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Publication
Hierarchical Model Predictive Control for Optimizing Intermodal Container Terminal Operations
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Advisor(s)
Abstract(s)
Transportation networks are large-scale complex
spatially distributed systems whose purpose is to deliver commodities
at the agreed time and at the agreed location. The
network nodes (terminals, depots or warehouses) can be seen
as the main decision making centers, as there the different
economic actors interact with each other. In particular, the
intermodal container terminal is responsible for storing containers
until they are picked up for transport towards their final
destination. Operations management at intermodal container
terminals can be seen as a flow assignment problem. In this
work we present a Hierarchical Model Predictive Control (HMPC)
framework for addressing flow assignments in intermodal
container terminals. The approach proposed is original due to
its capability to keep track of the container class while solving
a flow assignment problem respecting the available resources.
However, the dimension of the problem to be solved grows with
the number of container classes handled and the number of
available connections at the terminal. A system decomposition
inspired by container flows related to each connection served at
the terminal is proposed to diminish the problem dimension to
solve. The framework proposed is easily scalable to container
terminals where hundreds of container classes and connections
are available. The potential of the proposed framework is
compared to a centralized Model Predictive Control (MPC)
framework and is illustrated with a simulation study based on
a long-term scheduled scenario
Description
Trabalho apresentado em 16th IEEE Conference of Intelligent Transportation Systems (ITSC'13), 2013,Haia, Holanda