Abstract
Transportation networks are large scale complex systems spatially distributed whose objective is to deliver commodities at the agreed time and at the agreed location. These networks appear in different domain fields, such as communication, water distribution, traffic, logistics and transportation. A transportation network has at the macroscopic level storage capability (located in the nodes) and transport delay (along each connection) as main features. Operations management at transportation networks can be seen as a flow assignment problem. The problem dimension to solve grows exponentially with the number of existing commodities, nodes and connections. In this work we present a Hierarchical Model Predictive Control (H-MPC) architecture to determine flow assignments in transportation networks, while minimizing exogenous inputs effects. This approach has the capacity to keep track of commodity types while solving the flow assignment problem. A flow decomposition of the main system into subsystems is proposed to diminish the problem dimension to solve in each time step. Each subsystem is managed by a control agent. Control agents solve their problems in a hierarchical way, using a so-called push-pull flow perspective. Further problem dimension reduction is achieved using contracted projection sets. The framework proposed can be easily scaled to network topologies in which hundreds of commodities and connections are present.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
European Gateway Services are a service provided by European Container Terminal (ECT) whose main objective is to create an integrated network of hinterland terminals cooperating to increase the ECT terminals throughput at the Rotterdam port. Neuss Trimodal has been a member of this network since 20 December 2011.
References
R.K. Ahuja, T.L. Magnanti, J.B. Orlin, Network Flows (Prentice Hall, Upper Saddle River, 1993)
A. Alessandri, C. Cervellera, M. Cuneo, M. Gaggero, G. Soncin, Modeling and feedback control for resource allocation and performance analysis in container terminals. IEEE Trans. Intell. Transp. Syst. 9(4), 601–614 (2008)
E.F. Camacho, C. Bordons, Model Predictive Control in the Process Industry (Springer, Berlin, 1995)
T.G. Crainic, K.H. Kim, Intermodal transportation, in Transportation, Handbooks in Operations Research and Management Science, ed. by C. Barnhart, G. Laporte (Elsevier, North-Holland, 2007), pp. 467–537
ECT Publications. Fast forward 52, Winter 2011
A. Hegyi, B. De Schutter, J. Hellendoorn, Optimal coordination of variable speed limits to supress schock waves. IEEE Trans. Intell. Transp. Syst. 11(1), 102–112 (2005)
M. Kvasnica, P. Grieder, M. Baotić. Multi-parametric toolbox (mpt). http://control.ee.ethz.ch/~mpt/
S. Leirens, C. Zamora, R.R. Negenborn, B. De Schutter, Coordination in urban water supply networks using distributed model predictive control, in Proceedings of the 2010 American Control Conference (ACC10), pp. 3957–7104, Baltimore, Maryland, June 2010
J.M. Maciejowski, Predictive Control with Constraints (Prentice-Hall, Harlow, 2002)
J.M. Maestre, D. Mu noz de la Pena, E.F. Camacho, Distributed mpc: a supply chain case, in 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, pp. 7099–7104, Shanghai, China, Dec 2009
J.L. Nabais, R.R. Negenborn, M.A. Botto, Hierarchical model predictive control for optimizing intermodal container terminal operations, Submitted to a conference 2012
J.L. Nabais, R.R. Negenborn, M.A. Botto, A novel predictive control based framework for optimizing intermodal container terminal operations, in Proceedings of the 3rd International Conference on Computational Logistics (ICCL), pp. 53–71, Shanghai, China, September 2012
R.R. Negenborn, Z. Lukszo, H. Hellendoorn (eds.), Intelligent Infrastructures (Springer, Dordrecht, 2010)
R.R. Negenborn, P.J. Van Overloop, T. Keviczky, B. De Schutter, Distributed model predictive control of irrigation canals. Netw. Heterogen. Media 4(1), 359–380 (2009)
J. Ottjes, H. Veeke, M. Duinkerken, J. Rijsenbrij, G. Lodewijks, Simulation of a multiterminal system for container handling, in Container Terminals and Cargo Systems, edited by K. Hwan Kim, H.-O. Gunther (Springer, Berlin, 2007), pp. 15–36
M.E. Sezer, D.D. Šiljak, Decentralized control, in The Control Handbook, edited by W.S. Levine (CRC Press, New York, 1996), pp. 779–793
C.A. Silva, J.M.C. Sousa, T.A. Runkler, J.M.G. Sá da Costa, Distributed supply chain management using ant colony optimization. Eur. J. Oper. Res. 199, 349–358 (2009)
Acknowledgments
This work was supported by the Portuguese Government, through Fundação para a Ciência e a Tecnologia, under the project PTDC/EEACRO/102102/2008 - AQUANET, through IDMEC under LAETA and by the VENI project “Intelligent multi-agent control for flexible coordination of transport hubs” (project 11210) of the Dutch Technology Foundation STW, a subdivision of the Netherlands Organisation for Scientific Research (NWO).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Nabais, J.L., Negenborn, R.R., Carmona-Benítez, R.B., Mendonça, L.F., Botto, M.A. (2014). Hierarchical MPC for Multiple Commodity Transportation Networks. In: Maestre, J., Negenborn, R. (eds) Distributed Model Predictive Control Made Easy. Intelligent Systems, Control and Automation: Science and Engineering, vol 69. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7006-5_33
Download citation
DOI: https://doi.org/10.1007/978-94-007-7006-5_33
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-7005-8
Online ISBN: 978-94-007-7006-5
eBook Packages: EngineeringEngineering (R0)