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See detailOptimisation and uncertainty: comparing stochastic and robust programming
Cuvelier, Thibaut ULg

Speech/Talk (2016)

Traditional optimisation tools focus on deterministic problems: scheduling airline flight crews (with as few employees as possible while still meeting legal constraints, such as maximum working time ... [more ▼]

Traditional optimisation tools focus on deterministic problems: scheduling airline flight crews (with as few employees as possible while still meeting legal constraints, such as maximum working time), finding the shortest path in a graph (used by navigation systems to give directions), etc. However, this deterministic hypothesis sometimes provides useless solutions: actual parameters cannot always be known to full precision, one reason being their randomness. For example, when scheduling trucks for freight transportation, if there is unexpected congestion on the roads, the deadlines might not be met, the company might be required to financially compensate for this delay, but also for the following deliveries that could not be made on schedule. Two main approaches are developed in the literature to take into account this uncertainty: make decision based on probability distributions of the uncertain parameters (stochastic programming) or considering they lie in a so-called ¿uncertainty set¿ (robust programming). In general, the first one leads to a large increase in the size of the problems to solve (and thus requires algorithms to work around this dimensionality curse), while the second is more conservative but tends to change the nature of the programs (which can impose a new solver technology). This talk compares the two approaches on three different cases: facility location, unit-commitment, and reservoir management. On the implementation side, multiple specific algorithms have been implemented to solve stochastic programs in order to compare their relative performance: Benders¿ decomposition, progressive hedging, and the deterministic equivalent. When comparing stochastic and robust programming, many differences appear in many aspects, even though the literature about those is very scarce. (Furthermore, those two approaches are not incompatible: both can be used in the same optimisation model to take into account different parts of the uncertainty.) Concerning solving time, stochastic programming quickly gives rise to intractable problems, which requires the development of more specific algorithm just to be able to solve them to an acceptable accuracy in decent time. What is more, the stochastic description of the uncertain values (with a discretisation of the probability distribution through scenarios) must cover all the possible uncertainty, otherwise the solution risks overfitting those scenarios, and is likely to have poor performance on close but different scenarios that may happen in practice ¿ which imposes to use a large number of scenarios, which yields very large (and hardly tractable) optimisation programs. On the other hand, by using specific uncertainty sets, robust programming yields programs that are only very slightly harder to solve, with an objective function that is very close to that of stochastic programming, but with totally different robustness properties: by using an uncertainty set computed from the scenarios, and not the scenarios themselves, it is able to withstand a much higher uncertainty than stochastic programming. However, when facing other types of uncertainty, this conclusion might turn untrue, with robust programming unable to cope with them and to bring interesting solutions to the table. [less ▲]

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See detailImplementing and Comparing Stochastic and Robust Programming
Cuvelier, Thibaut ULg

Master's dissertation (2015)

Traditional optimisation tools focus on deterministic problems: scheduling airline flight crews (with as few employees as possible while still meeting legal constraints, such as maximum working time ... [more ▼]

Traditional optimisation tools focus on deterministic problems: scheduling airline flight crews (with as few employees as possible while still meeting legal constraints, such as maximum working time), finding the shortest path in a graph (used by navigation systems to give directions, usually based on GPS signals), etc. However, this deterministic hypothesis sometimes yields useless solutions: actual parameters cannot always be known to full precision, one reason being their randomness. For example, when scheduling trucks for freight transportation, if there is unexpected congestion on the roads, the deadlines might not be met, the company might be required to financially compensate for this delay, but also for the following deliveries that could not be made on schedule. Two main approaches are developed in the literature to take into account this uncertainty: take decision based on probability distributions of the uncertain parameters (stochastic programming) or considering they lie in some set (robust programming). In general, the first one leads to a large increase in the size of the problems to solve (and thus requires algorithms to work around this dimensionality curse), while the second is more conservative but tends to change the nature of the programs (which can impose a new solver technology). Some authors [2] claim that those two mindsets are equivalent, meaning that the solutions they provide are equivalent when faced with the same uncertainty. The goal of this thesis is to explore this question: for various problems, implement those two approaches, and compare them. Is one solution more secluded from variations due to the uncertain parameters? Does it bring benefits over a deterministic approach? Is one cheaper than the other to compute? [less ▲]

Detailed reference viewed: 22 (4 ULg)
See detailCréer des applications avec Qt 5 : les essentiels
Belz, Guillaume; Cuvelier, Thibaut ULg; Diallo, Ilya et al

Book published by D-BookeR - 1re (2013)

Sortie fin 2012, la version 5 de Qt® marque une nouvelle étape dans la programmation multiplate-forme. Prenant en compte l'évolution de la demande en matière de développement applicatif, le framework se ... [more ▼]

Sortie fin 2012, la version 5 de Qt® marque une nouvelle étape dans la programmation multiplate-forme. Prenant en compte l'évolution de la demande en matière de développement applicatif, le framework se présente désormais comme une vaste boîte à outils modulaire pour créer toutes sortes d'applications à destination des principales plates-formes de bureau et des appareils mobiles. Créer des applications avec Qt 5 - Les essentiels vise à couvrir les outils fondamentaux de Qt 5. Que vous soyez débutant ou avancé, développeur ou designer, ce livre vous accompagnera dans la prise en main de cette nouvelle version. Au travers d'exemples riches et variés, il vous fournit toutes les clés pour développer des applications, vous aider à choisir vos modules graphiques, tirer parti de Qt Creator, optimiser votre développement, ou encore réussir votre migration depuis Qt 4. La part belle est naturellement faite à Qt Quick et au QML, qui recèlent les principales nouveautés, et c'est par leur apprentissage que devra commencer le débutant. À l'image du framework, ce livre a été conçu selon une architecture modulaire, à savoir en unités plus ou moins autonomes traitant d'un aspect particulier de Qt 5. Son objectif est de répondre à des besoins d'apprentissage et d'utilisation variés et de permettre au lecteur d'accéder directement aux parties qui l'intéressent. La plupart des modules étant disponibles à l'unité, vous pouvez choisir de n'acquérir que les parties dont vous avez besoin. Il vous présentera notamment : les nouveautés de Qt 5 les éléments de base pour démarrer avec Qt Qt Quick QGraphicsView Qt Creator [less ▲]

Detailed reference viewed: 88 (12 ULg)
See detailWeb sémantique : méthodes et outils pour le Web de données
Heath, Tom; Bizer, Christian; Seilles, Antoine et al

Book published by Pearson Éducation France - première (2012)

Cet ouvrage est un outil de formation et de référence pour les professionnels des métiers du web (développeurs, administrateurs bases de données, Architectes) et des métiers scientifiques. Il présente l ... [more ▼]

Cet ouvrage est un outil de formation et de référence pour les professionnels des métiers du web (développeurs, administrateurs bases de données, Architectes) et des métiers scientifiques. Il présente l'ensemble des formats, techniques, méthodes et outils (RDF , SPARQL, OWL et RDFS) pour la publication d'informations sous forme de données liées sur le web, que ces données soient existantes (mais de sources ou de format différents) ou à créer. Tout au long du livre, on suit l’avancement d’un projet exemple « Production Big Lynx » au fur et à mesure de son développement. Il comporte des chapitres inédits, écrits pour l’édition française, spécifiques sur certaines notions : les ontologies (conception, définition, bonnes pratiques), les formats RDF (les différentes façons d’écrire du RDF) et les avantages de l’adoption du Web sémantique par les entreprises (référencement, accessibilité des données, faible coût). [less ▲]

Detailed reference viewed: 248 (14 ULg)