Hierarchical Model-Based Control for Automated Baggage Handling Systems

  • Alina N. Tarău
  • Bart De Schutter
  • Hans Hellendoorn
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 417)


This chapter presents a unified and extended account of previous work regarding modern baggage handling systems that transport luggage in an automated way using destination-coded vehicles (DCVs). These vehicles transport the bags at high speeds on a network of tracks. To control the route of each DCV in the system we first propose centralized and distributed predictive control methods. This results in nonlinear, nonconvex, mixed integer optimization problems. Therefore, the proposed approaches will be expensive in terms of computational effort. As an alternative, we also propose a hierarchical control framework where at higher control levels we reduce the complexity of the computations by simplifying and approximating the nonlinear optimization problem by a mixed integer linear programming (MILP) problem. The advantage is that for MILP problems, solvers are available that allow us to efficiently compute the global optimal solution. To compare the performance of the proposed control approaches we assess the trade-off between optimality and CPU time for the obtained results on a benchmark case study.


Mixed Integer Linear Programming Model Predictive Control Route Choice Network Controller Outgoing Link 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Atamtürk, A., Savelsbergh, M.: Integer-programming software systems. Annals of Operations Research 140(1), 67–124 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Bemporad, A., Morari, M.: Control of systems integrating logic, dynamics, and constraints. Automatica 35(3), 407–427 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Fay, A.: Decentralized control strategies for transportation systems. In: Proc. IEEE Int. Conf. Control and Automation, Budapest, Hungary, pp. 898–903 (2005)Google Scholar
  4. 4.
    Fletcher, R., Leyffer, S.: Numerical experience with lower bounds for MIQP branch-and-bound. SIAM Journal on Optimization 8(2), 604–616 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Floudas, C.: Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications. Oxford University Press, New York (1995)zbMATHGoogle Scholar
  6. 6.
    Gill, P.E., Murray, W., Wright, M.H.: Practical Optimization. Academic Press, London (1981)zbMATHGoogle Scholar
  7. 7.
    Hall, A., Hippler, S., Skutella, M.: Multicommodity flows over time: Efficient algorithms and complexity. Theoretical Computer Science 379(3), 387–404 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Hallenborg, K., Demazeau, Y.: Dynamical control in large-scale material handling systems through agent technology. In: Proc. 2006 IEEE /WIC/ACM Int. Conf. Intelligent Agent Technology, Hong Kong, China, pp. 637–645 (2006)Google Scholar
  9. 9.
    Langevin, A., Lauzon, D., Riopel, D.: Dispatching, routing, and scheduling of two automated guided vehicles in a flexible manufacturing system. International Journal of Flexible Manufacturing Systems 8(3), 247–262 (1996)CrossRefGoogle Scholar
  10. 10.
    Maciejowski, J.: Predictive Control with Constraints. Prentice Hall, Harlow (2002)Google Scholar
  11. 11.
    de Neufville, R.: The baggage system at Denver: Prospects and lessons. Journal of Air Transport Management 1(4), 229–236 (1994)CrossRefGoogle Scholar
  12. 12.
    Taghaboni, F., Tanchoco, J.M.A.: Comparison of dynamic routeing techniques for automated guided vehicle systems. Int. Journal of Production Research 33(10), 2653–2669 (1995)zbMATHCrossRefGoogle Scholar
  13. 13.
    Tarău, A., De Schutter, B., Hellendoorn, J.: Travel time control of destination coded vehicles in baggage handling systems. In: Proc. 17th IEEE Int. Conf. Control Applications, San Antonio, Texas, USA, pp. 293–298 (2008)Google Scholar
  14. 14.
    Tarău, A., De Schutter, B., Hellendoorn, H.: Receding horizon approaches for route choice control of automated baggage handling systems. In: Proc. European Control Conf. (ECC 2009), Budapest, Hungary, pp. 2978–2983 (2009)Google Scholar
  15. 15.
    Tarău, A., De Schutter, B., Hellendoorn, J.: Decentralized route choice control of automated baggage handling systems. In: Proc. 12th IFAC Symposium on Control in Transportation Systems, Redondo Beach, California, USA, pp. 70–75 (2009)Google Scholar
  16. 16.
    Tarău, A., De Schutter, B., Hellendoorn, J.: Predictive route choice control of destination coded vehicles with mixed integer linear programming optimization. In: Proc. 12th IFAC Symposium on Control in Transportation Systems, Redondo Beach, California, USA, pp. 64–69 (2009)Google Scholar
  17. 17.
    Tarău, A., De Schutter, B., Hellendoorn, J.: DCV route control in baggage handling systems using a hierarchical control architecture and mixed integer linear programming. In: Proc. 3rd Int. Conf. Information Systems, Logistics and Supply Chain (ILS 2010), Casablanca, Morocco (2010)Google Scholar
  18. 18.
    Weyns, D., Holvoet, T.: Architectural design of a situated multiagent system for controlling automatic guided vehicles. Int. Journal Agent Oriented Software Engineering 2(1), 90–128 (2008)CrossRefGoogle Scholar

Copyright information

© Springer London 2012

Authors and Affiliations

  • Alina N. Tarău
    • 1
  • Bart De Schutter
    • 1
  • Hans Hellendoorn
    • 1
  1. 1.Delft Center for Systems and ControlDelft University of TechnologyDelftThe Netherlands

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