Study of Global Change Impacts on the Inland Navigation Management: Application on the Nord-Pas de Calais Network

AbstractIn a global change context, governments in Europe want to promote alternative transports as inland navigation or railway instead of road transport. As example, in north of France, a shift of 20% from road transport to these alternative transport solutions is expected by 2050. Reaching this goal requires not only the delivery of new infrastructures and equipment, but also the design of efficient management strategies. By focusing on waterborne transport, it is thus necessary to improve the management of the inland navigation networks particularly the water resource. Indeed, the waterborne transport accommodation is strongly linked to the available water resource. This will be a challenging point in a global change context.The paper deals with the global change impacts on inland navigation networks. It aims at proposing new contributions as compared to past and current results of European projects on climate change and inland navigation. It appeared that the multi-scale modeling approach for inland navigation networks that was proposed during the last TRA Conference in Paris in 2014 is useful to determine the resilience of these networks and their ability to guarantee the navigation conditions during drought and flood periods. The proposed tools are developed to consider two space and time scales. The first approach is used to determine the water quantity that is necessary to accommodate the navigation during half a day, and the second allows the efficient control of the gates to keep the water level of each navigation reach close to its setpoint by rejecting disturbances and compensating the waves due to the lock operations. One example based on the real inland navigation network of the north of France is used to highlight the contributions of the multi-scale modeling approach

Producing efficient error-bounded solutions for transition independent decentralized MDPs

pages 539-546International audienceThere has been substantial progress on algorithms for single-agent sequential decision making problems represented as partially observable Markov decision processes (POMDPs). A number of efficient algorithms for solving POMDPs share two desirable properties: error-bounds and fast convergence rates. Despite significant efforts, no algorithms for solving decentralized POMDPs benefit from these properties, leading to either poor solution quality or limited scalability. This paper presents the first approach for solving transition independent decentralized Markov decision processes (MDPs), that inherits these properties. Two related algorithms illustrate this approach. The first recasts the original problem as a finite-horizon deterministic and completely observable Markov decision process. In this form, the original problem is solved by combining heuristic search with constraint optimization to quickly converge into a near-optimal policy. This algorithm also provides the foundation for the first algorithm for solving infinite-horizon transition independent decentralized MDPs. We demonstrate that both methods outperform state-of-the-art algorithms by multiple orders of magnitude, and for infinite-horizon decentralized MDPs, the algorithm is able to construct more concise policies by searching cyclic policy graphs

Distributed Economic Dispatch of Embedded Generation in Smart Grids

In a Smart Grid context, the increasing penetration of embedded generation units leads to a greater complexity in the management of production units. In this arti- cle, we focus on the impact of the introduction of decentralized generation for the unit commitment problem (UC). Unit Commitment Problems consist in finding the optimal schedules and amounts of power to be generated by a set of gen- erating units in response to an electricity demand forecast. While this problem have received a significant amount of attention, classical approaches assume these problems are centralized and deterministic. However, these two assumptions are not realistic in a smart grid context. Indeed, finding the optimal schedules and amounts of power to be generated by multiple distributed generator units is not trivial since it requires to deal with distributed computation, privacy, stochastic planning, ... In this paper, we focus on smart grid scenarios where the main source of complexity comes from the proliferation of distributed generating units. In solving this issue, we consider distributed stochastic unit commitment prob- lems. We introduce a novel distributed gradient descent algorithm which allow us to circumvent classical assumptions. This algorithm is evaluated through a set of experiments on real-time power grid simulator.Programme ADEME - Réseaux électrique intelligent - Projet AgentVP

Constraint satisfaction problem based on flow graph to study the resilience of inland navigation networks in a climate change context

The T-Ten European program aims at optimizing the transport logistics in Europe by promoting alternative transport modes. Navigation transport offers a competitive and environmentally friendly alternative. Hence, it is foresaw an increase of the navigation transport demand that it will be necessary to accommodate. This will be very challenging particularly in a global change context where less available water resource is expected. A constraint satisfaction problem based on flow graph is proposed in this paper to study the resilience of inland navigation networks against increase of the navigation demand and extreme events. Drought and flood scenarios are simulated considering an network composed of five interconnected navigation reaches. The results show that the designed tools are adapted to the resilience study of inland navigation networks

Management tools to study and to deal with effects of climate change on inland waterways

Inland navigation transport takes part in the Trans-European network program (TEN-T ), which aims at promoting this mode of transport by creating favorable conditions for the further development of this sector. The NAIADES Action Program comprises numerous actions and measures to boost transport on inland waterways. Among these actions, the infrastructure issue is dealt with. It requires the inventory of the existing infrastructure and the study of the possible effects of the expected climate change. This was one of the objectives of the GEPET-Eau project (2013-2016), which led to the proposal of multi-scale modeling approaches and adaptive and predictive control architectures. The resilience of inland waterways against the increase of navigation demand and the expected extreme drought and flood events was studied by considering deterministic models. The proposed architecture is suitable to consider two scales of space and time to optimize the water resource allocation among the inland networks and to guarantee the navigation conditions by proposing advanced control and fault detection tools. These approaches, which were designed by considering inland waterways in the north of France, are still being improved. Indeed, it is firstly necessary to consider all the uncertainties that are inherent to large-scale and environmental systems. Secondly, the advanced control and fault detection tools require further development to deal with the very complex dynamics that characterize inland waterways. The main objective of this work is to present the current state of the tools that have been developed in order to study and manage the inland waterways in a climate change context. The global framework that allows describing the link between these two management scales will be detailed. The water resource allocation approach can be based on three different techniques: the Constraint Satisfaction Problem (CSP), the quadratic optimization and the Markov Decision Process (MDP). The MDP-based approach will be emphasized due to its suitability to study complex systems with uncertainties, and its main advantages and drawbacks will be discussed and compared to the other techniques. Advanced control and fault detection tools require an in-depth knowledge of the inland waterway dynamics. Characteristics of navigation reaches, i.e. slope, resonance phenomenon, uncontrolled inputs and interconnections, need to be taken into account. A big effort has been made to improve the modeling step of the navigation reaches by considering the IDZ (Integrator Delay Zero) model. The designed tools are based on this accurate model, and they aim at improving the water level control of each reach of the inland waterways and at performing predictive maintenance strategies by detecting, isolating and forecasting faults on sensors and actuators (limnimeters, gates, locks, etc.). The designed management tools will be presented by considering a part of the real inland navigation network in the north of France. Perspectives and future developments will be described. Peer Reviewed Document type: Articl

Prise en compte des comportements anticipatifs dans la coordination multi-agent : application à la simulation de trafic en carrefour

Multi-agent systems allow the simulation of complex phenomena which are not easily describable in an analytical way. This approach is often based on the coordination of agents whose actions and interactions involve the emergence of the phenomenon to be simulated. When the aim is to mimic the human behaviour, it is sometimes necessary to use a competitive coordination and to be able to reproduce anticipatory behaviours. This thesis addresses the issue of recognition and anticipation of coordination context. Our work is based on an approach of the anticipation suggested by Rosen : "preventive anticipation" which consists in adapting its actual behaviour to avoid some undesired states. We propose a formalization of this concept based on constraints networks processing. The underlying decisional model uses constraints propagation techniques which allow each agent to gather the effects of an action and thus to detect some undesired states. The proposed algorithm is generic and filters out, before any coordination, all actions leading to one of the non-desired states of the system. Implemented within the framework of a simulation of traffic developed at INRETS and called ARCHISIM, our proposal enable to introduce opportunistic behaviours without any risk of having deadlock in the center of the intersection. The various evaluations validate the emergence of traffic phenomena and confirm the relevance of the approach.Les systèmes multi-agents permettent la simulation de phénomènes complexes difficilement descriptibles de manière analytique. Cette approche repose souvent sur la coordination d’agents dont l’ensemble des actions et interactions entraînent l’émergence du phénomène à simuler. Lorsqu’il s’agit de reproduire le comportement humain, il faut parfois composer avec une coordination compétitive et être capable de reproduire des comportements anticipatifs. Dans cette thèse, nous nous sommes intéressés à la problématique de reconnaissance et d’anticipation du contexte de coordina- tion. Nos travaux se basent sur une approche de l’anticipation proposée par Rosen : "l’anticipation préventive" qui consiste à adapter son comportement courant de manière à éviter un certain nombre d’états non désirés. Nous proposons une formalisation de ce concept basée sur la manipulation et le traitement de réseaux de contraintes. Le modèle décisionnel sous-jacent utilise des techniques de propagation de contraintes permettant à chaque agent d’inférer les effets d’une action et de détecter ainsi un certain nombre d’états non désirés. L’algorithme proposé est générique et permet de filtrer, en amont de la coordination, toutes actions conduisant à l’un des états non désirés du système. Implémentée dans le cadre d’une simulation de trafic développée à l’INRETS : ARCHISIM, notre proposition permet d’introduire des comportements opportunistes plus réalistes sans risque d’apparition d’interblocage au centre des carrefours. Les différentes évaluations menées ont permis de valider l’émergence des phénomènes de trafic obtenus confirmant ainsi la pertinence de l’approche

Prise en compte des comportements anticipatifs dans la coordination multi-agent (application à la simulation de trafic en carrefour)

Les systèmes multi-agents permettent la simulation de phénomènes complexes difficilement descriptibles de manière analytique. Cette approche repose souvent sur la coordination d'agents dont l'ensemble des actions et interactions entraînent l'émergence du phénomène à simuler. Lorsqu'il s'agit de reproduire le comportement humain, il faut parfois composer avec une coordination compétitive et être capable de reproduire des comportements anticipatifs. Dans cette thèse, nous nous sommes intéressés à la problématique de reconnaissance et d'anticipation du contexte de coordination. Nos travaux se basent sur une approche de l'anticipation proposée par Rosen: l'anticipation préventive qui consiste à adapter son comportement courant de manière à éviter un certain nombre d'états indésirés. Nous proposons une formalisation de ce concept basée sur la manipulation et le traitement de réseaux de contraintes. Le modèle décisionnel sous-jacent utilise des techniques de propagation de contraintes permettant à chaque agent d'inférer les effets d'une action et de détecter ainsi un certain nombre d'états indésirés. L'algorithme proposé est générique et permet de filtrer, en amont de la coordination, toutes actions conduisant à l'un des états non désirés du système. Implémentée dans le cadre d'une simulation de trafic développée à l'INRETS: ARCHISIM, notre proposition permet d'introduire des comportements opportunistes plus réalistes sans risque d'apparition d'interblocage au centre des carrefours. Les différentes évaluations menées ont permis de valider l'émergence des phénomènes de trafic obtenus confirmant ainsi la pertinence de l'approche.Multi-agent systems allow the simulation of complex phenomena which are not easily describable in an analytical way. This approach is often based on the coordination of agents whose actions and interactions involve the emergence of the phenomenon to be simulated. When the aim is to mimic the human behaviour, it is sometimes necessary to use a competitive coordination and to be able to reproduce anticipatory behaviours. This thesis addresses the issue of recognition and anticipation of coordination context. Our work is based on an approach of the anticipation suggested by Rosen: preventive anticipation which consists in adapting its actual behaviour to avoid some undesired states. We propose a formalization of this concept based on constraints networks processing. The underlying decisional model uses constraints propagation techniques which allow each agent to gather the effects of an action and thus to detect some undesired states. The proposed algorithm is generic and filters out, before any coordination, all actions leading to one of the non-desired states of the system. Implemented within the framework of a simulation of traffic developed at INRETS and called ARCHISIM, our proposal enable to introduce opportunistic behaviours without any risk of having deadlock in the center of the intersection. The various evaluations validate the emergence of traffic phenomena and confirm the relevance of the approach.VALENCIENNES-BU Sciences Lettres (596062101) / SudocSudocFranceF

Scaling up decentralized MDPs through heuristic search

Decentralized partially observable Markov decision processes (Dec-POMDPs) are rich models for cooperative decision-making under uncertainty, but are often intractable to solve optimally (NEXP-complete). The transition and observation independent Dec-MDP is a general subclass that has been shown to have complexity in NP, but optimal algorithms for this subclass are still inefficient in practice. In this paper, we first provide an updated proof that an optimal policy does not depend on the histories of the agents, but only the local observations. We then present a new algorithm based on heuristic search that is able to expand search nodes by using constraint optimization. We show experimental results comparing our approach with the state-of-the-art Dec-MDP and Dec-POMDP solvers. These results show a reduction in computation time and an increase in scalability by multiple orders of magnitude in a number of benchmarks.