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.

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)

Le problème de bin packing consiste à déterminer le nombre minimum de conteneurs (bins) nécessaires pour ranger un ensemble d’objets. Ce problème NP- complet fait depuis de nombreuses années l’objet de multiples travaux de recherche, théoriques et pratiques. On le retrouve entre autres dans l’industrie de découpe de tissu, de l’acier, de bois et de verre. La littérature sur le problème de bin packing est riche et les algorithmes et approches de résolution sont très diverses. Cependant, les solutions proposées par ces algorithmes peuvent ne pas être utiles quand on traite des problèmes industriels réels. Dans cette thèse, nous considérons plusieurs types de contraintes liées à des incompatibilités entre objets. Ces contraintes sont inspirées de celles rencontrées lors d’une collaboration industrielle. Le sujet de recherche de cette thèse porte sur la résolution d’une variété de problèmes de bin packing. Nous nous intéressons à des bornes inférieures et supérieures pour les trois problèmes suivants : un problème de bin packing avec conflits dans lequel des relations de compatibilité sont exprimées entre les couples d’objets ; un problème de bin packing bi-objectif dans lequel deux critères sont à minimiser, le nombre de bins utilisés et le nombre de couples en conflit placés dans le même bin ; un problème de bin packing avec objets fragiles dans lequel la somme des tailles des objets placés dans un bin ne dépasse la fragilité d’aucun de ces objets. / The bin packing problem consists in minimizing the number of containers (bins) needed to place a set of objects. This NP-complete problem has been, for many years, the subject of multiple theoretical and practical researches. It appears in many industrial applications such as cutting steel, wood and glass. The literature on the bin packing problem is rich and the algorithms and resolution approaches are also very are very diversified. However, solutions offered by these algorithms may not be useful when we deal with real industrial problems. In this thesis, we consider several types of constraints such as compatibility relations between objects. These constraints are issued from real life industrial applications. The research topic of this thesis focuses on solving a variety of bin packing problems. We are interested in lower and upper bounds for three problems: a bin packing problem with conflicts in which some compatibility relations exist between pairs of objects, a problem bi-objective bin packing in which two criteria are to minimize: the number of bins used and the number of conflicting couples of objects placed in the same bin, a problem of bin packing with fragile objects in which the sum of the sizes of objects placed in a bin does not exceed the fragility of any of these objects.

A presente pesquisa tem como objetivo estudar a integração entre dois temas clássicos da literatura de pesquisa operacional e gestão de operações: problemas de corte e empacotamento; e problemas de programação da produção. Ainda que sejam duas áreas intensamente exploradas e pesquisadas, e, ainda, que seja uma situação facilmente encontrada em sistemas de produção reais, abordagens de ambos problemas de forma coordenada ainda carecem de maiores pesquisas. Neste trabalho é feita uma revisão de ambos temas, com foco em problemas de bin packing e programação em ambiente de máquina única com objetivo de minimizar soma de atrasos e adiantamentos ponderados. Uma formulação matemática linear e inteira mista é proposta para o problema, contemplando as restrições que concernem a cada um e também à sua consideração simultânea. Como se trata de um problema que une dois outros, cada um NP-hard isoladamente, um método heurístico é proposto para obter uma solução interessante em tempos computacionais bastante reduzidos. Foram obtidas propriedades físicas de definição de data ideal de programação de um conjunto de itens atribuídos a um bin. Também é proposto um método para geração de um limitante inferior melhorado em relação a pacotes de otimização de mercado para o problema. Ambos métodos foram testados em uma massa de dados de 1.152 instâncias, geradas para retratar cenários de diferentes datas de entrega, setups, custos de atraso e adiantamento em relação à matéria-prima, tamanho de itens e número de itens na instância. Os resultados mostram-se largamente superiores aos obtidos por um otimizador genérico (CPLEX), embora ainda sejam gaps excessivamente grandes, o que reforça a dificuldade do problema. / The present research aims to explore the integration between two classic themes on operations research and operations management literature: cutting and packing problems; and production scheduling problems. Although they are intensive explored and researched areas and, besides, it\'s an easily found situation on real production systems, coordinated approaches of both themes still need deeper research. On this paper, it was done a review of both themes, focusing on bin packing problems and single-machine environment scheduling problems aiming to minimize total weighed earliness and tardiness. A mixed integer-linear mathematical formulation is proposed to the problem, including constraints referred to each problem and, also, to their simultaneous consideration. Once it\'s a problem that joins the other two, each one NP-hard solely, an heuristic method is proposed to obtain an interesting solution in reasonable computational times. Physical properties were identified, defining the best date to allocate a given lot of items to be processed together. Also, a lower bound generation method is proposed, improving the one generated by optimization softwares. Both methods were tested on a 1.152 instances mass of data, generated to represent well several scenarios of different due dates, setup times, earliness and tardiness costs compared to raw material, size of items and number the items the instance. Results show largely superiority the ones obtained by an optimization pack (CPLEX), although gaps are still excessively large, fact the reinforces problem\'s difficulty.

A presente pesquisa tem como objetivo estudar a integração entre dois temas clássicos da literatura de pesquisa operacional e gestão de operações: problemas de corte e empacotamento; e problemas de programação da produção. Ainda que sejam duas áreas intensamente exploradas e pesquisadas, e, ainda, que seja uma situação facilmente encontrada em sistemas de produção reais, abordagens de ambos problemas de forma coordenada ainda carecem de maiores pesquisas. Neste trabalho é feita uma revisão de ambos temas, com foco em problemas de bin packing e programação em ambiente de máquina única com objetivo de minimizar soma de atrasos e adiantamentos ponderados. Uma formulação matemática linear e inteira mista é proposta para o problema, contemplando as restrições que concernem a cada um e também à sua consideração simultânea. Como se trata de um problema que une dois outros, cada um NP-hard isoladamente, um método heurístico é proposto para obter uma solução interessante em tempos computacionais bastante reduzidos. Foram obtidas propriedades físicas de definição de data ideal de programação de um conjunto de itens atribuídos a um bin. Também é proposto um método para geração de um limitante inferior melhorado em relação a pacotes de otimização de mercado para o problema. Ambos métodos foram testados em uma massa de dados de 1.152 instâncias, geradas para retratar cenários de diferentes datas de entrega, setups, custos de atraso e adiantamento em relação à matéria-prima, tamanho de itens e número de itens na instância. Os resultados mostram-se largamente superiores aos obtidos por um otimizador genérico (CPLEX), embora ainda sejam gaps excessivamente grandes, o que reforça a dificuldade do problema. / The present research aims to explore the integration between two classic themes on operations research and operations management literature: cutting and packing problems; and production scheduling problems. Although they are intensive explored and researched areas and, besides, it\'s an easily found situation on real production systems, coordinated approaches of both themes still need deeper research. On this paper, it was done a review of both themes, focusing on bin packing problems and single-machine environment scheduling problems aiming to minimize total weighed earliness and tardiness. A mixed integer-linear mathematical formulation is proposed to the problem, including constraints referred to each problem and, also, to their simultaneous consideration. Once it\'s a problem that joins the other two, each one NP-hard solely, an heuristic method is proposed to obtain an interesting solution in reasonable computational times. Physical properties were identified, defining the best date to allocate a given lot of items to be processed together. Also, a lower bound generation method is proposed, improving the one generated by optimization softwares. Both methods were tested on a 1.152 instances mass of data, generated to represent well several scenarios of different due dates, setup times, earliness and tardiness costs compared to raw material, size of items and number the items the instance. Results show largely superiority the ones obtained by an optimization pack (CPLEX), although gaps are still excessively large, fact the reinforces problem\'s difficulty.

University, Diss., 2003--Kiel.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19864号 / 情博第615号 / 新制||情||107(附属図書館) / 32900 / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 岩間 一雄, 教授 永持 仁, 教授 五十嵐 淳 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DGAM

The optimization of package selection in logistics, particularly within the realm of e-commerce, offers numerous potential advantages, such as a reduction in environmental impact and decreased costs. This thesis addresses the problem of allocating items to the minimum number of packages, known as the bin packing problem, by proposing various heuristics. We develop and assess heuristics for assigning products to groups, while heuristics for accommodating these groups within packages are derived from previous research. These heuristics are evaluated within a commercial context, taking into account factors such as delivery cost, environmental impact, and their applicability in real-time systems. Our findings indicate that optimal solutions for smaller orders can be ascertained within a reasonable timeframe, while even rudimentary heuristics yield satisfactory results. It was determined that a key attribute of an effective solution was lowering the number of packages used, as this correlates with reduced shipping costs and environmental impact.

The open bin packing problem (OBPP) is a new variant of the well-known bin packing problem. In the OBPP, items are packed into bins so that the total content before the last item in each bin is strictly less than the bin capacity. The objective is to minimize the number of bins used. The applications of the OBPP can be found in the subway station systems in Hong Kong and Taipei and the scheduling in manufacturing industries. We show that the OBPP is NP-hard and propose two heuristic algorithms instead of solving the problem to optimality. We propose two offline algorithms in which the information of the items is known in advance. First, we consider the First Fit Decreasing (FFD) which is a good approximation algorithm for the bin packing problem. We prove that its asymptotic worst-case performance ratio is no more than 3/2. We observe that its performance for the OBPP is worse than that of the BPP. Consequently, we modify it by adding the algorithm that the set of largest items is the set of last items in each bin. Then, we propose the Modified First Fit Decreasing (MFFD) as an alternative and prove that its asymptotic worst-case performance ratio is no more than 91/80. We conduct empirical tests to show their average-case performance. The results show that in general, the FFD and MFFD algorithms use no more than 33% and 1% of the number of bins than that of optimal packing, respectively. In addition, the MFFD is asymptotically optimal when the sizes of items are (0,1) uniformly distributed. / Ph. D.