Dissertations / Theses on the topic 'Optimisation Sans Contrainte'
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Kchouk, Bilel. "Méthodes de Chebyshev d'ordres supérieurs pour l'optimisation non linéaire, sans contrainte et différentiable." Thèse, Université de Sherbrooke, 2012. http://hdl.handle.net/11143/6682.
Kortas, Manel. "Optimisation de la liaison montante pour un réseau de capteurs sans fil avec la contrainte d'énergie." Thesis, Limoges, 2020. http://aurore.unilim.fr/theses/nxfile/default/45adbe14-0f06-4b36-9fb7-80441c295210/blobholder:0/2020LIMO0025.pdf.
In this dissertation, we are interested in the data gathering with energy constraint for Wireless Sensor Networks (WSNs). Yet, there exist several challenges that may disturb a convenient functioning of this kind of networks. Indeed, WSNs' applications have to deal with limited energy, memory and processing capabilities of sensor nodes. Furthermore, as the size of these networks is growing continually, the amount of data for processing and transmitting becomes enormous. In many practical cases, the wireless sensors are distributed across a physical field to monitor physical phenomena with high space-time correlation. Hence, the main focus of this thesis is to reduce the amount of processed and transmitted data in the data gathering scenario. In the first part of this thesis, we consider the Compressive Sensing (CS), which is a promising technique to exploit this correlation in order to limit the number of transmission and therefore increase the lifetime of the network. Typically, we are interested in the mesh network topology, where the sink node is not in the range of sensors and routing schemes must be applied. We propose a joint Space-Time Compressive Sensing (STCS) by exploiting jointly the inter-sensors and intra-sensor data dependency. Moreover, since the routing and the number of retransmission affect significantly the total energy consumption, we introduce the routing in our cost function in order to optimize the selection of the transmitting sensors. Simulation results show that this method outperforms the existing ones and confirm the validity of our approach. In the second part of this thesis, we attempt to address nearly the same twofold energy saving scheme that is investigated in the first part with the use of the Matrix Completion (MC) methodology. Precisely, we assume that a restricted number of sensor nodes are selected to be active and represent the whole network, while the rest of nodes remain idle and do not participate at all in the data sensing and transmission. Furthermore, the set of active nodes' readings is efficiently reduced, in each time slot, according to a cluster scheduling with the Optimized Cluster-based MC data gathering approach (OCBMC). Relying on the existing MC techniques, the sink node is unable to recover the entire data matrix due to the existence of the completely empty rows that correspond to the inactive nodes. Although applying a high data compression ratio extremely reduces the overall network energy consumption, the network lifetime is not necessarily extended due to the uneven energy depletion of the sensor nodes' batteries. To this end, in the third part of this thesis, we have developed the Energy-Aware Matrix Completion based data gathering approach (EAMC), which designates the active nodes according to their residual energy levels. Furthermore, since we are mainly interested in the high data loss scenarios, the limited amount of delivered data must be sufficient in terms of informative quality it holds in order to reach good and satisfactory recovery accuracy for the entire network data. For that reason, the EAMC selects the nodes that can best represent the network depending on their inter-correlation as well as the network energy efficiency, with the use of a combined energy-aware and correlation-based metric. This introduced active node cost function changes with the type of application one wants to perform, with the intention to reach a longer lifespan for the network
Raynaud, Paul. "L'exploitation de la structure partiellement-séparable dans les méthodes quasi-Newton pour l'optimisation sans contrainte et l'apprentissage profond." Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALI021.
This thesis studies and improves the use of the partially-separable structure for unconstrained optimization, particularly for quasi-Newton methods and training neural networks.A partially-separable function is the sum of element functions, each of lower dimension than the total problem.Thus, the Hessian can be aggregated by separately approximating the Hessian of each element function with a dense matrix.These partitioned quasi-Newton methods are applicable to high-dimensional problems and maintain the sparse structure of the Hessian, unlike a limited-memory quasi-Newton method.In practice, these methods require fewer iterations than a limited-memory quasi-Newton method and are parallelizable by distributing computations related to the element functions.However, a comprehensive literature review on the subject has revealed some limitations, particularly when the dimension of the element functions is large.Additionally, the only open-source optimization software exploiting the partially-separable structure is unusable for inexperienced users, leaving only commercial software as an option.In this thesis, two solutions are proposed to address these shortcomings, along with an application of partially-separable optimization concepts to supervised learning of a neural network.The first contribution is a software suite based on an automatic detection of the partially-separable structure of a problem, i.e., retrieves each reduced-dimensional element function.Following this, partitioned data structures necessary for storing derivatives, or their approximations, are allocated and used to define partitioned quasi-Newton optimization methods.The entire suite is integrated into the "JuliaSmoothOptimizers" ecosystem, which gathers numerous tools for smooth optimization, including optimization algorithms that can therefore exploit the detected partial separability.The second contribution replaces the approximation of an element Hessian by a dense matrix with a limited-memory quasi-Newton linear operator.As a result, the memory cost of the total Hessian approximation is no longer quadratically related to the dimension of the element functions.A limited-memory partitioned quasi-Newton method is then applicable when the element functions are large.Each limited-memory partitioned quasi-Newton method has a proof of global convergence.Additionally, numerical results show that these methods outperform partitioned or limited-memory quasi-Newton methods when the elements are large.The final contribution examines the exploitation of the partially-separable structure during supervised training of a neural network.The optimization problem associated with training is generally not partially-separable.Therefore, a partially-separable loss function and a partitioned network architecture are introduced to make the training partially-separable.Numerical results combining these two contributions are competitive with standard architectures and loss functions according to state-of-the-art training methods.Moreover, this combination produces an additional parallelization scheme to existing methods for supervised learning.Indeed, the calculations of each element loss function can be distributed to a worker requiring only a fraction of the neural network to operate.Finally, a limited-memory partitioned quasi-Newton training is proposed.This training is empirically shown to be competitive with state-of-the-art training methods
Zayyoun, Najoua. "Optimisation et modélisation du détachement de couches minces de silicium par contrainte thermique avec ou sans guidage de la fracture : application au photovoltaïque." Thesis, Orléans, 2019. http://www.theses.fr/2019ORLE3036.
The reduction of photovoltaic cells cost and the increase of their efficiency is probably one of the best solution to tacle the climate change issues. The mean of this thesis is to study the innovative processes to produce ultra-thin monocrystalline silicon layers without loss of raw material (with thicknesses ranging from several hundred nanometers to several micrometers), by using thermal stress and low energy hydrogen implantation. The use of such kerf-free processes leads to a significant reduction of the silicon consumption, in order to produce of low-cost photovoltaic solar cells.In this work, by using analytical and numerical modeling, we first determined the thermal stresses needed for the detachment of silicon by stress-induced spalling process and predicts the detached thickness of silicon foils. These models depend on thermals and elastics parameters of metal used as well as the applied thermal loading. A good agreement between the theoretical and experimental results was obtained. Furthermore, different optimal parameters leading to the detachment of silicon foils with desired thicknesses using SIS process were investigated such as the thickness of the stressor layer, the nature of stressor layer and the thickness of glue. In a second part, thin silicon layers were transferred as a function of thermal annealing using the stress-induced spalling process guided by hydrogen implantation-induced defects. Then, the use of experimental characterizations and FEM simulations of the thermal stresses induced in implanted silicon we explain the mechanisms involved when combining the two processes. Characterization of silicon foils was performed by various technique in order to validate and optimized the process
Maurandi, Victor. "Algorithmes pour la diagonalisation conjointe de tenseurs sans contrainte unitaire. Application à la séparation MIMO de sources de télécommunications numériques." Thesis, Toulon, 2015. http://www.theses.fr/2015TOUL0009/document.
This thesis develops joint diagonalization of matrices and third-order tensors methods for MIMO source separation in the field of digital telecommunications. After a state of the art, the motivations and the objectives are presented. Then the joint diagonalisation and the blind source separation issues are defined and a link between both fields is established. Thereafter, five Jacobi-like iterative algorithms based on an LU parameterization are developed. For each of them, we propose to derive the diagonalization matrix by optimizing an inverse criterion. Two ways are investigated : minimizing the criterion in a direct way or assuming that the elements from the considered set are almost diagonal. Regarding the parameters derivation, two strategies are implemented : one consists in estimating each parameter independently, the other consists in the independent derivation of couple of well-chosen parameters. Hence, we propose three algorithms for the joint diagonalization of symmetric complex matrices or hermitian ones. The first one relies on searching for the roots of the criterion derivative, the second one relies on a minor eigenvector research and the last one relies on a gradient descent method enhanced by computation of the optimal adaptation step. In the framework of joint diagonalization of symmetric, INDSCAL or non symmetric third-order tensors, we have developed two algorithms. For each of them, the parameters derivation is done by computing the roots of the considered criterion derivative. We also show the link between the joint diagonalization of a third-order tensor set and the canonical polyadic decomposition of a fourth-order tensor. We confront both methods through numerical simulations. The good behavior of the proposed algorithms is illustrated by means of computing simulations. Finally, they are applied to the source separation of digital telecommunication signals
Montalbano, Pierre. "Contraintes linéaires et apprentissage sans conflit pour les modèles graphiques." Electronic Thesis or Diss., Toulouse 3, 2023. http://www.theses.fr/2023TOU30340.
Graphical models define a family of formalisms and algorithms used in particular for logical and probabilistic reasoning, in fields as varied as image analysis or natural language processing. They are capable of being learned from data, giving probabilistic information that can then be combined with logical information. The goal of the thesis is to improve the efficiency of reasoning algorithms on these models crossing probabilities and logic by generalizing a fundamental mechanism of the most efficient purely logical reasoning tools (SAT solvers) to this hybrid case mixing probabilities and logic: conflict-based learning. The work is based on the concept of duality in linear programming and our learning mechanism is conflict-free, producing linear constraints efficiently solved using a knapsack formulation
Medjiah, Samir. "Optimisation des protocoles de routage dans les réseaux multi-sauts sans fil à contraintes." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14663/document.
Great research efforts have been carried out in the field of challenged multihop wireless networks (MWNs). Thanks to the evolution of the Micro-Electro-Mechanical Systems (MEMS) technology and nanotechnologies, multihop wireless networks have been the solution of choice for a plethora of problems. The main advantage of these networks is their low manufacturing cost that permits one-time application lifecycle. However, if nodes are low-costly to produce, they are also less capable in terms of radio range, bandwidth, processing power, memory, energy, etc. Thus, applications need to be carefully designed and especially the routing task because radio communication is the most energy-consuming functionality and energy is the main issue for challenged multihop wireless networks.The aim of this thesis is to analyse the different challenges that govern the design of challenged multihop wireless networks such as applications challenges in terms of quality of service (QoS), fault-tolerance, data delivery model, etc., but also networking challenges in terms of dynamic network topology, topology voids, etc. Our contributions in this thesis focus on the optimization of routing under different application requirements and network constraints. First, we propose an online multipath routing protocol for QoS-based applications using wireless multimedia sensor networks. The proposed protocol relies on the construction of multiple paths while transmitting data packets to their destination, i.e. without prior topology discovery and path establishment. This protocol achieves parallel transmissions and enhances the end-to-end transmission by maximizing path bandwidth and minimizing the delays, and thus meets the requirements of QoS-based applications. Second, we tackle the problem of routing in mobile delay-tolerant networks by studying the intermittent connectivity of nodes and deriving a contact model in order to forecast future nodes' contacts. Based upon this contact model, we propose a routing protocol that makes use of nodes' locations, nodes' trajectories, and inter-node contact prediction in order to perform forwarding decisions. The proposed routing protocol achieves low end-to-end delays while using efficiently constrained nodes' resources in terms of memory (packet queue occupancy) and processing power (forecasting algorithm). Finally, we present a topology control mechanism along a packet forwarding algorithm for event-driven applications using stationary wireless sensor networks. Topology control is achieved by using a distributed duty-cycle scheduling algorithm. Algorithm parameters can be tuned according to the desired node's awake neighbourhood size. The proposed topology control mechanism ensures trade-off between event-reporting delay and energy consumption
Langouët, Hoël. "Optimisation sans dérivées sous contraintes : deux applications industrielles en ingénierie de réservoir et en calibration des moteurs." Phd thesis, Université de Nice Sophia-Antipolis, 2011. http://tel.archives-ouvertes.fr/tel-00671987.
Ngom, Diery. "Optimisation de la durée de vie dans les réseaux de capteurs sans fil sous contraintes de couvertureet de connectivité réseau." Thesis, Mulhouse, 2016. http://www.theses.fr/2016MULH9134/document.
Since the past two decades, a new technology called Wireless Sensor Network (WSN) which result in a fusion of embedded systems and wireless communications has emerged. A WSN is Ad hoc network composed of many sensors nodes communicating via wireless links and which can be deployed randomly or deterministically over a given interest region. Theses sensors can also collect data from the environment, do local processing and transmit the data to a sink node or Base Station (BS) via multipath routing. Thereby, a wide range of potential applications have been envisioned using WSN such as environmental conditions monitoring, wildlife habitat monitoring, industrial diagnostic, agricultural, improve health care, etc. Nevertheless,WSN are not perfect. Indeed, given their small size, their low cost and their deployment generally in hostile or difficult access areas, sensor nodes have some weaknesses such as: a limited energy, so a network lifetime limited, limited bandwidth, limited computations and communications capabilities, etc. To overcome these limitations, several research issues from were created in recent years, and the main issues focus on the optimization of energy consumption in order to improve the network lifetime. Other important researches focus on issues of coverage areas, placement strategies of sensor nodes and network connectivity. However, most solutions proposed in recent years to resolve these issues do not take into account all these issues that we cited above in resolutions models; while in many WSN applications such as monitoring critical region, wildlife habitat monitoring, agricultural application, a full coverage of the monitoring region and network connectivity are mandatory as well an energy-awareness network lifetime. The objective of this thesis is thus to propose new scheduling mechanisms for optimizing the network lifetime in WSN, while ensuring at any time of the network lifetime a full coverage of the monitored region and network connectivity. To achieve our goals, we have study and done proposal in two axes which are placement strategy of sensor nodes and scheduling mechanism in the MAC layer. For these, we have implemented a Distributed Scheduling Medium Access Control algorithm (DSMAC) based on our placement method. Furthermore, DSMAC enables to cover 100% of the monitored region, to ensure optimal network connectivity and also allows sensors node to save up to 30% of their energy compared to other MAC protocols such as TunableMAC
Hernandez, Sébastien. "Evaluation et optimisation du mécanisme de Handhover dans un Réseau Local Sans Fil dédié aux applications à trafic contraint par le temps." Phd thesis, Clermont-Ferrand 2, 2006. https://theses.hal.science/docs/00/70/32/74/PDF/2006CLF21682.pdf.
Hernandez, Sébastien. "Evaluation et optimisation du mécanisme de Handhover dans un Réseau Local Sans Fil dédié aux applications à trafic contraint par le temps." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2006. http://tel.archives-ouvertes.fr/tel-00703274.
Changuel, Nesrine. "Régulation de la qualité lors de la transmission de contenus vidéo sur des canaux sans fils." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00659806.
Dahito, Marie-Ange. "Constrained mixed-variable blackbox optimization with applications in the automotive industry." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS017.
Numerous industrial optimization problems are concerned with complex systems and have no explicit analytical formulation, that is they are blackbox optimization problems. They may be mixed, namely involve different types of variables (continuous and discrete), and comprise many constraints that must be satisfied. In addition, the objective and constraint blackbox functions may be computationally expensive to evaluate.In this thesis, we investigate solution methods for such challenging problems, i.e constrained mixed-variable blackbox optimization problems involving computationally expensive functions.As the use of derivatives is impractical, problems of this form are commonly tackled using derivative-free approaches such as evolutionary algorithms, direct search and surrogate-based methods.We investigate the performance of such deterministic and stochastic methods in the context of blackbox optimization, including a finite element test case designed for our research purposes. In particular, the performance of the ORTHOMADS instantiation of the direct search MADS algorithm is analyzed on continuous and mixed-integer optimization problems from the literature.We also propose a new blackbox optimization algorithm, called BOA, based on surrogate approximations. It proceeds in two phases, the first of which focuses on finding a feasible solution, while the second one iteratively improves the objective value of the best feasible solution found. Experiments on instances stemming from the literature and applications from the automotive industry are reported. They namely include results of our algorithm considering different types of surrogates and comparisons with ORTHOMADS
Gogu, Ada. "Dimensionnement des réseaux RCSF sous des contraintes énergétiques : modèles mathématiques et méthodes d'optimisation." Compiègne, 2012. http://www.theses.fr/2012COMP2028.
In this thesis, we focused on the development of optimal methods regarding WSN dimensioning problems, mostly encountered during the planning phase. These were instantiated basically into three combinatorial optimization problems. The network deployment scheme which seeks to place the sensors in a such way that the cost of communication operations is minimized. The network configuration problem that asks to find a strategy for dividing the network such that some criteria are satisfied. In the problem’s model we took into account the data aggregation constraint and the discrete values of power transmission. For both problems we proposed a resolution method, based on dynamic programming, which permitted us to solve them optimally. Finally, the joint problem of scheduling and power assignment, consisted in finding a feasible scheduling under SINR constraints and a power assignment scheme to guarantee successful concurrent transmissions. As the problem is shown to be NP-hard we propose a greedy heuristic. The resolution method for the power assignment strategy, an iterative algorithm based on linear programming, provides optimal solutions
Troltzsch, Anke. "Une méthode de région de confiance avec ensemble actif pour l'optimisation non linéaire sans dérivées avec contraintes de bornes appliquée à des problèmes aérodynamiques bruités." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2011. http://tel.archives-ouvertes.fr/tel-00639257.
Simard, Catherine. "Analyse d'algorithmes de type Nesterov et leurs applications à l'imagerie numérique." Mémoire, Université de Sherbrooke, 2015. http://hdl.handle.net/11143/7714.
Tröltzsch, Anke. "An active-set trust-region method for bound-constrained nonlinear optimization without derivatives applied to noisy aerodynamic design problems." Thesis, Toulouse, INPT, 2011. http://www.theses.fr/2011INPT0031/document.
Derivative-free optimization (DFO) has enjoyed renewed interest over the past years, mostly motivated by the ever growing need to solve optimization problems defined by functions whose values are computed by simulation (e.g. engineering design, medical image restoration or groundwater supply).In the last few years, a number of derivative-free optimization methods have been developed and especially model-based trust-region methods have been shown to perform well.In this thesis, we present a new interpolation-based trust-region algorithm which shows to be efficient and globally convergent (in the sense that its convergence is guaranteed to a stationary point from arbitrary starting points). The new algorithm relies on the technique of self-correcting geometry proposed by Scheinberg and Toint [128] in 2009. In their theory, they advanced the understanding of the role of geometry in model-based DFO methods, in our work, we improve the efficiency of their method while maintaining its good theoretical convergence properties. We further examine the influence of different types of interpolation models on the performance of the new algorithm.Furthermore, we extended this method to handle bound constraints by applying an active-set strategy. Considering an active-set method in bound-constrained model-based optimization creates the opportunity of saving a substantial amount of function evaluations. It allows to maintain smaller interpolation sets while proceeding optimization in lower dimensional subspaces. The resulting algorithm is shown to be numerically highly competitive. We present results on a test set of smooth problems from the CUTEr collection and compare to well-known state-of-the-art packages from different classes of DFO methods.To report numerical experiments incorporating noise, we create a test set of noisy problems by adding perturbations to the set of smooth problems. The choice of noisy problems was guided by a desire to mimic simulation-based optimization problems. Finally, we will present results on a real-life application of a wing-shape design problem provided by Airbus
Nguimpi, Langue Leïla. "Contribution au dimensionnement optimal d’une machine électrique sans aimant pour la propulsion de véhicules hybrides." Thesis, Compiègne, 2018. http://www.theses.fr/2018COMP2420/document.
Hybrid propulsion (electric thermal) is a relevant solution in the search for vehicles consuming less than 2 liters of fuel / 100 km. Nevertheless, this type of motorization comes up against cost levels that are too high for widespread distribution. One of the reasons for these high costs is the level of permanent magnets integrating the constitution of the electric machine. In addition, the "material cost" of these permanent magnets has soared in recent years making this type of machine difficult to match the target market. The aim of the thesis is to propose a magnetless electrical machine structure whose mass performances are comparable to those of permanent magnet machines. This increase in performance can be achieved by high rotation speeds or thermal sizing different from those usually used. The works proposed as part of this thesis will be as follows: - In-depth bibliographic analysis to propose a structure and a principle most adapted to the envisaged constraints- Proposal of a multi physical model (magnetic, thermal, mechanical) of the selected structure. - Use of the multi-physical model for optimal sizing- Follow-up of the realization of the prototype - Experimental validation of the prototype. This work will be conducted as part of a consortium integrating academics, manufacturers and automotive suppliers of the highest order
Bargiacchi, Sandrine. "Résolution de grands systèmes : du linéaire au non linéaire." Toulouse 3, 2004. http://www.theses.fr/2004TOU30049.
Castano, Giraldo Fabian Andres. "Decomposition-based approaches for the design of energy efficient wireless sensor networks." Thesis, Lorient, 2014. http://www.theses.fr/2014LORIS338/document.
Energy is a major concern in wireless sensor networks (WSN). These devices are typically battery operated and provided with a limited amount of energy. As a consequence, the time during which sensors can monitor the interesting phenomena and communicate through wireless signals might be limited because of (sometimes) irreplaceable batteries. Additionally, it is very common for WSN to be usedin remote or hostile environments which possibly makes necessary a random placement strategy (by using an airplane, a drone or a helicopter). Hence, the sensors location is not known a priori and approaches to efficiently use the energy are needed to answer to network topologies only known after sensors deployment. This thesis explores the use of column generation to efficiently use the energy in WSN. It is shown that column generation can be used as a general framework to tackle different problems in WSN design. Several versions of the problem and models for the operation of the WNS are adapted to be solved through column generation. These approaches take advantage of the natural way that column generation offers to consider different features of the WSN operation. Additionally, some computational improvements are proposed to keep the column generation method operating as an efficient exact approach. Hybrid strategies combining column generation with (meta)heuristic and exact approaches are considered and evaluated. The computational experiments demonstrate the efficiency of the proposed approaches and provide practitioners on WSN research with strategies to compute upper bounds to evaluate heuristic centralized and decentralized approaches. Finally, some future directions of research are provided based on the performance and adaptability of column generation to consider more sophisticated models and characteristics newly introduced in sensor devices
Pouilly-Cathelain, Maxime. "Synthèse de correcteurs s’adaptant à des critères multiples de haut niveau par la commande prédictive et les réseaux de neurones." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG019.
This PhD thesis deals with the control of nonlinear systems subject to nondifferentiable or nonconvex constraints. The objective is to design a control law considering any type of constraints that can be online evaluated.To achieve this goal, model predictive control has been used in addition to barrier functions included in the cost function. A gradient-free optimization algorithm has been used to solve this optimization problem. Besides, a cost function formulation has been proposed to ensure stability and robustness against disturbances for linear systems. The proof of stability is based on invariant sets and the Lyapunov theory.In the case of nonlinear systems, dynamic neural networks have been used as a predictor for model predictive control. Machine learning algorithms and the nonlinear observers required for the use of neural networks have been studied. Finally, our study has focused on improving neural network prediction in the presence of disturbances.The synthesis method presented in this work has been applied to obstacle avoidance by an autonomous vehicle
Wang, Chenghao. "Contribution à l’optimisation robuste de réseaux." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2632.
This Ph.D. Thesis is focused on proposing new optimization modeling and algorithmic approaches for dealing with real-world network optimization problems arising in the transportation and telecommunications fields. Since the focus has been on real-world applications, a relevant aspect that has been taken into account is data uncertainty, i.e. the fact that the value of a subset of input data of the problem is not exactly known when the problem is solved. More precisely, in the context of transportation problems, it was considered the flight level assignment problem, which arises in air traffic management. It aims at establishing the flight levels of a set of aircraft in order to improve the total assignment revenue, to reduce the total number of flight conflicts and also the total en-route delay. In this context, we proposed a new chance-constrained optimization problem and iterative constraint-generation heuristic which is based on both analytical and sampling methods. Besides transportation problems, this Thesis has also focused on the optimal design of 5th generation of wireless networks (5G) considering Superfluid and virtual architectures. Specifically, the 5G Superfluid architecture is based on atomic virtual entities called Reusable Functional Block (RFB). We investigated the problem of minimizing the total installation costs of a 5G Superfluid network (composed of virtual entities and realized over a physical network) while guaranteeing constraint on user coverage, downlink traffic performance and technical constraints on RFBs of different nature. To solve this hard problem, we proposed a Benders decomposition approach. Concerning instead the design of general virtual networks, we adopted a green paradigm that pursues energy-efficiency and tackled a state-of-the-art robust mixed integer linear programming formulation of the problem, by means of a new matheuris tic based on combining a genetic algorithm with exact large neighborhood searches. Results of computational tests executed considering realistic problem instances have shown the validity of all the new optimization modeling and algorithmic approaches proposed in this Thesis for the transportation and telecommunications problems sketched above
Klok, Zacharie-Francis. "Analyse du comportement hétérogène des usagers dans un réseau." Thèse, 2014. http://hdl.handle.net/1866/11905.
Using transportation roads enables workers to reach their work facilities. Security and traffic jam issues are all the more important given that the number of vehicles is always increasing and we will focus on merchandise transporters in this study. Dangerous items transportation is under strict control as it is for example forbidden for them to be carried through a tunnel or across a bridge. Some transporters may drive a vehicle that has defects or/and they may be ta\-king some forbidden roads so as to reach their destination faster. Transportation of goods is regulated by the law and there exists a control system, whose purpose is to detect frauds and to make sure controlled vehicles are in order. The strategic deployment of control resources can be based on the knowledge of transporters behaviour, which is going to be studied through their route choice analysis. The number of routes can be unbounded especially if we consider loops, which leads to a complex problem to be solved. We can also mention issues closely related to route choice problem using discrete choice models such as correlation between routes sharing links and point out the fact that human decision process is not considered something easy. A route choice problem can be modelled based on the random utility theory and as a consequence we will focus on the discrete choice models. We are going to use such model on the real road network of Quebec and we will derive an expression of the probability, for a transporter, to pick one route. We are going to explain the way we did our study. It started first by doing a data description job as we are convinced this is a step that will help other analysts to have a clear view of the data situation. Some data are network related and the corresponding attributes collected will be used to model the road network of Quebec. We will use some attributes to explain the utility function, which leads to the definition of the function that gives the probability that a user takes a given route. Once this function is fully specified, the behaviour study can be done, except that we have a set of observations that are absolutely incomplete. When observations are a gathering of data collected during a road control, the information they provide us is not enough and thus, the parameters estimation will fail. We might seem blocked but in fact, we brought the idea of using simulated observations. We are going to estimate model parameters with firstly complete observations and in order to imitate the real conditions, we then are going to use partial observations. This constitutes a main challenge and we overcome it by using the results presented in (Bierlaire et Frejinger, 2008) combined with those from (Fosgerau, Frejinger et Karlström, 2013). We will demonstrate that even though the observations used are simulated, we will deliver conclusions that can be useful for road network managers. The main results we provide in this work is that estimation can be done with a 0,05 signification level on real road network of Quebec, while the observations are incomplete. Eventually, our results should motivate network managers to improve the set of questions they use to collect data as it would help them to strengthen their knowledge about the merchandise transporters and hopefully, the decision process will lead to optimized resource deployments.