
  12. 12.
    Y. J. Cao and Q. H. Wu, “Optimization of control parameters in genetic algorithms: A stochastic approach,” Int. J. Syst. Sci., vol. 30, no. 5, pp. 551-559, 1999.Google Scholar
  13. 13.
    V. A. Cicirello, “A game-theoretic analysis of multi-agent systems for shop floor routing,” Technical Report CMU-RI-TR-01-28, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 2001.Google Scholar
  14. 14.
    V. A. Cicirello and S. F. Smith, “Modeling GA performance for control parameter optimization,” in D. Whitley, D. Goldberg, E. Cant-Paz, L. Spector, I. Parmee, and H. Beyer, (eds.), GECCO-2000: Proceedings of the Genetic and Evolutionary Computation Conference, Las Vegas, NV, 2000, pp. 235-242.Google Scholar
  15. 15.
    V. A. Cicirello and S. F. Smith, “Ant colony control for autonomous decentralized shop floor routing,” in ISADS-2001: International Symposium Autonomous Decentralized Systems, Dallas, TX, 2001, pp. 383-390.Google Scholar
  16. 16.
    V. A. Cicirello and S. F. Smith, “Insect societies and manufacturing,” in The IJCAI-01 Workshop on Artificial Intelligence and Manufacturing, Working Notes, Seattle, WA, 2001, pp. 33-38.Google Scholar
  17. 17.
    V. A. Cicirello and S. F. Smith, “Randomizing dispatch scheduling policies,” in Using Uncertainty Within Computation: Papers from the 2001 AAAI Fall Symposium, Technical Report FS-01-04, North Falmouth, Massachusetts, 2001, pp. 30-37.Google Scholar
  18. 18.
    V. A. Cicirello and S. F. Smith, “Wasp-like agents for distributed factory coordination,” Technical Report CMU-RI-TR-01-39, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 2001.Google Scholar
  19. 19.
    V. A. Cicirello and S. F. Smith, “Wasp nests for self-configurable factories,” in J. P. Müller, E. Andre, S. Sen, and C. Frasson, (eds.), Proceedings of the Fifth International Conference on Autonomous Agents, Montreal, Quebec, Canada, 2001, pp. 473-480.Google Scholar
  20. 20.
    V. A. Cicirello and S. F. Smith, “Amplification of search performance through randomization of heuristics,” in The Eighth International Conference on Principles and Practice of Constraint Programming (CP-02), Ithaca, NY, 2002.Google Scholar
  21. 21.
    J. Collins, M. Tsvetovatyy, B. Mobasher, and M. Gini, “MAGNET: A multi-agent contracting system for plan execution,” in G. F. Luger, (ed.), Proceedings of the Artificial Intelligence and Manufacturing Research Planning Workshop, pp. 63-68, 1998.Google Scholar
  22. 22.
    M. den Besten, T. Stützle, and M. Dorigo, “Ant colony optimization for the total weighted tardiness problem,” in M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, and H. S. Schwefel, (eds.), Proceedings of PPSN-VI, Sixth International Conference on Parallel Problem Solving from Nature, Lecture Notes in Computer Science, vol. 1917, 2000, pp. 611-620.Google Scholar
  23. 23.
    G. Di Caro and M. Dorigo, “AntNet: Distributed stigmergetic control for communications networks,” J. Artif. Intell. Res., vol. 9, pp. 317-365, 1998.Google Scholar
  24. 24.
    M. Dorigo and G. Di Caro, “The ant colony optimization meta-heuristic,” in D. Corne, M. Dorigo, and F. Glover, (eds.), New Ideas in Optimization, McGraw-Hill: New York, 1999, pp. 11-32.Google Scholar
  25. 25.
    M. Dorigo and L. M. Gambardella, “Ant colony system: A cooperative learning approach to the traveling salesman problem,” IEEE Trans. Evolut. Comput., vol. 1, no. 1, pp. 53-66, 1997.Google Scholar
  26. 26.
    M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: Optimization by a colony of cooperating agents,” IEEE Trans. Syst. Man Cyber.-Part B: Cyber., vol. 26, no. 1, pp. 29-41, 1996.Google Scholar
  27. 27.
    A. E. Eiben, R. Hinterding, and Z. Michalewicz, “Parameter control in evolutionary algorithms,” IEEE Trans. Evolut. Comput., vol. 3, no. 2, pp. 124-141, 1999.Google Scholar
  28. 28.
    K. Fischer, J. P. Müller, M. Pischel, and D. Schier, “A model for cooperative transportation scheduling,” in ICMAS-95: Proceedings of the First International Conference on Multi-Agent Systems, 1995, pp. 109-116.Google Scholar
  29. 29.
    T. D. Fitzgerald and S. C. Peterson, “Cooperative foraging and communication in caterpillars,” BioScience, vol. 38, no. 1, pp. 20-25, 1988.Google Scholar
  30. 30.
    P. Forsyth and A. Wren, “An ant system for bus driver scheduling,” Technical Report 97.25, University of Leeds, School of Computer Studies, Presented at the 7th International Workshop on Computer-Aided Scheduling of Public Transport, Boston, July 1997.Google Scholar
  31. 31.
    L. M. Gambardella and M. Dorigo, “Ant colony system hybridized with a new local search for the sequential ordering problem,”INFORMS J. Comput., vol. 12, no. 3, pp. 237-255, 2000.Google Scholar
  32. 32.
    L. M. Gambardella, E. Taillard, and G. Agazzi, “MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows,” in D. Corne, M. Dorigo, and F. Glover, (eds.), New Ideas in Optimization, McGraw-Hill: New York, 1999, pp. 63-76.Google Scholar
  33. 33.
    S. Y. Goldsmith and L. D. Interrante, “An autonomous manufacturing collective for job shop scheduling,” in G. F. Luger, (ed.), Proceedings of the Artificial Intelligence and Manufacturing Research Planning Workshop, 1998, pp. 69-74.Google Scholar
  34. 34.
    D. M. Gordon, “The organization of work in social insect colonies,” Nature, vol. 380, pp. 121-124, 1996.Google Scholar
  35. 35.
    K. Kempf and T. Beaumariage, “Chaotic behavior in manufacturing systems,” in AAAI-94 Workshop Program, Reasoning About the Shop Floor, Workshop Notes, 1994, pp. 82-96.Google Scholar
  36. 36.
    W. H. Kirchner and W. F. Towne, “The sensory basis of the honeybee's dance language,” Sci. Am., vol. 270, no. 6, pp. 74-80, 1994.Google Scholar
  37. 37.
    K. Kuwabara and T. Ishida, “Equilibratory approach to distributed resource allocation: toward coordinated balancing,” in C. Castelfranchi and E. Werner, (eds.), Artificial Social Systems: 4th European Workshop on Modelling Autonomous Agents in a Multi-Agent World (Selected Papers), 1992, pp. 133-146.Google Scholar
  38. 38.
    G. Y. J. Lin and J. J. Solberg, “Integrated shop floor control using autonomous agents,” IIE Trans., vol. 24, no. 3, pp. 57-71, 1992.Google Scholar
  39. 39.
    J. S. Liu, “Coordination of multiple agents in distributed manufacturing scheduling,” Ph.D. thesis, The Robotics Institute Carnegie Mellon University, Pittsburgh, PA, 1996.Google Scholar
  40. 40.
    D. Merkle, M. Middendorf, and H. Schmeck, “Ant colony optimization for resource-constrained project scheduling,” in GECCO-2000: Proceedings of the Genetic and Evolutionary Computation Conference, 2000, pp. 893-900.Google Scholar
  41. 41.
    D. Morley, “Painting trucks at general motors: The effectiveness of a complexity-based approach,” in Embracing Complexity: Exploring the Application of Complex Adaptive Systems to Business, The Ernst and Young Center for Business Innovation, 1996, pp. 53-58.Google Scholar
  42. 42.
    D. Morley and C. Schelberg, “An analysis of a plant-specific dynamic scheduler,” in Proceedings of the NSF Workshop on Dynamic Scheduling, 1993, pp. 115-122.Google Scholar
  43. 43.
    T. E. Morton and D. W. Pentico, Heuristic Scheduling Systems: With Applications to Production Systems and Project Management, John Wiley and Sons, 1993.Google Scholar
  44. 44.
    S. Nouyan, “Agent-based approach to dynamic task allocation,” in M. Dorigo, G. Di Caro, and M. Sampels, (eds.), Ant Algorithms: Third International Workshop, ANTS 2002, Proceedings, Lecture Notes in Computer Science, vol. LNCS 2463, 2002, pp. 28-39.Google Scholar
  45. 45.
    P. S. Ow, S. F. Smith, and R. Howie, “A cooperative scheduling system,” in M. D. Oliff, (ed.), Expert Systems and Intelligent Manufacturing, Elsevier Science Publishing Co., Inc., 1988, pp. 43-56.Google Scholar
  46. 46.
    V. Parunak, A. Baker, and S. Clark, “The AARIA agent architecture: From manufacturing requirements to agent-based system design,” in Proceedings of the ICAA'98 Workshop on Agent-Based Manufacturing, 1998.Google Scholar
  47. 47.
    R. J. Rabelo and L. M. Camarinha-Matos, “Negotiation in multi-agent based dynamic scheduling,” Robot. Comput.-Integrat. Manuf., vol. 11, no. 4, pp. 303-309, 1994.Google Scholar
  48. 48.
    R. Schoonderwoerd, O. Holland, and J. Bruten, “Ant-like agents for load balancing in telecommunications networks,” in Agents '97, Proceedings of the First International Conference on Autonomous Agents, 1997, pp. 209-216.Google Scholar
  49. 49.
    R. Schoonderwoerd, O. Holland, J. Bruten, and L. Rothkrantz, “Ant-based load balancing in telecommunications networks,” Adapt. Behav., vol. 5, no. 2, pp. 169-207, 1997.Google Scholar
  50. 50.
    T. Stützle, “An ant approach to the flow shop problem,” in Proceedings of the 6th European Congress on Intelligent Techniques & Soft Computing (EUFIT'98), vol. 3, pp. 1560-1564, 1998.Google Scholar
  51. 51.
    K. P. Sycara, S. F. Roth, N. Sadeh, and M. S. Fox, “Resource allocation in distributed factory scheduling,” IEEE Expert, vol. 6, no. 1, pp. 29-40, 1991.Google Scholar
  52. 52.
    G. Theraulaz, E. Bonabeau, and J. L. Deneubourg, “Self-organization of hierarchies in animal societies: The case of the primitively eusocial wasp polistes dominulus christ,” J. Theor. Biol., vol. 174, pp. 313-323, 1995.Google Scholar
  53. 53.
    G. Theraulaz, E. Bonabeau, and J. L. Deneubourg, “Response threshold reinforcement and division of labour in insect societies,” Proc. R. Soc. London B, vol. 265, no. 1393, pp. 327-335, 1998.Google Scholar
  54. 54.
    G. Theraulaz, S. Goss, J. Gervet, and J. L. Deneubourg, “Task differentiation in polistes wasp colonies: A model for self-organizing groups of robots,” in From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior, 1991, pp. 346-355.Google Scholar
  55. 55.
    S. van der Zwaan and C. Marques, “Ant colony optimisation for job shop scheduling,” in Proceedings of the '99 Workshop on Genetic Algorithms and Artficial Life GAAL'99, 1999.Google Scholar
  56. 56.
    W. E. Walsh, M. P. Wellman, P. R. Wurman, and J. K. MacKie-Mason, “Some economics of market-based distributed scheduling,” in Proceedings of the Eighteenth International Conference on Distributed Computing Systems, 1998, pp. 612-621.Google Scholar
  57. 57.
    M. Wellman, “A general-equilibrium approach to distributed transportation planning,” in AAAI-92: Proceedings of the Tenth National Conference on Artificial Intelligence, 1992, pp. 282-289.Google Scholar
  58. 58.
    S. J. Wu and P. T. Chow, “Genetic algorithms for nonlinear mixed discrete-integer optimization problems via meta-genetic parameter optimization,” Eng. Optim., vol. 24, no. 2, pp. 137-159, 1995.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Vincent A. Cicirello
    • 1
  • Stephen F. Smith
    • 2
  1. 1.Department of Computer ScienceDrexel UniversityPhiladelphiaUSA
  2. 2.The Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

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