Letteratura scientifica selezionata sul tema "Optimisation Combinatoire Robuste et Probabiliste"
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Tesi sul tema "Optimisation Combinatoire Robuste et Probabiliste":
Haddad, Marcel Adonis. "Nouveaux modèles robustes et probabilistes pour la localisation d'abris dans un contexte de feux de forêt". Electronic Thesis or Diss., Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLD021.
The location of shelters in different areas threatened by wildfires is one of the possible ways to reduce fatalities in acontext of an increasing number of catastrophic and severe forest fires. The problem is basically to locate p sheltersminimizing the maximum distance people will have to cover to reach the closest accessible shelter in case of fire. Thelandscape is divided in zones and is modeled as an edge-weighted graph with vertices corresponding to zones andedges corresponding to direct connections between two adjacent zones. Each scenario corresponds to a fire outbreak ona single zone (i.e., on a vertex) with the main consequence of modifying evacuation paths in two ways. First, an evacuationpath cannot pass through the vertex on fire. Second, the fact that someone close to the fire may have limited choice, ormay not take rational decisions, when selecting a direction to escape is modeled using a new kind of evacuation strategy.This evacuation strategy, called Under Pressure, induces particular evacuation distances which render our model specific.We propose two problems with this model: the Robust p-Center Under Pressure problem and the Probabilistic p-CenterUnder Pressure problem. First we prove hardness results for both problems on relevant classes of graphs for our context.In addition, we propose polynomial exact algorithms on simple classes of graphs and we develop mathematical algorithmsbased on integer linear programming
Tabia, Nourredine. "Modèles et algorithmes pour l'optimisation robuste dans les Self-Organizing Network (SON) des réseaux mobiles 4G (LTE)". Phd thesis, Université de Technologie de Belfort-Montbeliard, 2013. http://tel.archives-ouvertes.fr/tel-00983358.
Mbaye, Moustapha. "Conception robuste en vibration et aéroélasticité des roues aubagées de turbomachines". Phd thesis, Université Paris-Est, 2009. http://tel.archives-ouvertes.fr/tel-00529002.
Goka, Edoh. "Analyse des tolérances des systèmes complexes – Modélisation des imperfections de fabrication pour une analyse réaliste et robuste du comportement des systèmes". Thesis, Paris, ENSAM, 2019. http://www.theses.fr/2019ENAM0019/document.
Tolerance analysis aims toward the verification of the impact of individual tolerances on the assembly and functional requirements of a mechanical system. The manufactured products have several types of contacts and their geometry is imperfect, which may lead to non-functioning and non-assembly. Traditional methods for tolerance analysis do not consider the form defects. This thesis aims to propose a new procedure for tolerance analysis which considers the form defects and the different types of contact in its geometrical behavior modeling. A method is firstly proposed to model the form defects to make realistic analysis. Thereafter, form defects are integrated in the geometrical behavior modeling of a mechanical system and by considering also the different types of contacts. Indeed, these different contacts behave differently once the imperfections are considered. The Monte Carlo simulation coupled with an optimization technique is chosen as the method to perform the tolerance analysis. Nonetheless, this method is subject to excessive numerical efforts. To overcome this problem, probabilistic models using the Kernel Density Estimation method are proposed
Derrien, Alban. "Ordonnancement cumulatif en programmation par contraintes : caractérisation énergétique des raisonnements et solutions robustes". Thesis, Nantes, Ecole des Mines, 2015. http://www.theses.fr/2015EMNA0230/document.
Constraint programming is an approach regularly used to treat a variety of scheduling problems. Cumulative scheduling problems represent a class of problems in which non-preemptive tasks can be performed in parallel. These problems appear in many contexts, such as for example the allocation of virtual machines, the ordering process in the "cloud", personnel management or a port. Many mechanisms have been adapted and offered in constraint programming to solve scheduling problems. The various adaptations have resulted in reasoning that appear a priori significantly different. In this thesis we performed a detailed analysis of the various arguments, offering both a theoretical unified caracterization but also dominance rules, allowing a significant improvement in execution time of algorithms from the state of the art, up to a factor of seven. we also propose a new framework for robust cumulative scheduling, to find solutions that support at any time one or more tasks to be delayed while keeping a satisfactory end date of the project and without calling into question the generated scheduling. In this context, we propose an adaptation of an algorithm of the state of the art, Dynamic Sweep
Piegay, Nicolas. "Optimisation multi-objectif et aide à la décision pour la conception robuste. : Application à une structure industrielle sur fondations superficielles". Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0393/document.
Design in Civil Engineering is usually performed in a semi-probabilistic way using characteristic values which are associated with partial safety factors. However, this approach doesn’t guarantee the structure robustness with regard to uncertainties that could affect its performance during construction and operation. In this thesis, we propose a decision aid methodology for robust design of steel frame on spread foundations. Soil-structure interaction is taken into consideration in the design process implying that the design choices on foundations influence the design choices on steel frame (and vice versa). The proposed design approach uses multi-objective optimization and decision aid methods in order to obtain the best solution with respect to the decision-maker’s preferences on each criterion. Furthermore, sensitivity analyzes are performed in order to identify and quantify the most influencing uncertainty sources on variability of the structure performances. These uncertainties are modeled as random variables and propagated in the design process using latin hypercube sampling. A part of this dissertation is devoted to the effects of uncertainties involved in soil properties on the structure responses and on the design global approach
Maher, Agnès. "Programmation semi-définie positive. Méthodes et algorithmes pour le management d’énergie". Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112185/document.
The present thesis aims at exploring the potentialities of a powerful optimization technique, namely Semidefinite Programming, for addressing some difficult problems of energy management. We pursue two main objectives. The first one consists of using SDP to provide tight relaxations of combinatorial and quadratic problems. A first relaxation, called “standard” can be derived in a generic way but it is generally desirable to reinforce them, by means of tailor-made tools or in a systematic fashion. These two approaches are implemented on different models of the Nuclear Outages Scheduling Problem, a famous combinatorial problem. We conclude this topic by experimenting the Lasserre's hierarchy on this problem, leading to a sequence of semidefinite relaxations whose optimal values tends to the optimal value of the initial problem.The second objective deals with the use of SDP for the treatment of uncertainty. We investigate an original approach called “distributionnally robust optimization”, that can be seen as a compromise between stochastic and robust optimization and admits approximations under the form of a SDP. We compare the benefits of this method w.r.t classical approaches on a demand/supply equilibrium problem. Finally, we propose a scheme for deriving SDP relaxations of MISOCP and we report promising computational results indicating that the semidefinite relaxation improves significantly the continuous relaxation, while requiring a reasonable computational effort.SDP therefore proves to be a promising optimization method that offers great opportunities for innovation in energy management
Tfaili, Sara. "Contribution aux graphes creux pour le problème de tournées sur arcs déterministe et robustes : théorie et algorithmes". Thesis, Normandie, 2017. http://www.theses.fr/2017NORMLH14/document.
This dissertation consists of two main parts : in the first part, we study the detreministic capacitated arc routing problem over sparse underlying graphs wher we have developed a new transformation techniquevof sparse CARP into sparse CVRP. The second part is consecrated about the sparse CARP with travel costs uncertainty. We have given a mathematical formulation of the probleme in min-max. A worst scenario for the robust problem is then identified, and two algorithmic approaches are proposed to determine a solution of the studied problem
Griset, Rodolphe. "Méthodes pour la résolution efficace de très grands problèmes combinatoires stochastiques : application à un problème industriel d'EDF". Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0219/document.
The purpose of this Ph.D. thesis is to study optimization techniques for large-scale stochastic combinatorial problems. We apply those techniques to the problem of scheduling EDF nuclear power plant maintenance outages, which is of significant importance due to the major part of the nuclear energy in the French electricity system. We build on a two-stages extended formulation, the first level of which fixes nuclear outage dates and production profiles for nuclear plants, while the second evaluates the cost to meet the demand. This formulation enables the solving of deterministic industrial instances to optimality, by using a MIP solver. However, the computational time increases significantly with the number of scenarios. Hence, we resort to a procedure combining column generation of a Dantzig-Wolfe decomposition with Benders’ cut generation, to account for the linear relaxation of stochastic instances. We then obtain integer solutions of good quality via a heuristic, up to fifty scenarios. We further assume that outage durations are uncertain and that unexpected shutdowns of plants may occur. We investigate robust optimization methods in this context while ignoring possible recourse on power plants outage dates. We report on several approaches, which use bi-objective or probabilistic methods, to ensure the satisfaction of constraints which might be relaxed in the operating process. For other constraints, we apply a budget uncertainty-based approach to limit future re-organizations of the scheduling. Adding probabilistic information leads to better control of the price of the robustness