Abstract
The chapter describes the modeling of a material handling system with the production of individual units in a scheduled order. The units represent the agents in the model and are transported in the system which is abstracted as a directed graph. Since the hindrances of units on their path to the destination can lead to inefficiencies in the production, the blockages of units are to be reduced. Therefore, the units operate in the system by means of local interactions in the conveying elements and indirect interactions based on a measure of possible hindrances. If most of the units behave cooperatively (“socially”), the blockings in the system are reduced.
A simulation based on the model shows the collective behavior of the units in the system. The transport processes in the simulation can be compared with the processes in a real plant, which draws conclusions about the consequences of production based on superordinate planning.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
R. K. Ahuja, T. L. Magnanti, and J. B. Orlin.Network Flows: Theory, Algorithms, and Applications.Prentice Hall, Inc. Upper Saddle River, NJ, USA, 1993.
T. Altiok.Performance Analysis of Manufacturing Systems.Springer, 1997.
D. Armbruster, K. Kaneko, and A. S. Mikhailov, editors.Networks of Interacting Machines: Production Organization in Complex Industrial Systems and Biological Cells.World Scientific Publishing, 2006.
D. Arnold and K. Furmans.Materialfluss in Logistiksystemen.Springer, 4th edition, 2005.
N. Ascheuer, S. O. Krumke, and J. Rambau.Online dial-a-ride problems: Minimizing the completion time.In: Proceedings of the 17th Annual Symposium on Theoretical Aspects of Computer Science, pages 639–650, 2000.
F. L. Baccelli, G. Cohen, and G. J. Olsder.Synchronization and Linearity: An Algebra for Discrete Event Systems.Wiley, 1992.
L. Ben-Naoum, R. Boel, L. Bongaerts, B. De Schutter, Y. Peng, P. Valckenaers, J. Vandewalle, and V. Wertz. Methodologies for discrete event dynamic systems: A survey. Journal of the American Society for Horticultural Science, 36(4):3–14, 1995.
C. Bertelle, A. Dutot, F. Guinand, and D. Olivier. Distribution of agent based simulation with colored ant algorithm.In: A. Verbraeck and W. Krug, editors, Proceedings 14th European Simulation Symposium, pages 766–771. SCS Europe BVBA, 2002.
C. Blum and A. Roli. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35:268–308, 2003.
E. Bonabeau. Social insect colonies as complex adaptive systems. Ecosystems, 1:437–443, 1998.
E. Bonabeau. Agent-based modeling: methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 99 (Suppl. 3):7280–7287, 2002.
E. Bonabeau, M. Dorigo, and G. Theraulaz. Inspiration for optimization from social insect behaviour. Nature, 406:39–42, 2000.
O. Bräysy and M. Gendreau. Vehicle routing problem with time windows, Part I: Route construction and local search algorithms; vehicle routing problem with time windows, Part II: Metaheuristics. Transportation Science, 39:104–139, 2005.
S. Camazine, J.-L. Deneubourg, N. R. Franks, J. Sneyd, G. Theraulaz, and E. Bonabeau.Self-Organization in Biological Systems.Princeton University Press, 2001.
D. Corne, M. Dorigo, and F. Glover.New Ideas in Optimization.McGraw-Hill, 1999.
V. Darley. Towards a Theory of Autonomous, Optimising Agents.PhD thesis, Harvard University, Cambridge, 1999.
V. Darley, D. Sanders, and P. v. Tessin. An agent-based model of a corrugated box factory: The tradeoff between finished-goodstock and on-time-in-full delivery. In: H. Coelho and B. Espinasse, editors, Proc. of the Fifth Workshop on Agent-Based Simulation, pages 17–22,2004.
B. de Schutter and T. van den Boom. Model predictive control for perturbed max-plus-linear systems.Systems and Control Letters, 45(1):21–33, 2002.
B. De Schutter and T. van den Boom. MPC for discrete-event systems with soft and hard synchronisation constraints. International Journal of Control, 76(1):82–94, 2003.
J. Desrosiers, F. Soumis, and M. Desrochers. Routing with time-windows by column generation. Networks, 14:545–565, 1984.
W. Domschke.Logistik: Rundreisen und Touren.Oldenbourg, 1997.
M. Dorigo.Optimization, Learning and Natural Algorithms.PhD thesis, Politecnico di Milano, 1992.
M. Dorigo, E. Bonabeau, and G. Theraulaz. Ant algorithms and stigmergy. Future Generation Computer Systems, 16:851–871, 2000.
M. Dorigo and T. Stützle.Ant Colony Optimization.MIT Press, 2004.
A. Drogoul and J. Ferber. Multi-agent simulation as a tool for modeling societies: Application to social differentiation in ant colonies.In: Selected papers from the 4th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Artificial Social Systems, pages 3–23. Springer, London, 1992.
A. Dussutour, V. Fourcassié, D. Helbing, and J.-L. Deneubourg. Optimal traffic organization in ants under crowded conditions. Nature, 428:70–73, 2004.
K. Furmans. Ein Beitrag zur theoretischen Behandlung von Materialfluβpuffern in Bediensystemnetzwerken.Wissenschaftliche Berichte des Institutes für Fördertechnik und Logistiksysteme der Universität Karlsruhe (TH), 1992.
K. Furmans.Bedientheoretische Methoden als Hilfsmittel der Materialflussplanung.Wissenschaftliche Berichte des Institutes für Fördertechnik und Logistiksysteme der Universität Karlsruhe (TH), 2000.
M. Gendreau and J.-Y. Potvin.Dynamic vehicle routing and dispatching.In: T. Crainic and G. Laporte, editors, Fleet Management and Logistics, pages 115–126. Kluwer, 1998.
C. Godsil and G. Royle. Algebraic Graph Theory. Springer, New York, 2001.
W. Groβeschallau. Materialflussrechnung: Modelle und Verfahren zur Analyse und Berechnung von Materialfluβsystemen. Springer, Berlin, Heidelberg, 1984.
T. Gudehus.Logistik 2.Springer, 2000.
J. Hartwig.Photographs of despatch machines.2006.
D. Helbing. Traffic and related self-driven many-particle systems. Review of Modern Physics, 73:1067–1141, 2001.
D. Helbing. The wonderful world of active many-particle systems. Advances in Solid State Physics, 41:357–368, 2001.
D. Helbing. Modelling supply networks and business cycles as unstable transport phenomena. New Journal of Physics, 5:90.1–90.28, 2003.
D. Helbing.Modeling and optimization of production processes: Lessons from traffic dynamics.In: G. Radons and R. Neugebauer, editors, Nonlinear Dynamics of Production Systems, pages 39–54. Wiley InterScience, 2004.
D. Helbing, L. Buzna, A. Johansson, and T. Werner. Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transportation Science, 39:1–34, 2005.
D. Helbing, I. Farkas, and T. Vicsek.Simulating dynamical features of escape panic. Nature, 407:487–490, 2000.
D. Helbing, I. J. Farkas, P. Mol\’anr, and T. Vicsek. Simulation of pedestrian crowds in normal and evacuation situations.In: M. Schreckenberg and S. D. Sarma, editors, Pedestrian and Evacuation Dynamics, pages 21–58. Springer, Berlin, 2002.
D. Helbing, I. J. Farkas, and T. Vicsek. Freezing by heating in a driven mesoscopic system. Phys. Rev. Lett., 84(6):1240–1243, 2000.
D. Helbing, S. Lämmer, and J.-P. Lebacque. Self-organized control of irregular or perturbed network traffic.In: C. Deissenberg and R. F. Hartl, editors, Optimal Control and Dynamic Games, pages 239–274. Springer, Dordrecht, 2005.
W. Hopp and M. Spearman. Factory Physics. McGraw-Hill Irwin, 2000.
M. Hüttner. Prognoseverfahren und ihre Anwendung. Walter de Gruyter, Berlin, 1986.
J. J. Jaw, A. R. Odoni, H. N. Psaraftis, and N. H. M. Wilson. Heuristic algorithm for the multi-vehicle advance request dial-a-ride problem with time windows. Transportation Research Part A: Policy and Practice, 20B:243–257, 1986.
V. S. Kouikoglou and Y. A. Phillis. An exact discrete-event model and control policies for production lines with buffers. IEEE Transactions on Automatic Control, 36(5):515–527, 1991.
N. Krivulin.The max-plus algebra approach in modelling of queueing networks.In: Summer Computer Simulation Conference, pages 485–490. The Society for Computer Simulation, 1996.
C. Kube and E. Bonabeau. Cooperative transport by ants and robots. Robotic and Autonomous Systems, 30:85–101, 2000.
P. R. Kumar. Scheduling queueing networks: Stability, performance analysis and design.In: F. P. Kelly and R. J. Williams, editors, IMA Volumes in Mathematic and its Applications, pages 21–70. Springer, New York, 1995.
P. R. Kumar and T. I. Seidman. Dynamic instabilities and stabilization methods in distributed real-time scheduling of manufacturing systems. IEEE Transactions on Automatic Control, 35(3):289–298, 1990.
H. C. Lau, M. Sim, and K. M. Teo. Vehicle routing problem with time windows and a limited number of vehicles. European Journal of Operational Research, 148(3):559–569, 2003.
S. H. Lu and P. R. Kumar. Distributed scheduling based on due dates and buffer priorities. IEEE Transactions on Automatic Control, 36(12):1406–1416, 1991.
S. Lämmer, H. Korib, K. Peters, and D. Helbing. Decentralised control of material or traffic flows in networks using phase-synchronisation. Physica A, 363:39–47, 2006.
G. Mehlhorn, editor.Der Ingenieurbau.Ernst & Sohn, 1995.
R. Möhring, E. Köhler, E. Gawrilow, and B. Stenzel.Conflict-free real-time AGV routing.In: H. Fleuren, D. den Hertog, and P. Kort, editors, Operations Research Proceedings 2004: selected Papers of the Annual International Conference of the GOR, pages 18–24. Springer, 2005.
S. Nahmias. Production and Operations Analysis. McGraw-Hill Irwin, 2001.
G. Nikolakopoulou, S. Kortesis, A. Synefaki, and R. Kalfakakou. Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies. European Journal of Operational Research, 152(2):520–527, 2004.
J. R. Perkins and P. R. Kumar. Stable distributed, real-time scheduling of flexible manufacturing, assembly, disassembly systems. IEEE Transactions on Automatic Control, 34(2):139–148, 1989.
K. Peters, A. Johansson, and D. Helbing. Swarm intelligence beyond stigmergy: Traffic optimization in ants. Künstliche Intelligenz, 4:11–16, 2005.
H. N. Psaraftis. Dynamic programming solution to the single vehicle many-to-many immediate request dial-a-ride problem. Transportation Science, 14:130–154, 1980.
J. Quadrat, M. Akian, G. Cohen, S. Gaubert, and M. Viot.Max-plus algebra and applications to system theory and optimal control.In: Proceedings of the International Congress of Mathematicians 1994, pages 1502–1511. Birkh\"auser, 1995.
S. J. Russell and P. Norvig.Artificial Intelligence: A Modern Approach.Prentice-Hall, 2002.
SCA Packaging Aylesford Ltd.Technische Daten von Verarbeitungsmaschinen, 2002-2005.
SCA Packaging UK & Ireland Ltd., Aylesford.SCA Packaging Product Knowledge Programme, 2003.
T. Seidel.Modellierung von Produktionsnetzwerken aus der Perspektive interagierender Transportprozesse.PhD thesis, Technische Universität Dresden, 2007.
D. J. Stilwell and J. S. Bay. Toward the development of a material transport system using swarms of ant-like robots.In: Robotics and Automation, 1993. Proceedings., 1993 IEEE International Conference on, pages 766–771, 1993.
E.-G. Talbi. A taxonomy of hybrid metaheuristics. Journal of Heuristics, 8:541–564, 2002.
G. Theraulaz and E. Bonabeau. Coordination in distributed buildings. Science, 269:686–688, 1995.
G. Theraulaz and E. Bonabeau. Modelling the collective building of complex architectures in social insects with lattice swarms. Journal of Theoretical Biology, 177:381–400, 1995.
G. Theraulaz and E. Bonabeau. A brief history of stigmergy. Artificial Life, 5(2):97–116, 1999.
G. Theraulaz, E. Bonabeau, and J.-L. Deneubourg. The mechanisms and rules of coordinated building in social insect.In: C. Detrain, J.-L. Deneubourg, and J. Pasteels, editors, Information Processing in Social Insects, pages 309–330. Birkhäuser, Basel, 1999.
G. Weiβ.Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence.MIT Press, 1999.
G. Weiβ. Agent orientation in software engineering. The Knowledge Engineering Review, 16:349–373, 2001.
M. Wooldridge. An Introduction to Multiagent Systems.Wiley & Sons, 2002.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Seidel, T., Hartwig, J., Sanders, R.L., Helbing, D. (2008). An Agent-Based Approach to Self-organized Production. In: Blum, C., Merkle, D. (eds) Swarm Intelligence. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74089-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-74089-6_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74088-9
Online ISBN: 978-3-540-74089-6
eBook Packages: Computer ScienceComputer Science (R0)