The crossing number of a graph 𝐺 is the minimum number of crossings in any drawing of 𝐺 in the plane. The rectilinear crossing number of 𝐺 is the minimum number of crossings in any straight-line drawing of 𝐺. The Fáry-Wagner theorem states that planar graphs have rectilinear crossing number zero. By Wagner’s theorem, that is equivalent to stating that every graph that excludes 𝐾₅ and 𝐾₃,₃ as minors has rectilinear crossing number 0. We are interested in discovering other proper minor-closed families of graphs which admit strong upper bounds on their rectilinear crossing numbers. Unfortunately, it is known that the crossing number of 𝐾₃,ₙ with 𝑛 ≥ 1, which excludes 𝐾₅ as a minor, is quadratic in 𝑛, more specifically Ω(𝑛²). Since every 𝑛-vertex graph in a proper minor closed family has O(𝑛) edges, the rectilinear crossing number of all such graphs is trivially O(𝑛²). In fact, it is not hard to argue that O(𝑛) bound on the crossing number is the best one can hope for general enough proper minor-closed families of graphs and that to achieve O(𝑛) bounds, one has to both exclude a minor and bound the maximum degree of the graphs in the family. In this thesis, we do that for bounded degree graphs that exclude a single-crossing graph as a minor. A single-crossing graph is a graph whose crossing number is at most one. The main result of this thesis states that every graph 𝐺 that does not contain a single-crossing graph as a minor has a rectilinear crossing number O(∆𝑛), where 𝐺 has 𝑛 vertices and maximum degree ∆. This dependence on 𝑛 and ∆ is best possible. Note that each planar graph is a single-crossing graph, as is the complete graph 𝐾₅ and the complete bipartite graph 𝐾₃,₃. Thus, the result applies to 𝐾₅-minor-free graphs, 𝐾₃,₃-minor free graphs, as well as to bounded treewidth graphs. In the case of bounded treewidth graphs, the result improves on the previous best known bound of O(∆² · 𝑛) by Wood and Telle [New York Journal of Mathematics, 2007]. In the case of 𝐾₃,₃-minor free graphs, our result generalizes the result of Dujmovic, Kawarabayashi, Mohar and Wood [SCG 2008].

Cette thèse est composée de deux parties, chacune portant sur un sous-domaine de l'optimisation combinatoire a priori distant de l'autre. Le premier thème de recherche abordé est la programmation biniveau stochastique. Se cachent derrière ce terme deux sujets de recherche relativement peu étudiés conjointement, à savoir d'un côté la programmation stochastique, et de l'autre la programmation biniveau. La programmation mathématique (PM) regroupe un ensemble de méthodes de modélisation et de résolution, pouvant être utilisées pour traiter des problèmes pratiques que se posent des décideurs. La programmation stochastique et la programmation biniveau sont deux sous-domaines de la PM, permettant chacun de modéliser un aspect particulier de ces problèmes pratiques. Nous élaborons un modèle mathématique issu d'un problème appliqué, où les aspects biniveau et stochastique sont tous deux sollicités, puis procédons à une série de transformations du modèle. Une méthode de résolution est proposée pour le PM résultant. Nous démontrons alors théoriquement et vérifions expérimentalement la convergence de cette méthode. Cet algorithme peut être utilisé pour résoudre d'autres programmes biniveaux que celui qui est proposé.Le second thème de recherche de cette thèse s'intitule "problèmes de coupe et de couverture partielles dans les graphes". Les problèmes de coupe et de couverture sont parmi les problèmes de graphe les plus étudiés du point de vue complexité et algorithmique. Nous considérons certains de ces problèmes dans une variante partielle, c'est-à-dire que la propriété de coupe ou de couverture dont il est question doit être vérifiée partiellement, selon un paramètre donné, et non plus complètement comme c'est le cas pour les problèmes originels. Précisément, les problèmes étudiés sont le problème de multicoupe partielle, de coupe multiterminale partielle, et de l'ensemble dominant partiel. Les versions sommets des ces problèmes sont également considérés. Notons que les problèmes en variante partielle généralisent les problèmes non partiels. Nous donnons des algorithmes exacts lorsque cela est possible, prouvons la NP-difficulté de certaines variantes, et fournissons des algorithmes approchés dans des cas assez généraux. / This thesis is composed of two parts, each part belonging to a sub-domain of combinatorial optimization a priori distant from the other. The first research subject is stochastic bilevel programming. This term regroups two research subject rarely studied together, namely stochastic programming on the one hand, and bilevel programming on the other hand. Mathematical Programming (MP) is a set of modelisation and resolution methods, that can be used to tackle practical problems and help take decisions. Stochastic programming and bilevel programming are two sub-domains of MP, each one of them being able to model a specific aspect of these practical problems. Starting from a practical problem, we design a mathematical model where the bilevel and stochastic aspects are used together, then apply a series of transformations to this model. A resolution method is proposed for the resulting MP. We then theoretically prove and numerically verify that this method converges. This algorithm can be used to solve other bilevel programs than the ones we study.The second research subject in this thesis is called "partial cut and cover problems in graphs". Cut and cover problems are among the most studied from the complexity and algorithmical point of view. We consider some of these problems in a partial variant, which means that the cut or cover property that is looked into must be verified partially, according to a given parameter, and not completely, as it was the case with the original problems. More precisely, the problems that we study are the partial multicut, the partial multiterminal cut, and the partial dominating set. Versions of these problems were vertices are