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An optimization–simulation closed-loop feedback framework for modeling the airport capacity management problem under uncertainty
No full textInternational audienceThis paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for riving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields
Verifying the Mathematical Library of an UAV Autopilot with Frama-C
No full textInternational audienceEnsuring safety of critical systems is crucial and is often attained by extensive testing of the system. Formal methods are now commonly accepted as powerful tools to obtain guarantees on such systems, even if it is generally not possible to formally prove the safety and correctness of the whole system. This paper presents an ongoing work on the formal verification of the Paparazzi UAV autopilot using the Frama-C verification platform. We focus on a Paparazzi mathematical library providing different UAV state representations and associated conversion functions and manage to prove the absence of runtime errors in the library and some interesting functional properties on floating-point conversion functions
LAST MILE DELIVERY MODEL FOR SUSTAINABLE DEVELOPMENT
No full textInternational audienceLast mile delivery is defined as the movement of goods to/from a transportation hub from/to final delivery destinations, this part of the logistics chain being in general for many fields of logistics the more problematic and costly. Latest technologies are providing new means for collecting and exchanging information as well as for delivering logistics services. For example, advanced drone technology can produce today logistics solutions considering that UAVs can now provide acceptable payloads, autonomy and collision avoidance capabilities. This technology appears to be an opportunity to reduce the last mile logistics costs as well as to contribute to sustainability objectives. This paper focuses on the development of a methodology to design last mile logistics based on a hybrid solution composed of ground vehicles addressing the planned demand and of UAVs processing the unplanned demand. The ultimate objective of this study being to estimate the generated UAVs traffic in the urban space, a global continuous approach is adopted. The ground LMD routing and scheduling activities are considered globally using an empirical formula relating the minimum cost route operation in a dense urban area with the mean distance necessary to serve a customer. The UAV LMD activity, which here almost exclusively supports the unplanned demand, is analyzed globally using a stochastic model. In both cases, the generated traffic is estimated and performance indexes, related to the quality of service and the environmental impact, are produced
Simulating the transition of mobility toward smart and sustainable cities
No full textInternational audienceThis paper introduces a simulation framework to model and study mobility changes. Novelty comes from the possibility to combine different agent-based models into the GAMA platform, and to interact with the simulation through pre-designed scenarios and a serious game. The objective is to explore the impact of different urban policies on future mobility
A Multiscale Parametrization for Refractivity Estimation in the Troposphere
No full textInternational audienceThis paper presents the idea of multiscale parametrization for tropospheric refractivity inversion using gradient-based optimization method. Our motivation is to improve the accuracy of inversion without the use of apriori information. We retrieve the details of the refractivity distribution progressively from large to smaller scales using hierarchical multiscale strategies in the admissible parameter space. The proposed formulation for multiscale adjoint tomography is validated and is confronted to a numerical test. This study shows that such strategies can potentially resolve complex ducting conditions which would otherwise fail a plain gradient-based inversion
Probabilistic Robustness Estimates for Feed-forward Neural Networks
No full textInternational audienceRobustness of deep neural networks is a critical issue in practical applications. In the general case of feed-forward neural networks (including convolutional deep neural network architectures), under random noise attacks, we propose to study the probability that the output of the network deviates from its nominal value by a given threshold. We derive a simple concentration inequality for the propagation of the input uncertainty through the network using the Cramer-Chernoff method and estimates of the local variation of the neural network mapping computed at the training points. We further discuss and exploit the resulting condition on the network to regularize the loss function during training. Finally, we assess the proposed tail probability estimates empirically on various public datasets and show that the observed robustness is very well estimated by the proposed method
The aircraft runway scheduling problem: A survey
No full textInternational audienceThe aircraft scheduling problem consists in sequencing aircraft on airport runways and in scheduling their times of operations taking into consideration several operational constraints. It is known to be an NP-hard problem, an ongoing challenge for both researchers and air traffic controllers.The aim of this paper is to present a focused review on the most relevant techniques in the recent literature (since 2010) on the aircraft runway scheduling problem, including exact approaches such as mixed-integer programming and dynamic programming, metaheuristics, and novel approaches based on reinforcement learning. Since the benchmark instances used in the literature are easily solved by high-performance computers and current versions of solvers, we propose a new data set with challenging realistic problems constructed from real-world air traffic
Conception centrée utilisateur d'interfaces adaptées au vieillissement cognitif des pilotes
No full textNational audienceLe déclin des capacités cognitives des pilotes lié à l'âge peut mener à l'erreur et altérer ainsi la sécurité des vols. Cette thèse se propose d'étudier différentes solutions d'assistance afin de réduire l'impact du vieillissement sur les performances des pilotes. Des interviews ont fait émerger deux grandes problématiques : la baisse des capacités attentionnelles pouvant être compensée par l'utilisation de différentes modalités d'alarmes, et le déclin cognitif, dont les conséquences peuvent être atténuées par le développement d'interfaces soutenant le pilote dans ses tâches de pilotage. Nous prévoyons de tester ces solutions en vol simulé et/ou en vol réel
Maximum entropy on the mean approach to solve generalized inverse problems with an application in computational thermodynamics
No full textInternational audienceIn this paper, we study entropy maximisation problems in order to reconstruct functions or measures subject to very general integral constraints. Our work has a twofold purpose. We first make a global synthesis of entropy maximisation problems in the case of a single reconstruction (measure or function) from the convex analysis point of view, as well as in the framework of the embedding into the Maximum Entropy on the Mean (MEM) setting. We further propose an extension of the entropy methods for a multidimensional case
Understanding and overcoming horizontal separation complexity in air traffic control: an expert/novice comparison
No full textInternational audienceHumans still play a key role in air traffic control but their performances limit the capacity of the airspace and are responsible for delays. At the tactical level, even though air traffic controllers (ATCO) are trained for years, their performances are limited. In this article, we first isolated the tactical horizontal deconfliction task and explained its mathematical complexity. We observed through a simple experiment conducted on trainee and experienced ATCOs its complexity on random traffic in a part-task trainer displaying two to five aircraft trajectories at the same altitude. We compared performances of trainee ATCOs with experienced ATCOs using two different displays: a basic display showing information on aircraft positions and a dynamic visualization tool that represents the conflicting portions of aircraft trajectories and the evolution of the conflict zone when the user adds a maneuver to an aircraft. The tool allows the user to dynamically check the potential conflicting zones with the computer mouse before making a maneuver decision. Results showed that in easy situations (two aircraft), performance was similar with both displays and groups. However, as the complexity of the situations grows (from three to five aircraft), the dynamic visualization tool enables users to solve the conflicts more efficiently. Using the tool leads to fewer unsolved conflicts. Even if experienced ATCOs performed much better than trainee ATCOs on complex situations, they also performed much better with the conflict visualization tool than without on such situations