Journal of Intelligent Manufacturing

, Volume 28, Issue 1, pp 69–84 | Cite as

Patching process optimization in an agent-controlled timber mill

  • Matthias Wolfgang Hofmair
  • Martin Melik-Merkumians
  • Martin Böck
  • Munir Merdan
  • Georg Schitter
  • Andreas Kugi


Repair and patching of wood defects is a costly process of inline production in timber industry. A large variety of plain as well as laminated wooden products demands for offline human interaction and skilled handcrafting in order to achieve the desired quality of the final products. The EU FP7 project Hol-I-Wood PR demonstrates the transformation of a traditional wood patching line for shuttering panels into a fully automated, flexible patching plant. The focus of this paper is set on the optimization of the different production steps of a patching robot, which comprises optimal patch placement, path planning and trajectory generation. Based on this, the processing time of each workpiece can be accurately estimated. These computations serve as an input for advanced panel scheduling, which assigns panels to one of several identical parallel patching lines in a throughput-optimal manner. In order to ensure high modularity of the components and scalability for various wood mills, an agent-based approach was chosen for the implementation of the automation system.


Wood patching robot Patch placement Polygon covering Path planning Traveling salesman problem Trajectory generation Agent technology 



The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 284573.


  1. Allgöwer, F., Badgwell, T. A., Qin, J. S., Rawlings, J. B., & Wright, S. J. (1999). Nonlinear predictive control and moving horizon estimation—An introductory overview. In P. M. Frank (Ed.), Advances in Control: Highlights of ECC ’99 (pp. 391–449). London, UK: Springer.CrossRefGoogle Scholar
  2. Applegate, D. L., Bixby, R. E., Chvátal, V., & Cook, W. J. (2006). The traveling salesman problem: A computational study. Princeton series in applied mathematics. Princeton, NJ: Princeton University Press.Google Scholar
  3. Barbati, M., Bruno, G., & Genovese, A. (2012). Applications of agent-based models for optimization problems: A literature review. Expert Systems with Applications, 39(5), 6020–6028.CrossRefGoogle Scholar
  4. Biagiotti, L., & Melchiorri, C. (2008). Trajectory planning for automatic machines and robots. Berlin, Heidelberg: Springer.Google Scholar
  5. Bussmann, S., Jennings, N. R., & Wooldridge, M. (2004). Multiagent systems for manufacturing control: A design methodology. Springer series on agent technology. Berlin, Heidelberg: Springer.Google Scholar
  6. Chu, S.-C., Roddick, J.F., Su, C.-J., & Pan, J.-S. (2004). Constrained ant colony optimization for data clustering. In Proceedings of the 8th Pacific Rim international conference on artificial intelligence, Auckland, NZ, pp. 534–543.Google Scholar
  7. Das, G. K., Das, S., Nandy, S. C., & Sinha, B. P. (2006). Efficient algorithm for placing a given number of base stations to cover a convex region. Journal of Parallel and Distributed Computing, 66(11), 1353–1358.CrossRefGoogle Scholar
  8. Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: A new meta-heuristic. In Proceedings of the congress on evolutionary computation, vol. 2, Washington, D.C., USA, pp. 1470–1477.Google Scholar
  9. Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Cambridge, MA: MIT Press.Google Scholar
  10. Eiben, A. E., & Smith, J. E. (2003). Introduction to evolutionary computing. Natural computing series. Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  11. Gambardella, L. M., & Dorigo, M. (2000). An ant colony system hybridized with a new local search for the sequential ordering problem. INFORMS Journal on Computing, 12(3), 237–255.CrossRefGoogle Scholar
  12. Gen, M., & Lin, L. (2013). Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-the-art survey. Journal of Intelligent Manufacturing, 25(5), 849–866, (2013), doi: 10.1007/s10845-013-0804-4.
  13. Gerelli, O., & Guarino Lo Bianco, C. (2010). A discrete-time filter for the on-line generation of trajectories with bounded velocity, acceleration, and Jerk. In Proceedings of the IEEE international conference on robotics and automation, Anchorage, Alaska, USA, pp. 3989–3994.Google Scholar
  14. Guarino Lo Bianco, C., & Ghilardelli, F. (2012). Third order systemfor the generation of minimum-time trajectories with asymmetric bounds on velocity, acceleration, and Jerk. In Proceedings of the international conference on intelligent robots and systems, Vilamoura, Algarve, Portugal, pp. 137–143.Google Scholar
  15. Gutjahr, W. J., & Rauner, M. S. (2007). An aco algorithm for a dynamic regional nurse-scheduling problem in austria. Computers & Operations Research, Special Issue: Logistics of Health Care Management, 34(3), 642–666.CrossRefGoogle Scholar
  16. Hall, N. G., Potts, C. N., & Sriskandarajah, C. (2000). Parallel machine scheduling with a common server. Discrete Applied Mathematics, 102(3), 223–243.CrossRefGoogle Scholar
  17. Hegny, I., Hummer, O., Zoitl, A., Koppensteiner, G., & Merdan, M. (2008). Integrating software agents and IEC 61499 realtime control for reconfigurable distributed manufacturing systems. In Proceedings of the international symposium on industrial embedded systems, La Grande-Motte, FR, pp. 249–252.Google Scholar
  18. Helsgaun, K. (2000). An effective implementation of the lin-kernighan traveling salesman heuristic. European Journal of Operational Research, 126(1), 106–130.CrossRefGoogle Scholar
  19. Helsgaun, K. (2009). General k-opt submoves for the linkernighan tsp heuristic. Mathematical Programming Computation, 1(2–3), 119–163.CrossRefGoogle Scholar
  20. Hu, X.-B., & Chen, W.-H. (2005). Genetic algorithm based on receding horizon control for arrival sequencing and scheduling. Engineering Applications of Artificial Intelligence, 18(5), 633–642.Google Scholar
  21. Hu, X.-B., & Chen, W.-H. (2005). Receding horizon control for aircraft arrival sequencing and scheduling. IEEE Transactions on Intelligent Transportation Systems, 6(2), 189–197.CrossRefGoogle Scholar
  22. Hu, X.-B., Chen, W.-H., & Di Paolo, E. (2007). Multiairport capacity management: Genetic algorithm with receding horizon. IEEE Transactions on Intelligent Transportation Systems, 8(2), 254–263.Google Scholar
  23. Huhns, M. N., Singh, M. P., Burstein, M., Decker, K., Durfee, K. E., Finin, T., et al. (2005). Research directions for service-oriented multiagent systems. IEEE Internet Computing, 9(6), 65–70.CrossRefGoogle Scholar
  24. Jade, Java Agent DEvelopment Framework., 2014. [23 July 2014].
  25. Jennings, N. R., & Sycara, K. (1998). A roadmap of agent research and development, 1998.Google Scholar
  26. Kim, M.-Y., & Lee, Y. H. (2012). Mip models and hybrid algorithm for minimizing the makespan of parallel machines scheduling problem with a single server. Computers & Operations Research, 39(11), 2457–2468.CrossRefGoogle Scholar
  27. Kouiss, K., Pierreval, H., & Mebarki, N. (1997). Using multi-agent architecture in fms for dynamic scheduling. Journal of Intelligent Manufacturing, 8(1), 41–47.CrossRefGoogle Scholar
  28. Kravchenko, S. A., & Werner, F. (1997). Parallel machine scheduling problems with a single server. Mathematical and Computer Modelling, 26(12), 1–11.CrossRefGoogle Scholar
  29. Leitão, P. (2009). Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence, 22(7), 979–991.CrossRefGoogle Scholar
  30. Lepuschitz, W., Zoitl, A., Vallee, M., & Merdan, M. (2011). Toward self-reconfiguration of manufacturing systems using automation agents. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 41(1), 52–69.CrossRefGoogle Scholar
  31. Lin, S. (1965). Computer solutions of the traveling salesman problem. Bell System Technical Journal, 44(6), 2245–2269.CrossRefGoogle Scholar
  32. Melik-Merkumians, M., Baier, T., Steinegger, M., Lepuschitz, W., Hegny, I., & Zoitl, A. (2012). Towards OPC UA as portable SOA middleware between control software and external added value applications. In Proceedings of the IEEE 17th international conference on emerging technologies and factory automation, Krakow, PL, pp. 1–8.Google Scholar
  33. Merdan, M., Moser, T., Wahyudin, D., Biffl, S., & Vrba, P. (2008). Simulation of workflow scheduling strategies using the MAST test management system. In Proceedings of the 10th international conference on control, automation, robotics and vision, Hanoi, Vietnam, pp. 1172–1177.Google Scholar
  34. Mucientes, M., Vidal, J. C., Bugarin, A., & Lama, M. (2008). Processing times estimation in a manufacturing industry through genetic programming. In Proceedings of the 3rd international workshop on genetic and evolving systems, Witten-Bommerholz, GER, pp. 95–100.Google Scholar
  35. Ollinger, L., Zuhlke, D., Theorin, A., & Johnsson, C. (2013). A reference architecture for service-oriented control procedures and its implementation with SysML and Grafchart. In Proceedings of the IEEE 18th conference on emerging technologies factory automation, Cagliari, IT, pp. 1–8.Google Scholar
  36. Or, I., & Duman, E. (1996). Optimization issues in automated production of printed circuit boards: Operations sequencing, feeder configuration and load balancing problems. In Proceedings of the IEEE conference on emerging technologies and factory automation, vol. 1, Kauai, Hawaii, pp. 227–232.Google Scholar
  37. Pěchouček, M., & Mařík, V. (2008). Industrial deployment of multi-agent technologies: Review and selected case studies. Autonomous Agents and Multi-Agent Systems, 17(3), 397–431.CrossRefGoogle Scholar
  38. Ribeiro, L., Barata, J., & Mendes, P. (2008). MAS and SOA: Complementary automation paradigms. In Innovation in manufacturing networks. Springer, NY, USA, pp. 259–268.Google Scholar
  39. Rubinovitz, J., & Wysk, R. A. (1988). Task level off-line programming system for robotic arc welding—An overview. Journal of Manufacturing Systems, 7(4), 293–306.CrossRefGoogle Scholar
  40. Shen, W., Wang, L., & Hao, Q. (2006). Agent-based distributed manufacturing process planning and scheduling: A state-of-the-art survey. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, 36(4), 563–577.CrossRefGoogle Scholar
  41. Sipser, M. (2012). Introduction to the theory of computation. Boston, MA: Cengage Learning.Google Scholar
  42. Tasan, S. O., & Tunali, S. (2008). A review of the current applications of genetic algorithms in assembly line balancing. Journal of Intelligent Manufacturing, 19(1), 49–69.CrossRefGoogle Scholar
  43. Vallée, M., Kaindl, H., Merdan, M., Lepuschitz, W., Arnautovic, E., & Vrba, P. (2009). An automation agent architecture with a reflective world model in manufacturing systems. In Proceedings of the IEEE international conference on systems, man and cybernetics, San Antonio, TX, USA, pp. 305–310.Google Scholar
  44. Vincze, M., Biegelbauer, G., & Pichler, A. (2004). Painting parts automatically at lot size one. In Proceedings of the international workshop on robot sensing, Graz, AUT, pp. 35–40.Google Scholar
  45. Vrba, P., & Mařík, V. (2010). Capabilities of dynamic reconfiguration of multiagent-based industrial control systems. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 40(2), 213–223.CrossRefGoogle Scholar
  46. Williams, R. (1979). The geometrical foundation of natural structure: A source book of design. New York: Dover Publications.Google Scholar
  47. Zambonelli, F., Jennings, N.R., Omicini, A., & Wooldridge, M. (2000). Agent-oriented software engineering for internet applications. In Book coordination of internet agents: Models, technologies and applications. Springer, Heidelberg, GER, pp. 326–346.Google Scholar
  48. Zanasi, R., & Morselli, R. (2002). Third order trajectory generator satisfying velocity, acceleration and jerk constraints. In Proceedings of the international conference on control applications, Vol. 2, Glasgow, Scotland, UK, pp. 1165–1170.Google Scholar
  49. Zhan, Z.-H., Zhang, J., Li, Y., Liu, O., Kwok, S. K., Ip, W. H., et al. (2010). An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem. IEEE Transactions on Intelligent Transportation Systems, 11(2), 399–412.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Matthias Wolfgang Hofmair
    • 1
  • Martin Melik-Merkumians
    • 1
  • Martin Böck
    • 1
  • Munir Merdan
    • 1
  • Georg Schitter
    • 1
  • Andreas Kugi
    • 1
  1. 1.Automation and Control InstituteVienna University of TechnologyViennaAustria

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