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Advisor(s)
Abstract(s)
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.
Description
Keywords
Citation
Nabais, J., Negenborn, R. R., Carmona Benítez, R. B., Mendonça, L. F. & Ayala Botto, M. (2014). Hierarchical Model Predictive Control for Multi-Commodity Transportation Networks. In José M. Maestre & Rudy R. Negenborn (eds), Distributed MPC Made Easy (pp. 535-552). Netherlands: Springer.