Dans cette thèse nous considérons une classe de problèmes d’optimisation ayant une particularité : des décisions à la fois discrètes et continues doivent être prises simultanément. Ces problèmes se posent dans de nombreuses applications pratiques, comme par exemple dans les réseaux de télécommunications à large bande passante et dans les problèmes de transport écologique, où les ressources disponibles peuvent être très légèrement consommées ou réparties. Ces problèmes se sont avérés être plus difficiles à résoudre que leurs homologues purement discrets. Des méthodes efficaces pour la résolution de ces problèmes sont proposées dans cette thèse. Notre approche est de prendre en compte des variantes de problèmes classiques d’optimisation combinatoire appartenant à trois domaines : packing, routage et routage/ packing intégré. Les résultats obtenus suggèrent l’existence de méthodes efficaces, réduisant l’effort de calcul nécessaire pour résoudre ce type de problème. La plupart du temps, ces méthodes sont basées sur l’exploitation de la structure des solutions optimales pour réduire l’espace de recherche. / In this thesis we consider a class of optimization problems having adistinctive feature : both discrete and continuous decisions need to betaken simultaneously. These problems arise in many practical applications,for example broadband telecommunications and green transportation problems, where resources are available, that can be fractionally consumed or assigned. These problems are proven of being harder than their purely discrete counterpart. We propose effective methodologies to tackle them. Our approach is to consider variants of classical combinatorial optimization problems belonging to three domains : packing, routing, and integrated routing / packing. Our results suggest that indeed effective approaches exist, reducing the computational effort required for solving the problem. Mostly, they arebased on exploiting the structure of optimal solutions to reduce the search space. / In questa tesi affrontiamo una classe di problemi di ottimizzazione con una caratteristica in comune : sia le decisioni discrete che quelle continue devono essere prese simultaneamente. Questi problemi emergono in molti campi, come ad esempio le nelle telecomunicazioni abanda larga e in problemi di trasporto ecologico, dove le risorse disponibili possono essere consumate o assegnate in modo frazionario.Questi problemi sono generalmente più difficili da risolvere rispetto alla loro controparte puramente combinatoria. Noi proponiamo metodologie efficaci per affrontarli. Con il nostro approccio consideriamo varianti di problemi classici nel campo dell’ottimizzazione combinatoriache appartengono a tre domini : impaccamento, instradamento einstradamento / impaccamento integrati. I nostri risultati suggeriscono l’esistenza di approcci efficienti che riducono lo sforzo computazionale necessario per risolvere questi problemi. Nella maggior parte deicasi, tali approcci sono basati sullo sfruttamento di particolari proprietà della struttura delle soluzioni ottime in modo da ridurre lo spaziodi ricerca.

Bin packing is a very important and popular research area in the computer science field. Past work showed many good and real-world packing algorithms. How- ever, due to the complexity of the problem in multiple-dimensional bin packing, also called hyperbox packing, we need more practical packing algorithms for its real-world applications. In this dissertation, we extend 1D packing algorithms to hyperbox packing prob- lems via a general framework that takes two inputs of a 1D packing algorithm and an instance of hyperbox packing problem and outputs a hyperbox packing algorithm. The extension framework significantly enriches the family of hyperbox-packing algorithms, generates many framework-based algorithms, and simultaneously calls for the analysis for those algorithms. We also analyze the performance of a couple of framework-based algorithms from two perspectives of worst-case performance and average-case performance. In worst- case analysis, we use the worst-case performance ratio as our metric and analyze the relationship of the ratio of framework-based algorithms and that of the corresponding 1D algorithms. We also compare their worst-case performance against two baselines: strip optimal algorithms and optimal algorithms. In average-case analysis, we use expected waste as a metric, analyze the waste of optimal hyperbox packing algorithms, and estimate the asymptotic forms of the waste for framework-based algorithms.

Fu ren da xue Zhongguo wen xue yan jiu suo shuo shi lun wen. / Includes bibliographical references (p. 213-214).

Thesis--London University, 1935. / "Select bibliography": p. 258-262.

Fu ren da xue Zhongguo wen xue yan jiu suo shuo shi lun wen. / Bibliography: p. 213-214.

No description available.

Zugl.: Freiburg (Breisgau), Univ., Diss., 2006

Thesis (Ph. D.)--Boston University, 2005. / Vita. Includes bibliographical references (leaves [418]-443).

In immer flexibler werdenden Produktionsanlagen und -abläufen spielen Roboter aufgrund deren inheränter Flexibilität eine entscheidende Rolle. Eine klassische Aufgabe in diesem Kontext ist die Vereinzellung chaotisch gelagerter Teile (auch Bin-Picking genannt). In diesem Beitrag wird die Situation betrachtet, in der die Modellfabrik der FH Münster mit zylindrischen Rohteile versorgt werden soll, wobei diese Rohteile chaotisch gelagert, d. h. in beliebiger Orientierung, vorliegen. Dieser Beitrag präsentiert ein Konzept sowie dessen Realisierung, um die Rohteile für die spätere Übergabe an die Modellfabrik korrekt auszurichten. Das Konzept umfasst (1) die Erkennung der Lage der Rohteil basierend auf Tiefenbildinformationen und (2) das Greifen der Objekte für die Umorientierung in die gewünschte Lage. Die Implementierung des Konzeptes erfolgt in C++ unter Verwendung von des Robot Operating Systems sowie der Point Cloud Library.

An online algorithm must make decisions immediately and irrevocably based only on a part of the input without any knowledge of the future part of the input. We introduce the competitive analysis of online algorithms, a standard worst-case analysis, and present main results of this analysis on the problem of online Bin Packing and on some of its variants. In Bin Packing, a sequence of items of size up to 1 arrives to be packed into the minimal number of unit capacity bins. Mainly, we focus on Colored Bin Packing in which items have also a color and we cannot pack two items of the same color adjacently in a bin. For Colored Bin Packing, we improve some previous results on the problem with two colors and present the first results for arbitrarily many colors. Most notably, in the important case when all items have size zero, we give an optimal 1.5-competitive algorithm. For items of arbitrary size we present a lower bound of 2.5 and a 3.5-competitive algorithm. Powered by TCPDF (www.tcpdf.org)