Uma das aplicações mais importantes da análise de sistemas no planejamento de recursos hídricos diz respeito à determinação de estratégias operacionais de sistemas de múltiplos reservatórios, elementos indispensáveis aos aproveitamentos hídricos, cuja operação é alvo de análises que podem envolver muitas restrições e variáveis de decisão. Fica evidenciada, portanto, a necessidade de a operação destes ser otimizada, propiciando assim, o seu melhor aproveitamento, com o menor custo para a sociedade. A presente pesquisa estuda a operação otimizada de um sistema de reservatórios destinado a geração de energia elétrica, usando um modelo híbrido composto de algoritmos genéticos e o SIMPLEX de Nelder e Mead acoplado à programação linear sucessiva. Em conformidade com a recente proposta de Reis et al. (2005), o problema de otimização é resolvido através da decomposição em subproblemas seqüenciais independentes relativos a cada estágio de operação, conectados entre si por supor que os volumes dos reservatórios no final de cada estágio correspondam ao estado do sistema no início do estágio subseqüente. Para estimular a utilização mais eficiente dos volumes armazenados, no suprimento das demandas hídricas dos estágios futuros, são aplicados fatores de redução de custo (FRCs) sobre os volumes armazenados remanescentes no final de cada estágio / One of the most important uses for system analysis in water resources planning is the determination of the operational strategy for multiple reservoir systems, fundamental to better water supply, whose operation is the purpose of analysis that may involve many operation constraints and decision variables. Stay clear, so, the need of optimize their operation, creating in this manner, its best utilization with the less cost to society. This research on the optimal operation of a reservoir system has hydropower generation as its main objective. The optimization framework employs a hybrid model which corresponds to a combination of genetic algorithms and SIMPLEX of Nelder e Mead before employing successive linear programming. Accordant to recent Reis et. al (2005) proposal, the problem of optimizing is solved through decomposition in independents sequential sub problems related to each stage of operation, connected among themselves by supposing that reservoir storage at the end of each stage corresponds to the system state at the beginning of the subsequent stage. To promote the most efficient use of storage for water supply in future stages, FRC (cost reduction factors) are applied on the storage left at the end of each stage

O objetivo desta dissertação é analisar a viabilidade do transporte marítimo de automóveis novos por cabotagem no Brasil, buscando uma alternativa de transporte, pois atualmente este transporte somente é realizado pelo modal rodoviário. A utilização da cabotagem como transporte de cargas tem aumentado desde a promulgação da Lei de Modernização dos Portos (lei 8.630), em 1993. Nesta mesma época, as fábricas de veículos começaram a passar por um processo de desconcentração geográfica, se deslocando da região Sudeste, e necessitando de transportes mais eficientes. Para observar se tal alternativa é viável economicamente, foi elaborado um estudo de caso e foram usados os dados de uma montadora específica. Esta montadora possui fábricas nos municípios de Camaçari (BA) e de São Bernardo do Campo (SP) e precisa distribuir sua produção em 275 concessionárias espalhadas pelo país. A partir do levantamento e análise dos custos rodoviários e de cabotagem para o transporte de automóveis, foi desenvolvido um modelo de transporte solucionado com o uso de técnicas de programação linear cujas soluções a partir da minimização dos custos de transporte apontaram para o uso ou não da cabotagem. As rotas que apresentaram maiores ganhos econômicos têm como característica uma grande distância entre a fábrica e as concessionárias e uma proximidade dos portos de destino das concessionárias. / This study aims to examine the feasibility of costal shipping of new cars by cabotage in Brazil, searching for an alternative system, since, currently, this transport is performed solely by road transportation. The use of cabotage as cargo transportation has increased since the enactment of the Modernization of Ports Law (Act 8630) in 1993. Since then, automobile makers started to undergo a decentralization process, moving away from the Southeast, and, therefore, requiring more efficient transport. To evaluate the economical feasibility of this alternative, a case study was established and data were collected from a specific auto maker. The auto maker has plants in the cities of Camaçari (BA) and São Bernardo do Campo (SP) and needs to distribute its production to 275 authorized dealers across the country. From the study and analysis regarding road and cabotage costs for car transports, it was developed a transportation model whose solutions using linear programming techniques aim to mitigate transportation costs pointing to the use or not of cabotage. Routes that had greater economic yields are characterized by a large distance between the plant and dealers and proximity to destination ports of dealers.

Corn production in the United States has become increasingly efficient over the years. The use of nitrogen fertilizers has played a substantial role in this efficiency. Nitrogen drives biomass production which leads to increased yields. Unlike other nutrients, nitrogen is more mobile making it easier to lose through leaching and volatilization. The first part of this analysis uses an econometric model to examine the relationship between nitrogen usage and weather data. This relationship leads to farm management decisions to reduce nitrogen fertilization expenses. In addition to the use of nitrogen fertilizers, farmers in Kentucky take advantage of an abundance of poultry litter as a fertilizer source. Traditional poultry litter fertilization methods are being challenged by new technology, sub-surface injection, which has the potential to increase corn yields as compared to other methods. The second part of this analysis uses a resource allocation linear programming model to determine the economic viability of the sub-surface injection method for both spring and fall fertilizer applications. This model also reveals both farm management implications and provides valuable information for the development and commercialization of the sub-surface injector.

<p>Energy management has become a major concern in the past two decades with the increasing energy prices, overutilization of natural resources and increased carbon emissions. According to the department of Energy the industrial sector solely consumes 22.4% of the energy produced in the country [1]. This calls for an urgent need for the industries to design and implement energy efficient practices by analyzing the energy consumption, electricity data and making use of energy efficient equipment. Although, utility companies are providing incentives to consumer participating in Demand Response programs, there isn’t an active implementation of energy management principles from the consumer’s side. Technological advancements in controls, automation, optimization and big data can be harnessed to achieve this which in other words is referred to as “Smart Manufacturing”. In this research energy management techniques have been designed for two SEU (Significant Energy Use) equipment HVAC systems, Compressors and load shifting in manufacturing environments using control and optimization.</p> <p>The addressed energy management techniques associated with each of the SEUs are very generic in nature which make them applicable for most of the industries. Firstly, the loads or the energy consuming equipment has been categorized into flexible and non-flexible loads based on their priority level and flexibility in running schedule. For the flexible loads, an optimal load scheduler has been modelled using Mixed Integer Linear Programming (MILP) method that find carries out load shifting by using the predicted demand of the rest of the plant and scheduling the loads during the low demand periods. The cases of interruptible loads and non-interruptible have been solved to demonstrate load shifting. This essentially resulted in lowering the peak demand and hence cost savings for both “Time-of-Use” and Demand based price schemes. </p> <p>The compressor load sharing problem was next considered for optimal distribution of loads among VFD equipped compressors running in parallel to meet the demand. The model is based on MILP problem and case studies was carried out for heavy duty (>10HP) and light duty compressors (<=10HP). Using the compressor scheduler, there was about 16% energy and cost saving for the light duty compressors and 14.6% for the heavy duty compressors</p> <p>HVAC systems being one of the major energy consumer in manufacturing industries was modelled using the generic lumped parameter method. An Electroplating facility named Electro-Spec was modelled in Simulink and was validated using the real data that was collected from the facility. The Mean Absolute Error (MAE) was about 0.39 for the model which is suitable for implementing controllers for the purpose of energy management. MATLAB and Simulink were used to design and implement the state-of-the-art Model Predictive Control for the purpose of energy efficient control. The MPC was chosen due to its ability to easily handle Multi Input Multi Output Systems, system constraints and its optimal nature. The MPC resulted in a temperature response with a rise time of 10 minutes and a steady state error of less than 0.001. Also from the input response, it was observed that the MPC provided just enough input for the temperature to stay at the set point and as a result led to about 27.6% energy and cost savings. Thus this research has a potential of energy and cost savings and can be readily applied to most of the manufacturing industries that use HVAC, Compressors and machines as their primary energy consumer.</p><br>

Durant les dernières décennies, des réseaux de logistiques inverses ont été lancésdans plusieurs pays industrialisés dans l’objectif de préserver l’environnement. La mise enplace et la gestion de cette logistique concernent les différents niveaux, stratégique, tactique etopérationnel existants dans le cadre de la logistique directe pour tout système de productionde biens industriels. Pour améliorer ce type de réseau, la modélisation et la simulation sontdes outils efficaces. Après avoir présenté un état de l’art de ce domaine, notre étude sefocalise sur la planification de certains sous-ensembles (appelés maillons) de cette chaineinverse (i.e. collecte-tri, désassemblage) au niveau tactique, c'est-à-dire à moyen terme. Lebut de ce travail est donc de proposer un modèle générique en programmation linéaire dans uncontexte multi-produit et multi-période, qui cherche à maximiser le profit total du maillonétudié et qui prend en compte l’incertitude sur la qualité des produits traités. Le modèlelinéaire en nombres entiers (déterministe, stochastique et évaluation stochastique) est ainsiformulé autour d’un profit contraint par les capacités du maillon, l’évolution des stocksentrants et sortants et la livraison des produits traités aux clients, avec une politique delivraison sans déclassement ou avec déclassement des produits. / During the last decades, reverse logistics networks have been launched in severalindustrialized countries with the aim of preserving the environment. The implementation andmanagement of the reverse logistics concerns the different levels (strategic, tactical andoperational) existing in the framework of forward logistics for any production system. Toimprove this type of network, modeling and simulation are effective tools. After presenting astate of the art in this domain, our study focuses on the planning of two sub-systems of thereverse logistics chain (i.e. collection-sorting and disassembly) on the tactical level that is tosay the medium term. The aim of this work is to propose a generic model by linearprogramming in a multi-product and multi-period context, which searches for maximizingthe total profit of the sub-system studied, taking into account the uncertainty of the productssupplied. The integer linear model (deterministic, stochastic and stochastic assessment) is thusformulated around a profit constrained by the sub-system capacity, the evolution of incomingand outgoing inventory and the delivery of products to customers: a delivery policyauthorizing the quality degrading of products or not.

<p>In this thesis we address the problem of integrated software pipelining for clustered VLIW architectures. The phases that are integrated and solved as one combined problem are: cluster assignment, instruction selection, scheduling, register allocation and spilling.</p><p>As a first step we describe two methods for integrated code generation of basic blocks. The first method is optimal and based on integer linear programming. The second method is a heuristic based on genetic algorithms.</p><p>We then extend the integer linear programming model to modulo scheduling. To the best of our knowledge this is the first time anybody has optimally solved the modulo scheduling problem for clustered architectures with instruction selection and cluster assignment integrated.</p><p>We also show that optimal spilling is closely related to optimal register allocation when the register files are clustered. In fact, optimal spilling is as simple as adding an additional virtual register file representing the memory and have transfer instructions to and from this register file corresponding to stores and loads.</p><p>Our algorithm for modulo scheduling iteratively considers schedules with increasing number of schedule slots. A problem with such an iterative method is that if the initiation interval is not equal to the lower bound there is no way to determine whether the found solution is optimal or not. We have proven that for a class of architectures that we call transfer free, we can set an upper bound on the schedule length. I.e., we can prove when a found modulo schedule with initiation interval larger than the lower bound is optimal.</p><p>Experiments have been conducted to show the usefulness and limitations of our optimal methods. For the basic block case we compare the optimal method to the heuristic based on genetic algorithms.<em></em></p><p><em>This work has been supported by The Swedish national graduate school in computer science (CUGS) and Vetenskapsrådet (VR).</em></p>

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 250-274).

This thesis consists of four independent papers concerningdifferent aspects of interior methods for optimization. Threeof the papers focus on theoretical aspects while the fourth oneconcerns some computational experiments. The systems of equations solved within an interior methodapplied to a convex quadratic program can be viewed as weightedlinear least-squares problems. In the first paper, it is shownthat the sequence of solutions to such problems is uniformlybounded. Further, boundedness of the solution to weightedlinear least-squares problems for more general classes ofweight matrices than the one in the convex quadraticprogramming application are obtained as a byproduct. In many linesearch interior methods for nonconvex nonlinearprogramming, the iterates can "falsely" converge to theboundary of the region defined by the inequality constraints insuch a way that the search directions do not converge to zero,but the step lengths do. In the sec ond paper, it is shown thatthe multiplier search directions then diverge. Furthermore, thedirection of divergence is characterized in terms of thegradients of the equality constraints along with theasymptotically active inequality constraints. The third paper gives a modification of the analytic centerproblem for the set of optimal solutions in linear semidefiniteprogramming. Unlike the normal analytic center problem, thesolution of the modified problem is the limit point of thecentral path, without any strict complementarity assumption.For the strict complementarity case, the modified problem isshown to coincide with the normal analytic center problem,which is known to give a correct characterization of the limitpoint of the central path in that case. The final paper describes of some computational experimentsconcerning possibilities of reusing previous information whensolving system of equations arising in interior methods forlinear programming. <b>Keywords:</b>Interior method, primal-dual interior method,linear programming, quadratic programming, nonlinearprogramming, semidefinite programming, weighted least-squaresproblems, central path. <b>Mathematics Subject Classification (2000):</b>Primary90C51, 90C22, 65F20, 90C26, 90C05; Secondary 65K05, 90C20,90C25, 90C30.

Clustering is the process in which data is differentiated, classified according to some criteria. As a result of partitioning process, data is grouped into clusters for specific purpose. In a social network, clustering of people is one of the most popular problems. Therefore, we mainly concentrated on finding an efficient algorithm for this problem. In our study, data is made up of two types of entities (e.g., people, groups vs. political issues, religious beliefs) and distinct from most previous works, signed weighted bipartite graphs are used to model relations among them. For the partitioning criterion, we use the strength of the opinions between the entities. Our main intention is to partition the data into k-clusters so that entities within clusters represent strong relationship. One such example from a political domain is the opinion of people on issues. Using the signed weights on the edges, these bipartite graphs can be partitioned into two or more clusters. In political domain, a cluster represents strong relationship among a group of people and a group of issues. After partitioning, each cluster in the result set contains like-minded people and advocated issues. Our work introduces a general mechanism for k-way partitioning of signed bipartite graphs. One of the great advantages of our thesis is that it does not require any preliminary information about the structure of the input dataset. The idea has been illustrated on real and randomly generated data and promising results have been shown.

The unifying theme of this dissertation is the development of new solutions for decoding and inference problems based on convex optimization methods. Th first part considers the joint detection and decoding problem for low-density parity-check (LDPC) codes on finite-state channels (FSCs). Hard-disk drives (or magnetic recording systems), where the required error rate (after decoding) is too low to be verifiable by simulation, are most important applications of this research. Recently, LDPC codes have attracted a lot of attention in the magnetic storage industry and some hard-disk drives have started using iterative decoding. Despite progress in the area of reduced-complexity detection and decoding algorithms, there has been some resistance to the deployment of turbo-equalization (TE) structures (with iterative detectors/decoders) in magnetic-recording systems because of error floors and the difficulty of accurately predicting performance at very low error rates. To address this problem for channels with memory, such as FSCs, we propose a new decoding algorithms based on a well-defined convex optimization problem. In particular, it is based on the linear-programing (LP) formulation of the joint decoding problem for LDPC codes over FSCs. It exhibits two favorable properties: provable convergence and predictable error-floors (via pseudo-codeword analysis). Since general-purpose LP solvers are too complex to make the joint LP decoder feasible for practical purposes, we develop an efficient iterative solver for the joint LP decoder by taking advantage of its dual-domain structure. The main advantage of this approach is that it combines the predictability and superior performance of joint LP decoding with the computational complexity of TE. The second part of this dissertation considers the matrix completion problem for the recovery of a data matrix from incomplete, or even corrupted entries of an unknown matrix. Recommender systems are good representatives of this problem, and this research is important for the design of information retrieval systems which require very high scalability. We show that our IMP algorithm reduces the well-known cold-start problem associated with collaborative filtering systems in practice.