This master's thesis describes modified Chinese Postman Problems. These Problems are solved by (mixed) integer linear programming. The modified problems and also used approach (integer programming) belong at least to the NP complexity class. The thesis analyzes, compares and estimates computational complexity of each model. Based on this analysis, usability of described models for solving real-life problems is deduced. The models are focused on problems in urban environment. Therefore, it is possible to apply these models on problems like optimization of a waste collection or road maintenance. Graph and problem generator is programmed for purposes of this thesis.

O conteúdo deste trabalho trata da formulação para solução do problema de otimização estrutural com minimização de massa em estruturas unifilares, sujeitas a restrição de tensão, flambagem das barras isoladas e fadiga. São considerados três casos de otimização: paramétrica, de forma e dimensional. Os problemas de singularidades nas restrições de tensão e flambagem são evitados através de uma formulação que faz uso de programação inteira para solução do problema. Outra singularidade encontrada na otimização topológica é a singularidade na matriz de rigidez da estrutura. Este problema foi evitado através de uma formulação que considera a existência de matriz de rigidez regular como restrição do problema. O método de solução utilizado para resolver problema de otimização é o método dos algoritmos genéticos. As restrições do problema são impostas através da penalização da função objetivo. O método de solução mostrou-se adequado para solução dos problemas estudados. A formulação implementada é validada através da solução de problemas clássicos de otimização estrutural. Os resultados obtidos são comparados com a literatura onde verificou-se a coerência dos mesmos. Após realizar a validação, a formulação é utilizada em um estudo que tem como base uma estrutura real: uma torre de queima de gases (flare) oriundos do processo de extração e armazenagem de petróleo em uma unidade flutuante. Para o problema da torre as restrições foram determinadas com base em critérios de falha estabelecido na norma DNV. A otimização do flare permitiu minimizar a massa da estrutura sem que os critérios de falha fossem violados. Verificou-se que a metodologia proposta é adequada para solução com grande número de restrições e com diversos casos de carregamento. / The purpose of this work is the development of a methodology to solve the structural optimization problem of frame structures subject to stress, buckling of isolated members, and fatigue constraints. Three types of structural optimization problems are considered: sizing, shape and topological. The stress and buckling singularity problems are avoided by an integer design variable formulation, using integer programing to obtain the optimization problem solution. Another issue found in optimization problems is the stiffness matrix singularity. The proposed formulations include the linear system stability as a constraint in the optimization problem. A genetic algorithm is used to solve the general optimization problem. All constraints of the problem are included with a penalization equation. The results show that genetic algorithm is a good approach to solve the proposed formulation. The proposed formulation is tested for solving classical optimization problems. The obtained results are consistent with the literature. A real engineering problem is solved with proposed methodology: a gas burning tower (flare). In this problem, all constraints are based on failure criteria recommended by DNV standards. The structural optimization of this problem shows that structural mass minimization is possible without violating the failure criteria. It is observed that solution methodology deals successfully with problems with multiple constraints and load cases

Nesta dissertação, discute-se o problema de otimização de carteiras de investimento para estratégia passiva de Index Tracking. Os objetivos principais são (i) apresentar um modelo de otimização de Index Tracking e (ii) a solucionar esse modelo com uso do método heurístico de Algoritmos Genéticos (AG) para formação de carteiras com número reduzido de ativos. O índice de referência utilizado é o Ibovespa, para o período de Janeiro/2009 a Julho/2012, com um total de 890 observações diárias de preços. A partir de uma amostra de 67 ativos, são formadas carteiras sem limite de ativos e limitadas a 40, 30, 20, 10 e 05 ativos; os intervalos de rebalanceamento das carteiras são 20, 40 e 60 períodos (dias úteis), ou seja, rebalanceamento mensal, bimestral e trimestral. É verificado que, para essa amostra, não é possível formar carteiras de 20 ou menos ativos via otimização direta com o solver Cplex com menos de 1 hora de processamento e gap abaixo de 5%. Com uso da heurística de Algoritmos Genéticos, são formadas carteiras de 10 e 05 ativos com tempo de processamento em torno de 5 minutos; nesse caso, o gap médio fica abaixo de 10% para ambos os tipos de carteira. E, com tempo de processamento do AG um pouco maior, em torno de 8 minutos, o algoritmo fornece soluções para carteiras de 10 e 05 ativos com gap médio abaixo de 5%. / In this master’s thesis it is discussed the portfolio optimization problem using the passive investment strategy of Index Tracking. The main goals are (i) to present an optimization model for the Index Tracking problem and (ii) to solve this model using the heuristic approach of Genetic Algorithms (GA) to create portfolios with reduced amount of stocks. The benchmark used is the Ibovespa Index (main reference for the Brazilian Stock Market), during the period from January/2009 to July/2012 (using a total of 890 daily stock prices). The sample contains 67 assets, and the model is used to build portfolios without limit in the amount of assets and portfolios limited to 40, 30, 20, 10 and 05 assets; the ranges of time to rebalance the portfolios are 20, 40, and 60 trading days, which means to rebalance monthly, bimonthly and quarterly. The results show that, considering this sample, it is not possible to build portfolios with 20 stocks (or less than 20) through direct optimization using the solver Cplex with computational processing time less than 1 hour and results with gap below 5%. On the other hand, using the Genetic Algorithms heuristic approach, portfolios limited to 10 and 05 stocks are built with computational time close to 5 minutes; for both types of portfolio, the solutions provided by the GA have average gap below 10%. Also, with a computational time slightly bigger, close to 8 minutes, the algorithm provides solutions with average gap below 5% for portfolios limited to 10 and 05 stocks.

Orientadores: Pedro Jussieu de Rezende, Cid Carvalho de Souza / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-26T19:09:30Z (GMT). No. of bitstreams: 1 Zambon_MauricioJosedeOliveira_M.pdf: 2774684 bytes, checksum: 1d0ed1f5c1ae01b7646e4bffea6a3736 (MD5) Previous issue date: 2014 / Resumo: Nesta dissertação, apresentamos a primeira abordagem algorítmica e os primeiros resultados experimentais da literatura para tratamento do Problema Cromático Discreto da Galeria de Arte (DCAGP). Trata-se de um problema de natureza geométrica que consiste de uma variante do clássico Problema da Galeria de Arte. Neste, deseja-se encontrar um conjunto de guardas com cardinalidade mínima que consiga vigiar toda uma dada galeria. Já no DCAGP temos por objetivo obter um conjunto de observadores que cubra a galeria e que admita uma coloração válida com o menor número de cores. Uma coloração é válida se dois observadores que veem um mesmo ponto recebem cores distintas. Abordamos a resolução deste problema através de duas abordagens: uma exata e uma heurística. Inicialmente, apresentamos uma heurística primal que fornece limitantes superiores de boa qualidade e, em seguida, um modelo de programação linear inteira para resolução exata do DCAGP. Este método foi capaz de resolver todas as instâncias de um extenso conjunto de galerias, representadas por polígonos simples aleatoriamente gerados, de até 2500 vértices, em menos de um minuto. Já num outro conjunto de instâncias onde a representação inclui polígonos com buracos e polígonos fractais de von Koch com até 800 vértices, o método encontrou soluções comprovadamente ótimas para 80% das instâncias em menos de 30 minutos. No contexto dessas soluções, discutimos o uso de lazy-constraints e de técnicas de fortalecimento do modelo, assim como uma breve análise da dificuldade das instâncias. Reportamos ainda resultados da utilização de relaxação Lagrangiana, para obtenção de bons limitantes, principalmente superiores, e também resultados obtidos por meio de uma variação da técnica relax-and-fix. Finalmente, discutimos um processo de branch-and-price para resolução exata do DCAGP / Abstract: In this dissertation, we present the first algorithmic approach and the first experimental results in the literature for solving the Discrete Chromatic Art Gallery Problem (DCAGP). This problem is geometric in nature and consists of a variation of the classic Art Gallery Problem. In the latter, we want to find a minimum cardinality guard set that is able to watch over a given gallery. On the other hand, in the DCAGP, the objective is to find a set of watchers that covers the gallery and admits a valid coloring with a minimum number of colors. A coloring is valid if two watchers that observe a same point are assigned different colors. To solve this problem we apply two approaches: an exact and a heuristic one. Firstly, we present a primal heuristic able to provide good quality upper bounds, and subsequently an integer programming model that yields exact solutions for the DCAGP. This method was able to solve all instances from an extensive set of galleries, represented by randomly generated simple polygons, of up to 2500 vertices, in less than one minute. On another set of instances, where the representation includes polygons with holes and fractal von Koch polygons, with up to 800 vertices, this method found proven optimal solutions for 80% of the instances in less than 30 minutes. In the context of these solutions, we discuss the use of lazy constraints and techniques for strengthening the model, besides a brief analysis of the hardness of the instances. Moreover, we report on results obtained through a Lagrangian relaxation, mainly as a means to obtain good upper bounds, as well as from a variation of the relax-and-fix technique. Lastly, we discuss a branch-and-price process for solving the DCAGP to exactness / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

abstract: In this work, the stacked values of battery energy storage systems (BESSs) of various power and energy capacities are evaluated as they provide multiple services such as peak shaving, frequency regulation, and reserve support in an ‘Arizona-based test system’ - a simplified, representative model of Salt River Project’s (SRP) system developed using the resource stack information shared by SRP. This has been achieved by developing a mixed-integer linear programming (MILP) based optimization model that captures the operation of BESS in the Arizona-based test system. The model formulation does not include any BESS cost as the objective is to estimate the net savings in total system operation cost after a BESS is deployed in the system. The optimization model has been formulated in such a way that the savings due to the provision of a single service, either peak shaving or frequency regulation or spinning reserve support, by the BESS, can be determined independently. The model also allows calculation of combined savings due to all the services rendered by the BESS. The results of this research suggest that the savings obtained with a BESS providing multiple services are significantly higher than the same capacity BESS delivering a single service in isolation. It is also observed that the marginal contribution of BESS reduces with increasing BESS energy capacity, a result consistent with the law of diminishing returns. Further, small changes in the simulation environment, such as factoring in generator forced outage rates or projection of future solar penetration, can lead to changes as high as 10% in the calculated stacked value. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

This thesis introduces a Large Neighborhood Search (LNS) method to solve a multi-processor avionics scheduling problem. In a typical scheduling problem, tasks are scheduled with exact starting times. In this thesis however, tasks will instead be assigned to disjoint time segments, called buckets. For an assignment to be feasible, precedence relations and capacity constraints related to network and computing resources need to be fulfilled. The introduced LNS method relies on solving Mixed-Integer Programming (MIP)-models. To make progress in the search for a feasible assignment, we construct a MIP-model that allows violation of the problem constraints at a cost of increased objective value. The LNS method uses two operators, a destroy operator that chooses a set of tasks that are allowed to change buckets, and a repair operator that through solving the MIP-model creates a new schedule. This thesis develops 11 types of destroy operators and 30 (concrete) variants of them. The MIP-based LNS is evaluated on a set of 60 instances with up to 84 000 tasks and 21 processors. The instances belongs to six categories of varying difficulty. The MIP-based LNS solves 50 instances within our time limit, and the largest instance solved has 77 757 tasks. This is significantly better than solving the complete MIP-model in a single step. With this approach only 36 instances can be solved within our time limit and the largest instance solved has 48554 tasks.

Cette thèse est consacrée à une étude approfondie du Rank Pricing Problem (RPP) et de deux généralisations. Le RPP est un problème d'optimisation combinatoire qui vise à fixer le prix des produits d'une entreprise afin de maximiser son profit. Elle concerne les clients à la demande, c'est-à-dire les clients qui sont intéressés par plusieurs produits de l'entreprise, mais qui n'ont l'intention d'en acheter qu'un. Les clients disposent d'un budget fixe et classent les produits qui les intéressent du "meilleur" au "pire". Lorsque l'entreprise fixe les prix, chaque client achètera son produit préféré parmi ceux qu'il peut se permettre. Dans le RPP, nous supposons que les produits sont offerts en quantité illimitée, ce qui convient si l'on considère que l'entreprise a suffisamment de produits pour satisfaire la demande, ou lorsque les produits peuvent être fabriqués rapidement avec un coût négligeable (par exemple, les biens numériques).Cette thèse se compose de quatre chapitres. Le premier est un chapitre d'introduction au problème et aux concepts mathématiques présents dans la thèse, tandis que les trois chapitres suivants se concentrent sur chacun des problèmes étudiés :le RPP et deux généralisations. Ainsi, le troisième chapitre est consacré à l'étude du Rank Pricing Problem with Ties (RPPT). Dans cette extension du problème, nous supposons que les clients peuvent exprimer leur indifférence entre les produits qui les intéressent au moyen de liens dans leur liste de préférences. Enfin, le dernier chapitre de la thèse comprend l'étude du Capacitated Rank Pricing Problem (CRPP) avec envie. Dans cette extension, nous avons supposé des prix de réserve pour les clients qui reflètent ce qu'ils sont prêts à payer pour chaque produit, plutôt qu'un budget unique par consommateur. Cependant, la principale différence est que dans le cas du CRPP, l'entreprise dispose d'un nombre limité de produits et peut ne pas être en mesure de satisfaire la demande de tous les clients. L'objectif de la thèse est d'obtenir des formulations linéaires en nombres entiers mixtes pour les trois problèmes étudiés, et de les comparer sur le plan théorique et/ou computationnel. À cette fin, la méthodologie utilisée est basée sur la proposition de variables de décision et de contraintes appropriées qui modélisent le problème. L'objectif suivant est l'amélioration de ces formulations au moyen d'inégalités valides qui réduisent l’ensemble admissible de la relaxation du problème et permettent d'obtenir une meilleure borne de la relaxation linéaire. Et enfin, un troisième objectif est la proposition d'algorithmes de résolution pour chacun de ces modèles, et leur comparaison ultérieure au moyen d'études computationnelles et de résolution au moyen de logiciels d'optimisation commerciaux. / This doctorate is entirely devoted to an in-depth study of the Rank Pricing Problem (RPP) and two generalizations. The RPP is a combinatorial optimization problem which aims at setting the prices of a series of products of a company to maximize its revenue. This problem is specified by a set of unit-demand customers, that is, customers interested in a subset of the products offered by the company which intend to buy at most one of them. To do so, they count on a fixed budget, and they rank the products of their interest from the “best” to the “worst”. Once the prices are established by the company, they will purchase their highest-ranked product among the ones they can afford. In the RPP, it is assumed an unlimited supply of products, which is consistent with a company having enough copies of a product to satisfy the demand, or with a setting where the products can be produced quickly at negligible cost (e.g. digital goods). This dissertation consists of four chapters. The first chapter introduces the RPP problem and the mathematical concepts present in the work, whereas each of the next three chapters tackles the resolution of each of the problems of study: the RPP and two generalizations. Thus, Chapter 3 is dedicated to the Rank Pricing Problem with Ties (RPPT), an extension of the RPP where we consider that customers can express indifference among products in their preference list. And the last chapter of the thesis is devoted to a generalization of the problem that we have named the Capacitated Rank Pricing Problem (CRPP) with envy. For this generalization, we have considered reservation prices of customers for the different products that reflect their willingness to pay, instead of a single budget per customer. However, the main difference is that, in the CRPP, the company has a limited supply of products and might not be able to satisfy all the customers’ requests. This is a realistic assumption that we can find in many companies.The aim of this thesis is the proposal of mixed-integer linear formulations for the three problems of study, and their theoretical and/or computational comparison. The methodology used is based on the introduction of decision variables and adequate restrictions to model the problems. Another objective consists in strengthening the formulations by means of valid inequalities that reduce the feasible region of the relaxed problem and allow us to obtain better linear relaxation bounds. Finally, a third goal is to derive resolution algorithms for each of these models and compare them computationally, using commercial solvers. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

The development of wireless sensor networks (WSNs) is making it possible to monitor our cities. Due to the small size of the sensor nodes, and their capabilities of transmitting data remotely, they can be deployed at locations that are not easy or impossible to access, such as the pipelines of water distribution networks (WDNs), which plays an important role in protecting environment and securing public health.   The design of WSNs for WDNs faces major challenges. Generally, WSNs are resource-limited because most of the sensor nodes are battery powered. Thus, their resource allocation has to be carefully controlled. The thesis considers two prominent problems that occur when designing WSNs for WDNs: scheduling the sensing of the nodes of static WSNs, and sensor placement for mobile WSNs. These studies are reported in the thesis from three published or submitted papers. In the first paper, the scheduling of sleep/sensing for each sensor node is considered to maximize the whole WSNs lifetime while guaranteeing a monitoring performance constraint. The problem is transformed into an energy balancing problem, and solved by a dynamic programming based algorithm. It is proved that this algorithm finds one of the optimal solutions for the energy balancing problem. In the second paper, the question of how the energy balancing problem approximates the original scheduling problem is addressed. It is shown that even though these two problems are not equivalent, the gap of them is small enough. Thus, the proposed algorithm for the energy balancing problem can find a good approximation solution for the original scheduling problem. The second part of the thesis considers the use of mobile sensor nodes. Here, the limited resource is the number of available such mobile nodes. To maximize the monitoring coverage in terms of population, an optimization problem for determining the releasing locations for the mobile sensor nodes is formulated. An approximate solution algorithm based on submodular maximization is proposed and its performance is investigated. Beside WDNs, WSN applications for smart cities share a common characteristic: the area to monitor usually has a network structure. Therefore, the studies of this thesis can be potentially generalized for several IoT scenarios. / <p>QC 20160419</p>

This thesis presents a case study of mathematical production scheduling optimization applied on Arla Foods AB’s production of dairy products. The scheduling was performed as a possible remedy for problems caused by overcrowded finished goods warehouse. Based on the scheduling, conclusions were made on whether the existing two-shift production is sufficient or if an additional night shift should be introduced. In parallel, an empirical and theoretical analysis on the perceived effects of night shift work on employees was conducted. For the optimization, mixed integer programming was used to model the production context through a discrete time scheduling lot-sizing model developed in this thesis. The model developed and implemented on Arla Foods AB contributes to the research field through its feature of relatively low complexity enabling scheduling of extensive production systems when applied in industrial contexts where products may be categorized. The thesis concludes that mathematical production scheduling can solve Arla Foods AB’s production problematics and suggests reallocation of the existing shifts for the purpose of reduced costs and acceptable warehouse levels. This reallocation would incur production during inconvenient hours whereas management remedies reducing negative effects of night shift work are identified. / Denna avhandling innefattar en studie av matematisk optimering av produktionsscheman applicerad på Arla Foods ABs produktion av mejeriprodukter. Schemaläggningen utfördes som en möjlig lösning på produktionsproblematik orsakad av överfyllda färdigvarulager. Utifrån de optimerade produktionsschemana drogs slutsatser kring om dagens produktionsstruktur på två skift är tillräcklig eller om introduktion av ett andra nattskift skulle vara fördelaktig. Parallellt med detta presenteras en empirisk och teoretisk studie kring de produktionsanställdas uppfattning kring effekter av att arbeta nattskift. För optimeringen har heltalsoptimering (eng: mixed integer programming) använts för modellering av produktionen genom en produktionsplaneringsmodell med diskret tidsrepresentation (eng: discrete time scheduling lot-sizing model ) som utvecklas i denna avhandling. Denna model, som även appliceras på Arla Foods ABs produktion, presenteras i detalj och karaktäriseras av låg komplexitet vilket möjliggör schemaoptimering av omfattande produktionssystem givet att produktportföljen kan kategoriseras i produktgrupper med liknande egenskaper ur ett produktionsperspektiv. Avhandlingen fastslår att matematisk optimering av produktionsscheman har potential att lösa produktionsproblematiken på Arla Foods AB och föreslår en reallokering av den nuvarande produktionen för minskade kostnader och utjämnade nivåer i färdigvarulager. Produktionsomläggningen skulle innebära produktion under obekväm arbetstid vilket föranleder en analys av initiativ som har potential att minska de negativa effekterna av nattskiftarbete för de produktionsanställda.

In this dissertation, we show two significant applications of combinatorial branch-and-bound as an exact solution methodology in combinatorial optimization problems. In the first problem, we propose a set of new group centrality metrics and show their performance in estimating protein importance in protein-protein interaction networks. The centrality metrics introduced here are extensions of well-known nodal metrics (degree, betweenness, and closeness) for a set of nodes which is required to induce a specific pattern. The structures investigated range from the ``stricter'' induced stars and cliques, to a ``looser'' definition of a representative structure. We derive the computational complexity for each of the newly proposed metrics. Then, we provide mixed integer programming formulations to solve the problems exactly; due to the computational complexity of the problem and the sheer size of protein-protein interaction networks, using a commercial solver with the formulations is not always a viable option. Hence, we also propose a combinatorial branch-and-bound approach to solve the problems introduced. Finally, we conclude this work with a presentation of the performance of the proposed centrality metrics in identifying essential proteins in helicobacter pylori. In the second problem, we introduce the asymmetric probabilistic minimum-cost Hamiltonian cycle problem (APMCHCP) where arcs and vertices in the graph are possible to fail. APMCHCP has applications in many emerging areas, such as post-disaster recovery, electronic circuit design, and security maintenance of wireless sensor networks. For each vertex, we define a chance-constraint to guarantee that the probability of arriving at the vertex must be greater than or equal to a given threshold. Four mixed-integer programming (MIP) formulations are proposed for modeling the problem, including two direct formulations and two recursive formulations. A combinatorial branch-and-bound (CBB) algorithm is proposed for solving the APMCHCP, where data preprocessing steps, feasibility rules, and approaches of finding upper and lower bounds are developed. In the numerical experiments, the CBB algorithm is compared with formulations on a test-bed of two popular benchmark instance sets. The results show that the proposed CBB algorithm significantly outperforms Gurobi solver in terms of both the size of optimally solved instances and the computing time.