Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow and Capacity Management

We investigate a method to deal with congestion of sectors and delays in the tactical phase of air traffic flow and capacity management. It relies on temporal objectives given for every point of the flight plans and shared among the controllers in order to create a collaborative environment. This would enhance the transition from the network view of the flow management to the local view of air traffic control. Uncertainty is modeled at the trajectory level with temporal information on the boundary points of the crossed sectors and then, we infer the probabilistic occupancy count. Therefore, we can model the accuracy of the trajectory prediction in the optimization process in order to fix some safety margins. On the one hand, more accurate is our prediction; more efficient will be the proposed solutions, because of the tighter safety margins. On the other hand, when uncertainty is not negligible, the proposed solutions will be more robust to disruptions. Furthermore, a multiobjective algorithm is used to find the tradeoff between the delays and congestion, which are antagonist in airspace with high traffic density. The flow management position can choose manually, or automatically with a preference-based algorithm, the adequate solution. This method is tested against two instances, one with 10 flights and 5 sectors and one with 300 flights and 16 sectors.Comment: IEEE Congress on Evolutionary Computation (2013). arXiv admin note: substantial text overlap with arXiv:1309.391

Computational Methods for Probabilistic Inference of Sector Congestion in Air Traffic Management

This article addresses the issue of computing the expected cost functions from a probabilistic model of the air traffic flow and capacity management. The Clenshaw-Curtis quadrature is compared to Monte-Carlo algorithms defined specifically for this problem. By tailoring the algorithms to this model, we reduce the computational burden in order to simulate real instances. The study shows that the Monte-Carlo algorithm is more sensible to the amount of uncertainty in the system, but has the advantage to return a result with the associated accuracy on demand. The performances for both approaches are comparable for the computation of the expected cost of delay and the expected cost of congestion. Finally, this study shows some evidences that the simulation of the proposed probabilistic model is tractable for realistic instances.Comment: Interdisciplinary Science for Innovative Air Traffic Management (2013

Online Learning for Ground Trajectory Prediction

This paper presents a model based on an hybrid system to numerically simulate the climbing phase of an aircraft. This model is then used within a trajectory prediction tool. Finally, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm is used to tune five selected parameters, and thus improve the accuracy of the model. Incorporated within a trajectory prediction tool, this model can be used to derive the order of magnitude of the prediction error over time, and thus the domain of validity of the trajectory prediction. A first validation experiment of the proposed model is based on the errors along time for a one-time trajectory prediction at the take off of the flight with respect to the default values of the theoretical BADA model. This experiment, assuming complete information, also shows the limit of the model. A second experiment part presents an on-line trajectory prediction, in which the prediction is continuously updated based on the current aircraft position. This approach raises several issues, for which improvements of the basic model are proposed, and the resulting trajectory prediction tool shows statistically significantly more accurate results than those of the default model.Comment: SESAR 2nd Innovation Days (2012

Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning

Parameter tuning is recognized today as a crucial ingredient when tackling an optimization problem. Several meta-optimization methods have been proposed to find the best parameter set for a given optimization algorithm and (set of) problem instances. When the objective of the optimization is some scalar quality of the solution given by the target algorithm, this quality is also used as the basis for the quality of parameter sets. But in the case of multi-objective optimization by aggregation, the set of solutions is given by several single-objective runs with different weights on the objectives, and it turns out that the hypervolume of the final population of each single-objective run might be a better indicator of the global performance of the aggregation method than the best fitness in its population. This paper discusses this issue on a case study in multi-objective temporal planning using the evolutionary planner DaE-YAHSP and the meta-optimizer ParamILS. The results clearly show how ParamILS makes a difference between both approaches, and demonstrate that indeed, in this context, using the hypervolume indicator as ParamILS target is the best choice. Other issues pertaining to parameter tuning in the proposed context are also discussed.Comment: arXiv admin note: substantial text overlap with arXiv:1305.116

Divide-and-Evolve : une nouvelle méta-heuristique pour la planification temporelle indépendante du domaine

Traduction en français de l'article Divide-and-Evolve: a New Memetic Scheme for Domain-Independent Temporal Planning, présenté à la conférence EvoCOP 2006 à Budapest, http://hal.inria.fr/inria-00000975/en/Une approche originale dénommée Divide-and-Evolve est proposée pour l'hybridation des Algorithmes Évolutionnaires (AEs) avec des méthodes d'Intelligence Artificielle dans le domaine des Problèmes de Planification Temporelle (PPTs). Alors que les algorithmes mémétiques standards utilisent des méthodes locales de résolution pour améliorer les solutions évolutionnaires, l'approche Divide-and-Evolve divise arbitrairement le problème en plusieurs sous-problèmes (que l'on espère plus faciles), et peut ainsi résoudre globalement des problèmes hors d'atteinte lorsque directement fournis en entrée d'algorithmes spécialisés classiques. Mais le principal avantage de l'approche Divide-and-Evolve est qu'elle ouvre immédiatement une avenue pour l'optimisation multi-objectifs, même avec une méthode spécialisée mono-objectif. La preuve du concept de cette approche sur le benchmark de transport standard Zeno (mono-objectif) est donnée, et un petit benchmark multi-objectifs original est proposé dans ce même cadre Zeno pour montrer les possibilités multi-objectifs de la méthodologie proposée, une percée dans la planification temporelle

Divide-and-Evolve: a New Memetic Scheme for Domain-Independent Temporal Planning

An original approach, termed Divide-and-Evolve is proposed to hybridize Evolutionary Algorithms (EAs) with Operational Research (OR) methods in the domain of Temporal Planning Problems (TPPs). Whereas standard Memetic Algorithms use local search methods to improve the evolutionary solutions, and thus fail when the local method stops working on the complete problem, the Divide-and-Evolve approach splits the problem at hand into several, hopefully easier, sub-problems, and can thus solve globally problems that are intractable when directly fed into deterministic OR algorithms. But the most prominent advantage of the Divide-and-Evolve approach is that it immediately opens up an avenue for multi-objective optimization, even though the OR method that is used is single-objective. Proof of concept approach on the standard (single-objective) Zeno transportation benchmark is given, and a small original multi-objective benchmark is proposed in the same Zeno framework to assess the multi-objective capabilities of the proposed methodology, a breakthrough in Temporal Planning

DAE: Planning as Artificial Evolution -- (Deterministic part)

International audienceThe sub-optimal DAE planner implements the stochastic approach for domain-independent planning decomposition. The purpose of this planner is to optimize the makespan, or the number of actions, by generating ordered sequences of intermediate goals via a process of artificial evolution. For the evolutionary part we used the Evolving Objects (EO) library, and to solve each intermediate subproblem we used the constraint-based optimal temporal planner CPT. Therefore DAE can only solve problems that CPT can solve. Compression of subplans into a global solution plan is also achieved efficiently with CPT by exploiting causalities found so far. Because the selection of predicates for intermediate goal generation is still an open question, we have submitted two planners DAE1 and DAE2 that use different strategies for the generation of intermediate goals. An empirical formula has been defined to set a limit on the number of backtracks allowed for solving the intermediate subproblems

Planification Evolutionnaire par Décomposition

Ce rapport présente l'approche Divide-and-Evolve pour la résolution générique des problèmes de planification temporelle par décomposition. L'idée principale de l'approche est la recherche des solutions dans l'espace des décompositions en états intermédiaires à l'aide d'un algorithme évolutionnaire: les solutions candidates sont des séquences d'états intermédiaires qui définissent successivement les plans partiels du problème initial. Nous nous sommes intéressés à la résolution des problèmes de type "simple temporal planning problems". La résolution des séquences d'états intermédiaires et la détermination d'une solution globale se font à l'aide du planificateur CPT. Ce rapport formalise l'approche, définit l'algorithme Divide-and-Evolve et compare les résultats obtenus à ceux trouvés par les meilleurs planificateurs existants à notre connaissance

Learning Divide-and-Evolve Parameter Configurations with Racing

International audienceThe sub-optimal DAE planner implements the stochastic approach for domain-independent planning decomposition introduced in (Schoenauer, Sav´eant, and Vidal 2006; 2007). This planner optimizes either the makespan or the number of actions by generating ordered sequences of intermediate goals via a process of artificial evolution. The evolutionary part of DAE uses the Evolving Objects (EO) library, and the embedded planner it is based on is the non-optimal STRIPS planner YAHSP (Vidal 2004). For a given domain, the learning phase uses a racing procedure to choose the rates of the different variation operators used in DAE, and processes the results obtained during this process to specify the predicates that will be later used to describe the intermediate goals

Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow and Capacity Management

International audienceWe investigate a method to deal with congestion of sectors and delays in the tactical phase of air traffic flow and capacity management. It relies on temporal objectives given for every point of the flight plans and shared among the controllers in order to create a collaborative environment. This would enhance the transition from the network view of the flow management to the local view of air traffic control. Uncertainty is modeled at the trajectory level with temporal information on the boundary points of the crossed sectors and then, we infer the probabilistic occupancy count. Therefore, we can model the accuracy of the trajectory prediction in the optimization process in order to fix some safety margins. On the one hand, more accurate is our prediction; more efficient will be the proposed solutions, because of the tighter safety margins. On the other hand, when uncertainty is not negligible, the proposed solutions will be more robust to disruptions. Furthermore, a multiobjective algorithm is used to find the tradeoff between the delays and congestion, which are antagonist in airspace with high traffic density. The flow management position can choose manually, or automatically with a preference-based algorithm, the adequate solution. This method is tested against two instances, one with 10 flights and 5 sectors and one with 300 flights and 16 sectors