Ferramentas de projeto de circuitos integrados (do inglˆes, electronic design automation, ou simplesmente EDA) tˆem um papel fundamental na crescente complexidade dos projetos de circuitos digitais. Elas permitem aos projetistas criar circuitos com um n´umero de componentes ordens de grandezas maior do que seria poss´ıvel se os circuitos fossem projetados `a m˜ao como nos dias iniciais da microeletrˆonica. Neste trabalho, dois importantes problemas em EDA ser˜ao abordados: dimensionamento de portas e posicionamento detalhado dirigido a desempenho. Para dimensionamento de portas, uma nova metodologia de relaxac¸ ˜ao Lagrangiana ´e apresentada baseada em informac¸ ˜ao de temporarizac¸ ˜ao locais e propagac¸ ˜ao de sensitividades. Para posicionamento detalhado dirigido a desempenho, um conjunto de movimentos de c´elulas ´e criado usando uma formac¸ ˜ao ´otima atenta `a forc¸a de alimentac¸ ˜ao para o balanceamento de cargas. Nossos resultados experimentais mostram que tais t´ecnicas s˜ao capazes de melhorar o atual estado-da-arte. / Electronic design automation (EDA) tools play a fundamental role in the increasingly complexity of digital circuit designs. They empower designers to create circuits with several order of magnitude more components than it would be possible by designing circuits by hand as was done in the early days of microelectronics. In this work, two important EDA problems are addressed: gate sizing and timing-driven detailed placement. They are studied and new techniques developed. For gate sizing, a new Lagrangian-relaxation methodology is presented based on local timing information and sensitivity propagation. For timing-driven detailed placement, a set of cell movement methods are created using drive strength-aware optimal formulation to driver/sink load balancing. Our experimental results shows that those techniques are able to improve the current state-of-the-art.

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For larger instances of multi-objective optimisation problems, the exact Pareto frontier can be both difficult and time-consuming to calculate. There is a wide range of methods to find feasible solutions to such problems, but techniques for finding good optimistic bounds to compare the feasible solutions with are missing. In this study, we investigate the use of Lagrangian relaxation to create optimistic bounds to bi-objective optimisation problems with complicating side constraints. The aim is to develop an effective method to produce optimistic bounds that are closer to the Pareto frontier than the commonly used linear programming bounds.  In order to use Lagrangian relaxation on the bi-objective problem, the objectives are combined using the weighted sum method. A Lagrangian dual function is then constructed by relaxing the complicating constraints and the subgradient method is used to optimise the dual problem in order to find an optimistic solution. By solving the single-objective problem for multiple weights, an optimistic bound to the Pareto frontier can be constructed. The subgradient method also includes a heuristic to find feasible solutions. The feasible solutions found by the heuristic form a pessimistic bound to the frontier. The method has been implemented and tested on several instances of a capacitated facility location problem with cost and CO2 emission as objectives. The results indicate that, by using Lagrangian relaxation, an optimistic bound close to the Pareto frontier can be found in a relatively short time. The heuristic used also manages to produce good pessimistic bounds, and hence the Pareto frontier can be tightly enclosed. The optimistic bounds found by Lagrangian relaxation are better and more consistent along the Pareto frontier than the bounds found by linear programming.

The rapid technological advancement in the past century resulted in a decreased life cycle of a large number of products and, consequently, increased the rate of technological obsolescence. The disposal of obsolete products has resulted in rapid landfilling and now poses a major environmental threat. The governments in many countries around the world have started imposing regulations to curb uncontrolled product disposal. The consumers, too, are now aware of adverse effects of product disposal on environment and increasingly favor environmentally benign products. In the wake of imminent stringent government regulations and the consumer awareness about ecosystem-friendly products, the manufacturers need to think about the alternatives to product disposal. One way to deal with this problem is to disassemble an obsolete product and utilize some of its components/subassemblies in the manufacturing of new products. This seems to be a promising solution because products now-a-days are made in accordance with the highest quality standards and, although an obsolete product may not be in the required functional state as a whole, it is possible that several of its components or subassemblies are still in near perfect condition. However, product disassembly is a complex task requiring human labor as well as automated processes and, consequently, a huge amount of monetary investment. This research addresses a disassembly optimization problem, which aims at minimizing the costs associated with the disassembly process (namely, the costs of breaking the joints and the sequence dependent set-up cost associated with disassembly operations), while maximizing the benefits resulting from recovery of components/subassemblies from a product. We provide a mathematical abstraction of the disassembly optimization problem in the form of integer-programming models. One of our formulations includes a new way of modeling the subtour elimination constraints (SECs), which are usually encountered in the well-known traveling salesman problems. Based on these SECs, a new valid formulation for asymmetric traveling salesman problem (ATSP) was developed. The ATSP formulation was further extended to obtain a valid formulation for the precedence constrained ATSP. A detailed experimentation was conducted to compare the performance of the proposed formulations with that of other well-known formulations discussed in the literature. Our results indicate that in comparison to other well-known formulations, the proposed formulations are quite promising in terms of the LP relaxation bounds obtained and the number of branch and bound nodes explored to reach an optimal integer solution. These new formulations along with the results of experimentation are presented in Appendix A. To solve the disassembly optimization problem, a three-phase iterative solution procedure was developed that can determine optimal or near optimal disassembly plans for complex assemblies. The first phase helps in obtaining an upper bound on our maximization problem through an application of a Lagrangian relaxation scheme. The second phase helps to further improve this bound through addition of a few valid inequalities in our models. In the third phase, we fix some of our decision variables based on the solutions obtained in the iterations of phases 1 and 2 and then implement a branch and bound scheme to obtain the final solution. We test our procedure on several randomly generated data sets and identify the factors that render a problem to be computationally difficult. Also, we establish the practical usefulness of our approach through case studies on the disassembly of a computer processor and a laser printer. / Master of Science

The semiconductor industry provides a host of very challenging problems in production planning and scheduling because of the unique features of the wafer fab. This research addresses the need to develop an approach, which can be used to generate optimal or near-optimal solutions to the scheduling problem of a wafer fab, by using Mathematical Programming for a general case of a wafer fab. The problem is approached in two steps. First, the number of lots of different products to be released into the system during each planning period is determined, such that the total tardiness of the product orders is minimized over the planning horizon. Second, the schedule of these lots is determined so that the cycle time of each lot released into the system is minimized. Thus, the performance measures based both on due dates and cycle time are considered. The lot release, tardiness problem is formulated as an integer linear program, and a 3-phase procedure, which utilizes a variation of the Wilkerson-Irwin algorithm, is developed. The performance of this 3-phase procedure is further improved by using insights from classical scheduling theory. The scheduling problem is formulated as a 0-1 integer linear program. An algorithm is developed for tightening the LP relaxation of this 0-1 integer linear programming model (of the scheduling problem) leading to a better performance of the branch and bound procedure used for its solution. Lagrangian relaxation is applied on a carefully chosen set of constraints in the scheduling problem, and a Lagrangian heuristic is developed for scheduling the jobs in each period of the planning horizon. Several useful insights are developed throughout to further improve the performance of the proposed algorithm. Experiments are conducted for both the tardiness and the scheduling problems. Five experiments are conducted for the tardiness problem. Each experiment has a different combination of number of products, machines, and work orders in a small sized wafer fab (2 to 6 products, 8 to 10 station families, 15 to 30 workstations, 9 to19 work orders, and 100 to 250 lots per work order). The solutions obtained by the 3-phase procedure are compared to the optimal solutions of the corresponding tardiness problems, and the tardiness per work order for the 3-phase procedure is 0% to 25% greater than the optimal solution. But the time required to obtain the optimal solution is 22 to 1074 times greater than the time required to obtain the solution through the 3-phase procedure. Thus, the 3-phase procedure can generate almost optimal solutions and requires much smaller computation time than that required by the optimal solution. Four experiments are conducted to test the performance of the scheduling problem. Each experiment has a different combination of number of products, machines, routes, bottleneck stations, processing times, and product mix entering the system each day in a small sized wafer fab (2 products, 8 station families, 18 workstations, and 8 to 10 lots released per day into the system). The solution quality of the schedule generated by the Lagrangian heuristic is compared to the solution provided by the standard dispatching rules available in practice. In each experiment, the cycle time of a product for each dispatching rule is divided by the best cycle time for that product over all the dispatching rules in that experiment. This ratio for the Lagrangian heuristic in each experiment and over all the experiments varies from 100% to 104%. For the standard dispatching rules, this ratio ranges from 100% to 120% in each experiment and also over all the experiments. The average of the ratio over all the experiments is the least for the Lagrangian heuristic. This indicates that for the experiments conducted, the Lagrangian heuristic consistently provides a solution that is, or is close to, the best solution and, hence, quite competitive when compared to the standard dispatching rules. / Master of Science

Fornecedores necessitam atender a demanda de seus clientes da forma mais adequada possível e mantendo a qualidade de seu serviço, porém em muitos casos essa demanda é desconhecida. Esse problema pode ser modelado como um problema de roteirização e estoque com demanda estocástica o qual inclui o controle de estoque, transporte do produto e decisões de agendamento da entrega. Existem vários trabalhos na literatura para resolver esse problema, porém nenhum deles lida com janela de tempo de atendimento, capacidade máxima de estoque tanto no cliente quanto no depósito e o nível de confiança de atendimento individualizado para cada cliente. O objetivo principal deste trabalho é propor um novo algoritmo baseado em otimização matemática para lidar com esse problema mais realista. Além disso, este trabalho tem como objetivo secundário melhorar o algoritmo de estado da arte baseado em otimização matemática, visando encontrar soluções com um menor tempo computacional e custo. Foram realizados experimentos com instâncias sintéticas com 15 até 50 clientes, as quais são geradas aleatoriamente, e com uma instância real, baseada na experiência profissional no mercado empresarial e em cenários reais de distribuição na cidade de São Paulo / Providers need to supply the demand of their clients as optimally as possible and maintaining the quality of their service, however in many cases this demand is unknown. This problem can be modeled as a inventory routing problem with stochastic demand, which includes inventory control, product transportation and delivery scheduling decisions. There are several papers in the literature to solve this problem, but none of them deals with service time window, maximum stock capacity for both the customer and the depot and individualized confidence level for each costumer. The main objective of this work is to propose a new algorithm based on mathematical optimization to deal with this more realistic problem. In addition, this work has as secondary objective to improve the state of the art algorithm based on mathematical optimization, aiming to find solutions with a lower computational time and cost. Experiments were performed with synthetic instances with 15 to 50 clients, which are randomly generated, and with a real instance, based on professional experience in the business market and in real distribution scenarios in the city of São Paulo

Esta tese aborda um problema de dimensionamento e sequenciamento de lotes de produção baseado em uma indústria alimentícia brasileira que opera por meio de diversas linhas de produção heterogêneas. Nesse ambiente produtivo, as linhas de produção compartilham recursos escassos, tais como, trabalhadores e máquinas e devem ser montadas (ativadas) em cada período produtivo, respeitando-se a capacidade disponível de cada recurso necessário para ativação das mesmas. Modelos de programação matemática inteira mista são propostos para representação do problema, bem como diversos métodos heurísticos de solução, compreendendo procedimentos construtivos e de melhoramento baseados na formulação matemática do problema e heurísticas lagrangianas. São propostas heurísticas do tipo relax-and-fix explorando diversas partições das variáveis binárias dos modelos e uma heurística baseada na decomposição do modelo para construção de soluções. Procedimentos do tipo fix-and-optimize e matheuristics do tipo iterative MIP-based neighbourhood search são propostas para o melhoramento das soluções iniciais obtidas pelos procedimentos construtivos. Testes computacionais são realizados com instâncias geradas aleatoriamente e mostram que os métodos propostos são capazes de oferecer melhores soluções do que o algoritmo Branch-and-Cut de um resolvedor comercial para instâncias de médio e grande porte. / This doctoral dissertation addresses the simultaneous lot sizing and scheduling problem in a real world production environment where production lines share scarce production resources. Due to the lack of resources, the production lines cannot operate all simultaneously and they need to be assembled in each period respecting the capacity constraints of the resources. This dissertation presents mixed integer programming models to deal with the problem as well as various heuristic approaches: constructive and improvement procedures based on the mathematical formulation of the problem and lagrangian heuristics. Relax-and-fix heuristics exploring some partitions of the set of binary variables of a model and a decomposition based heuristic are proposed to construct solutions. Fix-and-optimize heuristics and iterative MIP-based neighbourhood search matheuristics are proposed to improvement solutions obtained by constructive procedures. Computational tests are performed with randomly instances and show that the proposed methods can find better solutions than the Branch-and-Cut algorithm of a commercial solver for medium and large size instances.

Fornecedores necessitam atender a demanda de seus clientes da forma mais adequada possível e mantendo a qualidade de seu serviço, porém em muitos casos essa demanda é desconhecida. Esse problema pode ser modelado como um problema de roteirização e estoque com demanda estocástica o qual inclui o controle de estoque, transporte do produto e decisões de agendamento da entrega. Existem vários trabalhos na literatura para resolver esse problema, porém nenhum deles lida com janela de tempo de atendimento, capacidade máxima de estoque tanto no cliente quanto no depósito e o nível de confiança de atendimento individualizado para cada cliente. O objetivo principal deste trabalho é propor um novo algoritmo baseado em otimização matemática para lidar com esse problema mais realista. Além disso, este trabalho tem como objetivo secundário melhorar o algoritmo de estado da arte baseado em otimização matemática, visando encontrar soluções com um menor tempo computacional e custo. Foram realizados experimentos com instâncias sintéticas com 15 até 50 clientes, as quais são geradas aleatoriamente, e com uma instância real, baseada na experiência profissional no mercado empresarial e em cenários reais de distribuição na cidade de São Paulo / Providers need to supply the demand of their clients as optimally as possible and maintaining the quality of their service, however in many cases this demand is unknown. This problem can be modeled as a inventory routing problem with stochastic demand, which includes inventory control, product transportation and delivery scheduling decisions. There are several papers in the literature to solve this problem, but none of them deals with service time window, maximum stock capacity for both the customer and the depot and individualized confidence level for each costumer. The main objective of this work is to propose a new algorithm based on mathematical optimization to deal with this more realistic problem. In addition, this work has as secondary objective to improve the state of the art algorithm based on mathematical optimization, aiming to find solutions with a lower computational time and cost. Experiments were performed with synthetic instances with 15 to 50 clients, which are randomly generated, and with a real instance, based on professional experience in the business market and in real distribution scenarios in the city of São Paulo

<p>Sweden experiences heavy snowfall during the winter season and cost effective road maintenance is significantly affected by the routing of snowplows. The routing problem becomes more complex as the SwedishNational Road Administration (Vägverket) sets operational requirements such as satisfying a time window for each road segment. </p><p>This thesis focuses on route optimization for snowplows after snowfall; to develop and implement an algorithm for finding combinations of generated routes which minimize the total cost. The results are compared to those stated in the licentiate thesis by Doctoral student Nima Golbaharan (2001). </p><p>The algorithm calculates a lower bound to the problem using a Lagrangian master problem. A common subgradient approach is used to find near-optimal dual variables to be sent to a column-generation program which returns routes for the snowplows. A greedy heuristic chooses a feasible solution, which gives an upper bound to the problem. This entire process is repeated as needed. </p><p>This method for routing snowplows produces favorable results with a relatively small number of routes and are comparable to Golbaharan's results. An interesting observation involves the allocation of vehicles in which certain depots were regularly over- or under-utilized. This suggests that the quantity and/or distribution of available vehicles may not be optimal.</p>

Sweden experiences heavy snowfall during the winter season and cost effective road maintenance is significantly affected by the routing of snowplows. The routing problem becomes more complex as the SwedishNational Road Administration (Vägverket) sets operational requirements such as satisfying a time window for each road segment. This thesis focuses on route optimization for snowplows after snowfall; to develop and implement an algorithm for finding combinations of generated routes which minimize the total cost. The results are compared to those stated in the licentiate thesis by Doctoral student Nima Golbaharan (2001). The algorithm calculates a lower bound to the problem using a Lagrangian master problem. A common subgradient approach is used to find near-optimal dual variables to be sent to a column-generation program which returns routes for the snowplows. A greedy heuristic chooses a feasible solution, which gives an upper bound to the problem. This entire process is repeated as needed. This method for routing snowplows produces favorable results with a relatively small number of routes and are comparable to Golbaharan's results. An interesting observation involves the allocation of vehicles in which certain depots were regularly over- or under-utilized. This suggests that the quantity and/or distribution of available vehicles may not be optimal.