Distributed MPC for autonomous ships on inland waterways with collaborative collision avoidance

This paper presents a distributed solution for the problem of collaborative collision avoidance for autonomous inland waterway ships. A two-layer collision avoidance framework that considers inland waterway traffic regulations is proposed to increase navigational safety for autonomous ships. Our approach allows for modifying traffic rules without changing the collision avoidance algorithm, and is based on a novel formulation of model predictive control (MPC) for collision avoidance of ships. This MPC formulation is designed for inland waterway traffic and can handle complex scenarios. The alternating direction method of multipliers is used as a scheme for exchanging and negotiating intentions among ships. Simulation results show that the proposed algorithm can comply with traffic rules. Furthermore, the proposed algorithm can safely deviate from traffic rules when necessary to increase efficiency in complex scenarios

The Cargo Fare Class Mix problem for an intermodal corridor: revenue management in synchromodal container transportation

The intermodal hinterland transportation of maritime containers is under pressure from port authorities and shippers to achieve a more integrated, efficient network operation. Current optimisation methods in literature yield limited results in practice, though, as the transportation product structure limits the flexibility to optimise network logistics. Synchromodality aims to overcome this by a new product structure based on differentiation in price and lead time. Each product is considered as a fare class with a related service level, allowing to target different customer segments and to use revenue management for maximising revenue. However, higher priced fare classes come with tighter planning restrictions and must be carefully balanced with lower priced fare classes to match available capacity and optimise network utilisation. Based on the developments of intermodal networks in North West European, such as the network of European Gateway Services, the Cargo Fare Class Mix problem is proposed. Its purpose is to set limits for each fare class at a tactical level, such that the expected revenue is maximised, considering the available capacity at the operational level. Setting limits at the tactical level is important, as it reflects the necessity of long-term agreements between the transportation provider and its customers. A solution method for an intermodal corridor is proposed, considering a single intermodal connection towards a region with multiple destinations. The main purpose of the article is to show that using a limit on each fare class increases revenue and reliability, thereby outperforming existing fare class mix policies, such as Littlewood

Real-time Container Transport Planning with Decision Trees based on Offline Obtained Optimal Solutions

Hinterland networks for container transportation require planning methods in order to increase efficiency and reliability of the inland road, rail and waterway connections. In this paper we aim to derive real-time decision rules for suitable allocations of containers to inland services by analysing the solution structure of a centralised optimisation method used offline on historic data. The decision tree can be used in a decision support system (DSS) for instantaneously allocating incoming orders to suitable services, without the need for continuous planning updates. Such a DSS is beneficial, as it is easy to implement in the current practice of container transportation. Earlier proposed centralised methods can find the optimal solution for the intermodal inland transportation problem in retrospect, but are not suitable when information becomes gradually available. The main contributions are threefold: firstly, a structured method for creating decision trees from optimal solutions is proposed. Secondly, an innovative method is used for obtaining multiple equivalent optimal solutions to prevent overfitting of the decision tree. And finally, a structured analysis of three error types is presented for assessing the quality of an obtained tree. A case study illustrates the method’s purpose by comparing the quality of the resulting plan with alternative methods

Service network design for an intermodal container network with flexible due dates/times and the possibility of using subcontracted transport

An intermodal container transportation network is being developed between Rotterdam and several inland terminals in North West Europe: the EUROPEAN GATEWAY SERVICES (EGS) network. This network is developed and operated by the seaports of EUROPE CONTAINER TERMINALS (ECT). To use this network cost-efficiently, a centralized planning of the container transportation is required, to be operated by the seaport. In this paper, a new mathematical model is proposed for the service network design. The model uses a combination of a path-based formulation and a minimum flow network formulation. It introduces two new features to the intermodal network-planning problem. Firstly, overdue deliveries are penalized instead of prohibited. Secondly, the model combines self-operated and subcontracted services. The service network design considers the network-planning problem at a tactical level: the optimal service schedule between the given network terminals is determined. The model considers self-operated or subcontracted barge and rail services as well as transport by truck. The model is used for the service network design of the EGS network. For this case, the benefit of using container transportation with multiple legs and intermediate transfers is studied. Also, a preliminary test of the influence of the new aspects of the model is done. The preliminary results indicate that the proposed model is suitable for the service network design in modern intermodal container transport networks. Also, the results suggest that a combined business model for the network transport and terminals is worth investigating further, as the transit costs can be reduced with lower transfer costs

Impact and relevance of transit disturbances on planning in intermodal container networks

__Abstract__ An intermodal container transportation network is being developed between Rotterdam and several inland terminals in North West Europe: the European Gateway Services network. This network is developed and operated by the sea terminals of Europe Container Terminals (ECT). To use this network cost-efficiently, centralised planning by the sea terminal of the container transportation is required. For adequate planning it is important to adapt to occurring disturbances. In this paper, a new mathematical model is proposed: the Linear Container Allocation model with Time-restrictions (LCAT). This model is used for determining the influence of three main types of transit disturbances on the network performance: early departure, late departure, and cancellation of inland services. The influence of a disturbance is measured in two ways. The impact measures the additional cost incurred by an updated planning in case of a disturbance. The relevance measures the cost difference between a fully updated and a locally updated plan. With the results of the analysis, key service properties of disturbed services that result in a high impact or high relevance can be determined. Based on this, the network operator can select focus areas to prevent disturbances with high impact and to improve the planning updates in case of disturbances with high relevance. In a case study of the EGS network, the impact and relevance of transit disturbances on all network services are assessed

A closed-loop maintenance strategy for offshore wind farms : incorporating dynamic wind farm states and uncertainty-awareness in decision-making

The determination of maintenance strategies is subject to complexity and uncertainty arising from variable offshore wind farm states and inaccuracies in model parameters. The most common method in the existing studies is to adopt an open-loop approach to optimize a maintenance strategy. However, this approach lacks the ability to capture periodic operational state of the wind farm and the awareness of eliminating uncertainty. Consequently, the determined strategy is inadequate to instruct maintenance activities, inducing excessive revenue losses. In this paper, a closed-loop maintenance strategy optimization method is proposed for decision-makers to identify a more profitable manner of wind farm maintenance management. The life-cycle maintenance optimization problem is decomposed into a sequence of sub-optimization problems covering multiple time periods by using a rolling-horizon approach. Each sub-optimization problem is intentionally designed based on the monitored state of the wind farm and the available reliability, availability, and maintainability (RAM) database. Meanwhile, the decision maker consciously mitigates the parameter uncertainty in the maintenance model gradually by updating the current database. Compared to conventional strategies covering the entire lifetime of wind farms, the proposed maintenance strategy is periodically adjusted to provide a series of sub-strategies. The proposed approach was applied in a simulation experiment, a generic small-scale offshore wind farm, to assess its performance. Computational results show that adapting maintenance strategies based on the current state of the wind farm can reduce revenue losses in comparison to conventional open-loop strategies. In addition, the benefits of updating the RAM database in decreasing revenue losses is revealed

Operation and maintenance management for offshore wind farms integrating inventory control and health information

Effective operation and maintenance (O&M) management is significant for enhancing the economic performance of offshore wind farms. Despite recent research progress in O&M, there remains a gap in integrating health information and spare parts inventory into decision-making processes at the scale of offshore wind farms. To bridge this gap, this paper develops an optimisation framework integrating these aspects to establish cost-effective joint maintenance and inventory policies. In the framework, a maintenance policy is firstly developed to plan maintenance actions based on component health and maintenance opportunities. Meanwhile, in order to support maintenance implementation, a multi-echelon inventory network using (,) policies is proposed to store diverse units across distinct warehouses. A genetic algorithm (GA) is then employed to identify the optimal policy, aiming to minimise overall costs. Upon developing the optimisation framework, in order to illustrate the application of the proposed approach in practice, a numerical simulation of a generic offshore wind farm in the North Sea is performed. Results demonstrate that comprehensive O&M management considering interrelationship be-tween maintenance and inventory policies reduces overall costs, showcasing its capacity in strengthening the economic performance. Finally, sensitivity analysis is performed to investigate the most influential O&M factors, providing actionable insights for O&M management

Supervisory hybrid model predictive control for voltage stability of power networks

International audienceEmergency voltage control problems in electric power networks have stimulated the interest for the implementation of online optimal control techniques. Briefly stated, voltage instability stems from the attempt of load dynamics to restore power consumption beyond the capability of the transmission and generation system. Typically, this situation occurs after the outage of one or more components in the network, such that the system cannot satisfy the load demand with the given inputs at a physically sustainable voltage profile. For a particular network, a supervisory control strategy based on model predictive control is proposed, which provides at discrete time steps inputs and set-points to lower-layer primary controllers based on the predicted behavior of a model featuring hybrid dynamics of the loads and the generation system

A Multi-Agent Control Architecture for Supply Chains using a Predictive Pull-Flow Perspective

Com o apoio RAADRI.Supply 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