Browsing by Author "Negenborn, R. R."
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- Achieving Transport Modal Split Targets at Intermodal Freight Hubs Using a Model Predictive ApproachPublication . Nabais, João; Negenborn, R. R.; Carmona Benítez, R. B.; Ayala Botto, M.The increase of international freight commerce is creating pressure on the existing transport network. Cooperation between the different transport parties (e.g., terminal managers, forwarders and transport providers) is required to increase the network throughput using the same infrastructure. The intermodal hubs are locations where cargo is stored and can switch transport modality while approaching the final destination. Decisions regarding cargo assignment are based on cargo properties. Cargo properties can be fixed (e.g., destination, volume, weight) or time varying (remaining time until due time or goods expiration date). The intermodal hub manager, with access to certain cargo information, can promote cooperation with and among different transport providers that pick up and deliver cargo at the hub. In this paper, cargo evolution at intermodal hubs is modeled based on a mass balance, taking into account hub cargo inflows and outflows, plus an update of the remaining time until cargo due time. Using this model, written in a state-space representation, we propose a model predictive approach to address the Modal Split Aware – Cargo Assignment Problem (MSA–CAP). The MSA–CAP concerns the cargo assignment to the available transport capacity such that the final destination can be reached on time while taking into consideration the transport modality used. The model predictive approach can anticipate cargo peaks at the hub and assigns cargo in advance, following a push of cargo towards the final destination approach. Through the addition of a modal split constraint it is possible to guide the daily cargo assignment to achieve a transport modal split target over a defined period of time. Numerical experiments illustrate the validity of these statements.
- A Constrained MPC Heuristic to Achieve a Desired Transport Modal Split at Intermodal HubsPublication . Nabais, João; Negenborn, R. R.; Carmona Benítez, R. B.; Ayala Botto, MiguelIntermodal hubs are a component of freight transportation networks that have as main goal to deliver cargo at the agreed time and at the agreed location. Currently, authorities are forcing transport operators to act in more sustainable ways. For intermodal hubs this is translated into making a preferable choice for sustainable transport modalities. In some cases, this is no longer a choice and is imposed on the intermodal hub in terms of a desired transport modal split. In this paper, a heuristic based on Model Predictive Control (MPC) to achieve a desired transport modal split at intermodal hubs is proposed. A terminal state constraint is used for the quantity of cargo assigned per modality over the prediction horizon to guide the cargo assignment. Feasibility of the optimization problem and cargo delivery at the agreed time are assured by relaxing the terminal state constraint. The proposed heuristic can anticipate the transport of cargo due to the inclusion of predictions, leading to a push of cargo towards the final destination. As cargo is moving in anticipation to the due time the transport is more robust to unforseen events, such as jams and weather conditions. The proposed heuristic is a step towards sustainable and synchromodal transportation networks. Simulation experiments illustrate the validity of these statements.
- Cooperative Relations Among Intermodal hubs and Transport Providers at Freight Networks Using an MPC ApproachPublication . Nabais, João; Negenborn, R. R.; Carmona Benítez, R. B.; Ayala Botto, MiguelFreight networks are more exposed to unforeseen events leading to delays compromising the delivery of cargo on time. Cooperation among different parties present at freight networks are required to accommodate the occurrence of delays. Cargo assignment to the available transport capacity at the terminal is addressed using a Model Predictive approach in this paper, taking into consideration the final destination and the remaining time until due time of cargo. A cooperative framework for transport providers and intermodal hubs is proposed in this paper. The cooperation is based on information exchange regarding the amount of cargo at risk of not reaching the destination on time. The terminal searches for a faster connection at the terminal to allocate the cargo at risk such that the final destination is reached on time. The proposed heuristic is a step towards sustainable and synchromodal transportation networks. Simulation experiments illustrate the validity of these statements.
- Hierarchical Model Predictive Control for Multi-Commodity Transportation NetworksPublication . Nabais, João; Negenborn, R. R.; Carmona Benítez, R. B.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.
- Hierarchical Model Predictive Control for Optimizing Intermodal Container Terminal OperationsPublication . Nabais, João; Negenborn, R. R.; Ayala Botto, MiguelTransportation 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
- Model Predictive Control for a Sustainable Transport Modal Split at Intermodal Container HubsPublication . Nabais, João; Negenborn, R. R.; Ayala Botto, MiguelThe increase of international commerce and the expected container vessels capacity with 18, 000 TEU (twentyfoot equivalent unit) will put a considerable pressure on container hubs. High flow peaks will appear at gateway hubs in the transport network compromising the cargo transportation towards the hinterland and decreasing the network transport capacity. Moreover, authorities are forcing transport operators to operate in more sustainable ways. For container hubs this is translated into making a preferable choice for barge and train modalities before opting for truck modality. In this work we present a framework based on Model Predictive Control (MPC) to address the so-called transport modal split problem for the outgoing cargo at container hubs. We use two features (destination and due time) to categorize the cargo present at a container hub and develop a dynamic model to make predictions of cargo volume over time. The controller decision takes into account transporting cargo towards the final destination while opting for sustainable transport modalities. The approach is able to assign cargo in advance to the existing connections at the hub in order to overcome predicted cargo peaks in the future. The framework can also be used to choose between different connection schedules. Giving decision freedom to container hubs is a step towards a synchromodal and more flexible transport network. These statements are illustrated with two simulation examples.
- MPC Approach for Synchronized Supply Chains of Perishable GoodsPublication . Hipólito, Tomás; Nabais, João; Carmona Benítez, R. B.; Ayala Botto, Miguel; Negenborn, R. R.The movement of perishable goods is growing worldwide. Perishable goods need to be available to the market before the expiration date. With the decrease in inventory levels the components of a supply chain become even more integrated and dependent on coordinated decisions. Information regarding perishable goods must be visible throughout the supply chain for avoiding goods loss. A Model Predictive Control (MPC) heuristic to address operations management at supply chains of perishable goods is proposed in this paper. The approach is capable to follow the remaining time until expiration date which is critical to avoid losses. The supply chain is modeled using a state-space representation and operations management at the supply chain is formulated as an MPC Problem. In order to cope with operational decisions, the problem is solved on a periodic basis. The proposed approach is capable to deal with production decisions, monitor work-in-progress (WIP), and make transport assignments while monitoring the remaining time until the expiration date. Flows over the supply chain can be synchronized and therefore we named this type of supply chain a Synchronized Supply Chain (SSC). The approach is modular and easily scalable for largescale supply chains. Numerical results illustrate these statements.
- A Multi-agent Control Architecture for Supply Chains Using a Predictive Pull-Flow PerspectivePublication . Nabais, João; Negenborn, R. R.; Carmona Benítez, R. B.; Mendonça, Luís F.; Lourenço, João; Ayala Botto, MiguelSupply chains are large-scale distribution networks in which multiple types of commodities are present. In this paper, the operations management in supply chains is posed as a tracking control problem. All inventory levels in the network should be kept as close as possible to the desired values over time. The supply chain state is disturbed due to client demand at the end nodes. A multiagent control architecture to restore all inventory levels over the supply chain is proposed. First the model for the supply chain is broken down into smaller subsystems using a flow decomposition. The operations management for each subsystem will be decided upon by a dedicated control agent. The control agents solve their problems using a pull-flow perspective, starting at the end nodes and then propagating upstream. Adding new components to the supply chain will have as a consequence the inclusion of more control agents. The proposed architecture is easily scalable to large supply chains due to its modular feature. The multi-agent control architecture performance is illustrated using a supply chain composed of four levels (suppliers, consolidation, distribution, end nodes) using different levels of predictions about client demands. With the increase of prediction demand accuracy the proposed control architecture is able to keep the desired inventory level at the end nodes over time, which makes it suitable for use for just in time production strategies.
- A Multi-Agent MPC Scheme for Vertically Integrated Manufacturing Supply ChainsPublication . Nabais, João; Negenborn, R. R.; Carmona Benítez, R. B.; Ayala Botto, Miguel
- A Novel Predictive Control Based Framework for Optimizing Intermodal Container Terminal OperationsPublication . Nabais, João; Negenborn, R. R.; Ayala Botto, MiguelDue to the increase in world-wide containerized cargo transport port authorities are facing considerable pressure to increase effi- ciency of existing facilities. Container vessels with 18, 000 TEUs (twentyfoot equivalent units) are expected soon to create high flow peaks at container terminals. In this paper we propose a new framework for managing intermodal container terminals, based on the model predictive control methodology. A model based on queues and container categorization is used by a model predictive controller to solve the handling resource allocation problem in a container terminal in an optimal way, while respecting constraints on resource availability. The optimization of the operations is performed in an integrated way for the whole terminal rather than only for an individual subprocess. Containers are categorized into empty and full containers, and divided in classes according to their final destination. With more detailed information available, like container fi- nal destination, it is possible to establish priorities for the container flows inside the terminal. The order in which the container classes should be loaded into a carrier can now be addressed taking into account the carrier future route. The model ability to track the number of containers per class makes this framework suitable for describing terminals integrated in an intermodal transport network and a valuable tool for coordinating the transport modal shift towards a more sustainable and reliable transport. The potential of the proposed framework is illustrated with simulation studies based on a high-peak flow scenario and for a long-term scheduled scenario.