Orientador: Cid Carvalho de Souza / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-15T07:24:38Z (GMT). No. of bitstreams: 1 Piva_Breno_M.pdf: 1251793 bytes, checksum: bf559620a7bdefeec032b5c87d196b5b (MD5) Previous issue date: 2009 / Resumo: O problema do Máximo Subgrafo Induzido Comum (MSIC) pertence a classe NP-difícil e possui aplicações em diversas áreas. Apesar de sua complexidade, ainda é importante conhecer soluções exatas para instâncias deste problema. Os algoritmos exatos encontrados na literatura buscam resolvê-lo através de técnicas de backtracking ou através de sua redução para o problema da Clique Máxima. Neste trabalho procuramos dar uma solução exata para o MSIC, tratando-o diretamente através da utilização de modelos de Programação Linear Inteira (PLI) e técnicas de combinatória poliédrica. Assim, realizamos um estudo teórico do poliedro do MSIC e fomos capazes de encontrar algumas desigualdades válidas fortes, inclusive com provas de que algumas delas representam facetas daquele poliedro. Adicionalmente, provamos que existe uma equivalâencia entre o modelo PLI aqui apresentado para o MSIC e uma formulação bem conhecida para o problema da Clique Máxima. Posteriormente, foram implementados algoritmos de Branch-and-Bound (B&B) e Branch-and-Cut (B&C) utilizando as desigualdades encontradas e algumas técnicas para tentar tornar os algoritmos mais eficientes. Experimentos foram executados com os algoritmos implementados neste trabalho e, também, com um algoritmo já existente para resolver o problema da Clique, chamado Cliquer. Os resultados foram comparados e, dentre os algoritmos de PLI, constatamos que o mais eficiente foi aquele que utilizou uma formulação para o MSIC que chamamos de Clique-IS, utilizando B&B e técnicas mais básicas que outros algoritmos. Este algoritmo mostrou-se mais eficiente, inclusive, que um algoritmo PLI com um modelo baseado no problema da Clique Máaxima. Este fato sugere que para uma abordagem baseada em PLI, vale a pena utilizar uma formulação do MSIC diretamente, ao invés de uma que se apóie na redução deste para o problema da Clique Máxima. Ja a comparaçao do melhor algoritmo desenvolvido neste trabalho com o Cliquer, mostrou que este último é mais eficiente. Para que um algoritmo baseado em PLI (utilizando uma formulação com as mesmas variáveis usadas por nós) tivesse alguma chance de vencer um algoritmo combinatório como o Cliquer, seria necessário conhecer mais desigualdades que estivessem ativas na solução ótima do problema / Abstract: The Maximum Common Subgraph problem (MSIC) is in MV-hard and has applications in several fields. Despite its complexity, it is still important to know exact solutions for instances of this problem. The exact algorithms found in literature try to solve it through backtracking techniques or through its reduction to the Maximum Clique problem. In this work we try to give an exact solution to MSIC by addressing it directly, using Linear Integer Programming (PLI) and polyhedral combinatorics techniques. So, we performed a study of the MSIC polyhedron and we were able to find some strong valid inequalities, including some that were proven to define facets of that polyhedron. Additionally, we proved that an equivalence between the PLI model presented here for MSIC and a well known formulation for the Maximum Clique problem exists. Later, Branch-and-Bound (B&B) and Branch-and-Cut (B&C) algorithms were implemented using the inequalities found and some techniques to try to render the algorithms more efficient. Experiments were performed with the algorithms implemented in this work and, also, with an already existing algorithm to solve the Maximum Clique problem, called Cliquer. The results were compared and, among the PLI algorithms, we found that the most efficient was the one that used the formulation which we called Clique-IS, using B&B and more basic techniques than other algorithms. This algorithm was even more efficient than a PLI algorithm with a Clique-based model. This fact suggests that for a PLI approach it is worth to use a formulation based on the MSIC polyhedron instead of one based on its reduction to the Maximum Clique problem. The comparison of the best algorithm developed in this work with Cliquer, though, showed that the latest is more efficient. In order to some PLI-based algorithm (using a formulation with the same variables used by us) to have any chance of outperforming a combinatorial algorithm like Cliquer, it would be necessary to know more inequalities that are active in the problem's optimal solution / Mestrado / Otimização Combinatoria / Mestre em Ciência da Computação

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / Neste trabalho avaliamos mÃtodos heurÃsticos e exatos para o Problema de Particionamento de Conjuntos (PPC). Realizamos testes computacionais com heurÃsticas lagrangeanas baseadas em algoritmos gulosos, busca tabu e mÃtodo de otimizaÃÃo pelo subgradiente. Os resultados obtidos, comparados com os da literatura, comprovam a eficiÃncia de nossas heurÃsticas na obtenÃÃo de limites inferiores e superiores de boa qualidade, em tempo computacional razoÃvel, para instÃncias da literatura. Utilizamos um esquema de Branch and Bound para tentar resolver instÃncias do PPC ÃÂotimalidade e para comprovar a qualidade dos resultados alcanÃados por nossas heurÃsticas. / In this work we evaluate both exact and heuristic methods for the set partitioning problem (SPP). These heuristics are based on greedy algorithms, tabu search and subgradient optimization. Computational experiments performed on benchmark instances of the problem indicate that our heuristics are competitive with existing ones from the literature in obtaining both lower and upper bounds of good quality in reasonable execution time. We use a Branch and Bound algorithm that allows to prove optimality of solutions obtained by our heuristics for a large set of benchmark instances of the SPP. Thus, we show that our heuristics are efficient in obtaining feasible solutions of good quality for this problem.

Submitted by Miriam Lucas (miriam.lucas@unioeste.br) on 2018-02-22T16:42:27Z No. of bitstreams: 2 Miguel_Angel_Sanchez_Cuno_2016.pdf: 1159111 bytes, checksum: 5e8f5e6fcd310a19270e2164cb09c3e3 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-02-22T16:42:27Z (GMT). No. of bitstreams: 2 Miguel_Angel_Sanchez_Cuno_2016.pdf: 1159111 bytes, checksum: 5e8f5e6fcd310a19270e2164cb09c3e3 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-08-16 / Fundação Parque Tecnológico de Itaipu / This work presents the development of a Hybrid Algorithm to solve the problem of Planning the Expansion of Medium Voltage Overhead Networks. The Hybrid Algorithm uses two strategies to solve the problem. First uses a Constructive Heuristic Algorithm that tries to work with parameters instead of working with variables, with the objective of reducing the convergence time to the research process trying not to impair the quality of the solution. The second strategy is based in a Branch and Bound Algorithm, that uses the solution of the problem obtained as a starting point while the first strategy is running. Thus, this solution is used like incumbent in the second process. In this context the hybrid algorithm developed and implemented in this work, takes advantage of reducing the convergence time of the Constructive Heuristic Algorithm and the advantage of guarantee that the solution has the best quality, which are the solutions produced by algorithms type Branch and Bound. The Algorithm has been tested in three test systems, being established a plan to expand overhead medium voltage networks for each system. / Neste trabalho é apresentado um Algoritmo Híbrido para resolver o problema de Planejamento da Expansão de Redes Aéreas de Média Tensão. O Algoritmo Híbrido utiliza duas estratégias para resolver o problema. A primeira utiliza um Algoritmo Heurístico Construtivo que procura trabalhar com parâmetros ao invés de trabalhar com variáveis, com o objetivo de reduzir o tempo de convergência do processo de busca procurando não prejudicar a qualidade da solução. A segunda estratégia é baseada em um Algoritmo do tipo Branch and Bound, que utiliza a solução do problema obtida durante a execução da primeira estratégia como um ponto de partida. Assim, esta solução é usada como incumbente neste segundo processo. Neste contexto, o Algoritmo Híbrido desenvolvido e implementado neste trabalho, aproveita a vantagem de reduzir o tempo de convergência do Algoritmo Heurístico Construtivo e a vantagem de garantir que a solução seja a de melhor qualidade, que são as soluções produzidas por algoritmos do tipo Branch and Bound. O Algoritmo foi testado em três sistemas testes, sendo estabelecido um plano para a expansão de redes aéreas de média tensão para cada sistema

Within such disciplines as Management Science, Information and Computer Science, Engineering, Mathematics and Operations Research, problems of cutting and packing (C&P) of concrete and abstract objects appear under various specifications (cutting problems, knapsack problems, container and vehicle loading problems, pallet loading, bin packing, assembly line balancing, capital budgeting, changing coins, etc.), although they all have essentially the same logical structure. In cutting problems, a large object must be divided into smaller pieces; in packing problems, small items must be combined to large objects. Most of these problems are NP-hard. Since the pioneer work of L.V. Kantorovich in 1939, which first appeared in the West in 1960, there has been a steadily growing number of contributions in this research area. In 1961, P. Gilmore and R. Gomory presented a linear programming relaxation of the one-dimensional cutting stock problem. The best-performing algorithms today are based on their relaxation. It was, however, more than three decades before the first `optimum? algorithms appeared in the literature and they even proved to perform better than heuristics. They were of two main kinds: enumerative algorithms working by separation of the feasible set and cutting plane algorithms which cut off infeasible solutions. For many other combinatorial problems, these two approaches have been successfully combined. In this thesis we do it for one-dimensional stock cutting and two-dimensional two-stage constrained cutting. For the two-dimensional problem, the combined scheme provides mostly better solutions than other methods, especially on large-scale instances, in little time. For the one-dimensional problem, the integration of cuts into the enumerative scheme improves the results of the latter only in exceptional cases. While the main optimization goal is to minimize material input or trim loss (waste), in a real-life cutting process there are some further criteria, e.g., the number of different cutting patterns (setups) and open stacks. Some new methods and models are proposed. Then, an approach combining both objectives will be presented, to our knowledge, for the first time. We believe this approach will be highly relevant for industry.

A new branch of machine and deep learning models has evolved in constrained optimization, specifically in mixed integer programming problems (MIP). These models draw inspiration from earlier solver methods, primarily the heuristic, branch and bound. While utilizing the branch and bound framework, machine and deep learning models enhance either the computational efficiency or performance of the model. This thesis examines how imitating different variable selection strategies of classical MIP solvers behave on a state-of-the-art deep learning model. A recently developed deep learning algorithm is used in this thesis, which represents the branch and bound state as a bipartite graph. This graph serves as the input to a graph network model, which determines the variable in the MIP on which branching occurs. This thesis compares how imitating different classical branching strategies behaves on different algorithm outputs and, most importantly, time span. More specifically, this thesis conducts an empirical study on a MIP known as the facility location problem (FLP) and compares the different methods for imitation. This thesis shows that the deep learning algorithm can outperform the classical methods in terms of time span. More specifically, imitating the branching strategies resulting in small branch and bound trees give rise to a more rapid performance in finding the global optimum. Lastly, it is shown that a smaller embedding size in the network model is preferred for these instances when looking at the trade-off between variable selection and time cost. / En ny typ av maskin och djupinlärningsmodeller har utvecklats inom villkors optimering, specifikt för så kallade blandade heltalsproblem (MIP). Dessa modeller hämtar inspiration från tidigare lösningsmetoder, främst en heuristisk som kallas “branch and bound”. Genom att använda “branch and bound” ramverket förbättrar maskin och djupinlärningsmodeller antingen beräkningshastigheten eller prestandan hos modellen. Denna uppsats undersöker hur imitation av olika strategier för val av variabler från klassiska MIP-algoritmer beter sig på en modern djupinlärningsmodell. I denna uppsats används en nyligen utvecklad djupinlärningsalgoritm som representerar “branch and bound” tillståndet som en bipartit graf. Denna graf används som indata till en “graph network” modell som avgör vilken variabel i MIP-problemet som tas hänsyn till. Uppsatsen jämför hur imitation av olika klassiska “branching” strategier påverkar olika algoritmutgångar, framför allt, tidslängd. Mer specifikt utför denna uppsats en empirisk studie på ett MIP-problem som kallas för “facility location problem” (FLP) och jämför imitationen av de olika metoderna. I denna uppsats visas det att denna djupinlärningsalgoritm kan överträffa de klassiska metoderna när det gäller tidslängd. Mer specifikt ger imitation av “branching” strategier som resulterar i små “branch and bound” träd upphov till en snabbare prestation vid sökning av den globala optimala lösningen. Slutligen visas det att en mindre inbäddningsstorlek i nätverksmodellen föredras i dessa fall när man ser på avvägningen mellan val av variabler och tidskostnad.

O objetivo desse trabalho é o de estudar o problema de programação de tarefas num ambiente produtivo com uma única máquina com data comum de entrega. Nesse caso, as tarefas, depois de processadas uma única vez na máquina, devem ser entregues em uma data comum e sofrem penalidades de adiantamento e de atraso conforme o instante em que são completadas. Na prática, esse problema é encontrado em casos de pedidos de lotes de produtos com data de entrega comum préespecificada, embarques para exportação e material químico ou misturas que têm vida média de curta duração. Problemas desse tipo são NP-hard (Hall, Kubiak & Sethi, 1991; Hoogeven & van de Velde, 1991), sendo comumente tratados na literatura através de heurísticas e meta-heurísticas. Visto não ser de nosso conhecimento a existência na literatura de tratamento desse problema através de métodos exatos, propôs-se a utilização de um algoritmo do tipo branch-and-bound para obtenção da solução ótima do problema que minimize a soma das penalidades de adiantamento e de atraso. No desenvolvimento do algoritmo, a utilização de propriedades do problema foi importante na elaboração de limitantes inferiores e regras de dominância que melhoraram a eficiência do modelo. Os experimentos realizados avaliaram o desempenho de diferentes critérios elaborados, como escolha do nó pai, limitante inferior, ordem de execução das estratégias e ordem de construção da seqüência. Os resultados obtidos mostraram-se robustos quando comparados com o benchmark da literatura e revelaram o bom desempenho do modelo para problemas de pequeno porte, superando o desempenho de programas de otimização comerciais. / The objective of this work is to study the single-machine scheduling problem with a common due date. In this case, jobs, after be processed only once in the machine, must be delivered in a common due date and they are penalized of earliness or tardiness according to their completion time. This problem is found in cases of batch production with prespecified common due date, exportation shipping and chemical material that has short half-life period. This kind of problem is NP-hard (Hall, Kubiak & Sethi, 1991; Hoogeven & van de Velde, 1991) and it has been treated in the literature by heuristics and meta-heuristics. Not having knowledge about previous treatment by exact methods in the literature, it was proposed the implementation of a branch-and-bound algorithm to obtain the optimal solution that minimizes the total weighted earliness and tardiness penalties. In the development of the algorithm, the utilization of problem properties was important to the elaboration of lower bounds and pruning rules that have enhanced the efficiency of the model. The realized tests have evaluated the performance of different criteria, like the choice of father node, lower bound, strategy execution order and sequence construction order. The obtained results have demonstrated robustness comparing to benchmark and they have revealed the good working of the model for small problems, overcoming optimization software performance.

A very wide-ranging subgroup of vehicle routing problems from the graph theory is a common and frequent problem handled daily by transport companies, airline businesses, hi-tech companies with planning drilling of printed circuits boards or other companies from different industries. During numerous previous researches of these problems a lot of analyses were made and many solutions proposed -- of which an outline is in this paper. Some of them giving better or worse results in longer or shorter computing time. In spite of the fact that the processors and new technologies performance is increasing, with some algorithms we cannon compute the result in a reasonable time. That is why this paper is asking a question, if there can be found a fitting algorithm which could be applied on different and faster processing unit structures so it could be ensured a multiple computing speed increase so far. The analysis was carried out using computer experiments on a new build and implemented branch and bound algorithm with a matrix rate reduction.

In this thesis, we study the Euclidean Steiner tree problem (ESTP) which arises in the field of combinatorial optimization. The ESTP asks for a network of minimal total edge length spanning a set of given terminal points in Rd with the ability to add auxiliary connecting points (Steiner points) to decrease the overall length of the network. The graph theory literature contains extensive studies of exact, approximation, and heuristic algorithms for ESTP in the plane, but less is known in higher dimensions. The contributions of this thesis include a heuristic algorithm and enhancements to an exact algorithm for solving the ESTP. We present a local search heuristic for the ESTP in Rd for d ≥ 2. The algorithm utilizes the Delaunay triangulation to generate candidate Steiner points for insertion, the minimum spanning tree to create a network on the inserted points, and second order cone programming to optimize the locations of Steiner points. Unlike other ESTP heuristics relying on the Delaunay triangulation, the algorithm inserts Steiner points probabilistically into Delaunay triangles to achieve different subtrees on subsets of terminal points. The algorithm extends effectively into higher dimensions, and we present computational results on benchmark test problems in Rd for 2 ≤ d ≤ 5. We develop new geometric conditions derived from properties of Steiner trees which bound below the number of Steiner points on paths between terminals in the Steiner minimal tree. We describe conditions for trees with a Steiner topology and show how these conditions relate to the Voronoi diagram. We describe more restrictive conditions for trees with a full Steiner topology and their implementation into the algorithm of Smith (1992). We present computational results on benchmark test problems in Rd for 2 ≤ d ≤ 5.

Cette thèse est principalement consacrée à l'association des méthodes de points intérieurs et des techniques de l'optimisation DC et DCA pour résoudre les problèmes d'optimisation non convexes de grande taille.<br />La thèse comporte trois parties : <br />la première partie est consacrée aux techniques d'optimisations locales et s'articule autour des méthodes de points intérieurs et de la programmation DC. Nous y développons deux algorithmes. Après une présentation non exhaustive de la programmation DC, des méthodes de points intérieurs et des propriétés essentielles de la classe des matrices quasi-définies au chapitre un, nous présentons au chapitre deux un nouvel algorithme basé sur une reformulation des conditions d'optimalité de Karush-Kuhn-Tucker. Le troisième chapitre est consacré à l'intégration des techniques d'optimisation DC dans un schéma de points intérieurs, c'est l'algorithme IPDCA.<br />La seconde partie de la thèse est consacrée aux solutions globales de problèmes de programmation quadratique. Dans le premier chapitre de cette partie nous explorons l'intégration d'IPDCA dans un schéma B&B. Le second chapitre de la partie est consacré à la résolution de problèmes quadratiques à variables 0-1 par un schéma B\&B dans lequel nous faisons intervenir IPDCA. Le troisième chapitre est quant à lui consacré à l'optimisation monotone due au Professeur Tuy. Nous examinons plus particulièrement son intégration dans un B&B dans lequelle DCA est appelé pour améliorer la borne supérieure.<br />Le quatrième et dernier chapitre de cette partie est consacré à une procédure de redémarrage de DCA. <br />La dernière partie de la thèse est consacrée aux applications industrielles. Nous y appliquons les deux algorithmes développés dans la première partie de la thèse à un problème de mécanique de structure de grande dimension et à un problème en Data Mining.

<p>This thesis contributes to theoretical and quantitative aspects of machine scheduling. In fact, it is dedicated to the issue of scheduling n jobs on one single machine. The scope is limited to deterministic problems - i.e. those with all data available and known with certainty in advance - with tardiness involved objectives; hence, the common denominator of all problems addressed are jobs with a predetermined due date assigned to. A job is finished on time as long as it is completed before its due date, otherwise it is said to be tardy. Since the single machine utilized is assumed to be restricted to process at most one job at a time, the aim is to find a proper sequence - a schedule - of how to process the jobs in order to best fulfill a certain objective. The contribution of this thesis aims at giving a state of the art survey and detailed review of research effort considering the objectives "minimizing the number of tardy jobs" and "minimizing the weighted number of tardy jobs". Further, the objectives of "minimizing the total tardiness", "minimizing the total weighted tardiness" and "minimizing the maximum tardiness" are adumbrated but reduced to a rough overview of research effort made.</p>