Hierarchical Model Predictive Control for optimizing intermodal container terminal operations

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.