The facility layout problem (FLP) is one of the most important and challenging problems in both the operations research and industrial engineering research domains. In FLP research, the continuous-representation-based FLP can consider all possible all-rectangular department solutions. Given this flexibility, this representation has become the representation of-choice in FLP research. Much of this research is based on a methodology of mixed integer programming (MIP) models. However, these MIP-FLP models can only solve problems with a limited number of departments to optimality due to a large number of binary variables used in the models to prevent departments from overlapping. Our research centers around the sequence-pair representation, a concept that originated in the Very Large Scale Integration (VLSI) design literature. We show that an exhaustive search of the sequence-pair solution space will result in finding the optimal layout of the MIP-FLP and that every sequence-pair solution is binary-feasible in the MIP-FLP. Based on this fact, we propose a methodology that combines the sequence-pair and MIP-FLP model to efficiently solve large continuous-representation-based FLPs. Our heuristic approach searches the sequence-pair solution space and then use the sequence-pair representation to simplify and solve the MIPFLP model. Based on this methodology, we systematically study the different aspects of the FLP throughout this dissertation. As the first contribution of this dissertation, we present a genetic algorithm based heuristic, SEQUENCE, that combines the sequence-pair representation and the most recent MIPFLP model to solve the all-rectangular-department continuous-representation-based FLP. Numerical experiments based on different sized test problems from both the literature and industrial applications are provided and the solutions are compared with both the optimal solutions and the solutions from other heuristics to show the effectiveness and efficiency of our heuristic. For eleven data sets from the literature, we provide solutions better than those previously found. For the FLP with fixed departments, many sequence-pairs become infeasible with respect to the fixed department location and dimension restrictions. As our second contribution, to address this difficulty, we present a repair operator to filter the infeasible sequence-pairs with respect to the fixed departments. This repair operator is integrated into SEQUENCE to solve the FLP with fixed departments more efficiently. The effectiveness of combining SEQUENCE and the repair operator for solving the FLP with fixed departments is illustrated through a series of numerical experiments where the SEQUENCE solutions are compared with other heuristics' solutions. The third contribution of this dissertation is to formulate and solve the FLP with an existing aisle structure (FLPAL). In many industrial layout designs, the existing aisle structure must be taken into account. However, there is very little research that has been conducted in this area. We extend our research to further address the FLPAL. We first present an MIP model for the FLPAL (MIP-FLPAL) and run numerical experiments to test the performance of the MIP-FLPAL. These experiments illustrate that the MIP-FLPAL can only solve very limited sized FLPAL problems. Therefore, we present a genetic algorithm based heuristic, SEQUENCE-AL, to combine the sequence-pair representation and MIP-FLPAL to solve larger-sized FLPAL problems. Different sized data sets are solved by SEQUENCE-AL and the solutions are compared with both the optimal solutions and other heuristics' solutions to show the effectiveness of SEQUENCE-AL. The fourth contribution of this dissertation is to formulate and solve the FLP with non-rectangular-shaped departments. Most FLP research focuses on layout design with all rectangular-shaped departments, while in industry there are many FLP applications with non-rectangular-shaped departments. We extend our research to solve the FLP with nonrectangular-shaped departments. We first formulate the FLP with non-rectangular-shaped departments (FLPNR) to a MIP model (MIP-FLPNR), where each non-rectangular department is partitioned into rectangular-shaped sub-departments and the sub-departments from the same department are connected according to the department's orientation. The effect of different factors on the performance of the MIP-FLPNR is explored through a series of numerical tests, which also shows that MIP-FLPNR can only solve limited-sized FLPNR problems. To solve larger-sized FLPNR problems, we present a genetic algorithm based heuristic, SEQUENCE-NR, along with two repair operators based on the mathematical properties of the MIP-FLPNR to solve the larger-sized FLPNR. A series of numerical tests are conducted on SEQUENCE-NR to compare the SEQUENCE-NR solutions with both the optimal solutions and another heuristic's solutions to illustrate the effectiveness of SEQUENCE-NR. As the first systematic research study on a methodology that combines the sequence-pair representation and the MIP-based FLP, this dissertation addresses different types of continuous-representation based facility layout design problems: from block layout design with and without fixed departments to re-layout design with an existing aisle structure, and from layout design with all-rectangular-shaped departments to layout design with arbitrary non-rectangular-shaped departments. For each type of layout design problem, numerical experiments are conducted to illustrate the effectiveness of our specifically designed family of sequence-pair and MIP-based heuristics. As a result, better solutions than those previously found are provided for some widely used data sets from the literature and some new datasets based on both the literature and industrial applications are proposed for the first time. Furthermore, future research that continues to combine the sequence-pair representation and the MIP-FLP model to solve the FLP is also discussed, indicating the richness of this research domain. / Ph. D.

Stochastic programming is an optimization technique that incorporates random variables as parameters. Because it better reflects the uncertain real world than its traditional deterministic counterpart, stochastic programming has drawn increasingly more attention among decision-makers, and its applications span many fields including financial engineering, health care, communication systems, and supply chain management. On the flip side, stochastic programs are usually very difficult to solve, which is further compounded by the fact that in many of the aforementioned applications, we also have discrete decisions, thereby rendering these problems even more challenging. In this dissertation, we study the class of two-stage stochastic mixed-integer programs (SMIP), which, as its name suggests, lies at the confluence of two formidable classes of problems. We design a novel algorithm for this class of problems, and also explore specialized approaches for two related real-world applications. Although a number of algorithms have been developed to solve two-stage SMIPs, most of them deal with problems containing purely integer or continuous variables in either or both of the two stages, and frequently require the technology and/or recourse matrices to be deterministic. As a ground-breaking effort, in this work, we address the challenging class of two-stage SMIPs that involve 0-1 mixed-integer variables in both stages. The only earlier work on solving such problems (Carøe and Schultz (1999)) requires the optimization of several non-smooth Lagrangian dual problems using subgradient methods in the bounding process, which turns out to be computationally very expensive. We begin with proposing a decomposition-based branch-and-bound (DBAB) algorithm for solving two-stage stochastic programs having 0-1 mixed-integer variables in both stages. Since the second-stage problems contain binary variables, their value functions are in general nonconvex and discontinuous; hence, the classical Benders' decomposition approach (or the L-shaped method) for solving two-stage stochastic programs, which requires convex subproblem value functions, cannot be directly applied. This motivates us to relax the second-stage problems and accompany this relaxation with a convexification process. To make this process computationally efficient, we propose to construct a certain partial convex hull representation of the two-stage solution space, using the relaxed second-stage constraints and the restrictions confining the first-stage variables to lie within some hyperrectangle. This partial convex hull is sequentially generated using a convexification scheme, such as the Reformulation-Linearization Technique (RLT), which yields valid inequalities that are functions of the first-stage variables and, of noteworthy importance, are reusable in the subsequent subproblems by updating the values of the first-stage variables. Meanwhile, since the first stage contains continuous variables, whenever we tentatively fix these variables at some given feasible values, the resulting constraints may not be facial with respect to the associated bounding constraints that are used to construct the partial convex hull. As a result, the constructed Benders' subproblems define lower bounds for the second-stage value functions, and likewise, the resulting Benders' master problem provides a lower bound for the original stochastic program defined over the same hyperrectangle. Another difficulty resulting from continuous first-stage variables is that when the given first-stage solution is not extremal with respect to its bounds, the second-stage solution obtained for a Benders' subproblem defined with respect to a partial convex hull representation in the two-stage space may not satisfy the model's binary restrictions. We thus need to be able to detect whether or not a Benders' subproblem is solved by a given fractional second-stage solution. We design a novel procedure to check this situation in the overall algorithmic scheme. A key property established, which ensures global convergence, is that these lower bounds become exact if the given first-stage solution is a vertex of the defining hyperrectangle, or if the second-stage solution satisfies the binary restrictions. Based on these algorithmic constructs, we design a branch-and-bound procedure where the branching process performs a hyperrectangular partitioning of the projected space of the first-stage variables, and lower bounds for the nodal problems are computed by applying the proposed modified Benders' decomposition method. We prove that, when using the least-lower-bound node-selection rule, this algorithm converges to a global optimal solution. We also show that the derived RLT cuts are not only reusable in subsequent Benders iterations at the same node, but are also inheritable by the subproblems of the children nodes. Likewise, the Benders' cuts derived for a given sub-hyperrectangle can also be inherited by the lower bounding master programs solved for its children nodes. Using these cut inheritance properties results in significant savings in the overall computational effort. Some numerical examples and computational results are presented to demonstrate the efficacy of this approach. The sizes of the deterministic equivalent of our test problems range from having 386 continuous variables, 386 binary variables, and 386 constraints, up to 1795 continuous variables, 1539 binary variables, and 1028 constraints. The results reveal an average savings in computational effort by a factor of 9.5 in comparison with using a commercial mixed-integer programming package (CPLEX 8.1) on a deterministic equivalent formulation. We then explore an important application of SMIP to enhance the traditional airline fleet assignment models (FAM). Given a flight schedule network, the fleet assignment problem solved by airline companies is concerned with assigning aircraft to flight legs in order to maximize profit with respect to captured path- or itinerary-based demand. Because certain related crew scheduling regulations require early information regarding the type of aircraft serving each flight leg, the current practice adopted by airlines is to solve the fleet assignment problem using estimated demand data 10-12 weeks in advance of departure. Given the level of uncertainty, deterministic models at this early stage are inadequate to obtain a good match of aircraft capacity with passenger demands, and revisions to the initial fleet assignment become naturally pertinent when the observed demand differs considerably from the assigned aircraft capacities. From this viewpoint, the initial decision should embrace various market scenarios so that it incorporates a sufficient look-ahead feature and provides sufficient flexibility for the subsequent re-fleeting processes to accommodate the inevitable demand fluctuations. With this motivation, we propose a two-stage stochastic programming approach in which the first stage is concerned with the initial fleet assignment decisions and, unlike the traditional deterministic methodology, focuses on making only a family-level assignment to each flight leg. The second stage subsequently performs the detailed assignments of fleet types within the allotted family to each leg under each of the multiple potential scenarios that address corresponding path- or itinerary-based demands. In this fashion, the initial decision of what aircraft family should serve each flight leg accomplishes the purpose of facilitating the necessary crew scheduling decisions, while judiciously examining the outcome of future re-fleeting actions based on different possible demand scenarios. Hence, when the actual re-fleeting process is enacted several weeks later, this anticipatory initial family-level assignment will hopefully provide an improved overall fleet type re-allocation that better matches demand. This two-stage stochastic model is complemented with a secondary model that performs adjustments within each family, if necessary, to provide a consistent fleet type-assignment information for accompanying decision processes, such as yield management. We also propose several enhanced fleet assignment models, including a robust optimization model that controls decision variation among scenarios and a stochastic programming model that considers the recapture effect of spilled demand. In addition to the above modeling concepts and framework, we also contribute in developing effective solution approaches for the proposed model, which is a large-scale two-stage stochastic 0-1 mixed-integer program. Because the most pertinent information needed from the initial fleet assignment is at the family level, and the type-level assignment is subject to change at the re-fleeting stage according to future demand realizations, our solution approach focuses on assigning aircraft families to the different legs in the flight network at the first stage, while finding relaxed second-stage solutions under different demand scenarios. Based on a polyhedral study of a subsystem extracted from the original model, we derive certain higher-dimensional convex hull as well as partial convex hull representations for this subsystem. Accordingly, we propose two variants for the primary model, both of which relax the binary restrictions on the second-stage variables, but where the second variant then also accommodates the partial convex hull representations, yielding a tighter, albeit larger, relaxation. For each variant, we design a suitable solution approach predicated on Benders' decomposition methodology. Using certain realistic large-scale flight network test problems having 900 flight legs and 1,814 paths, as obtained from United Airlines, the proposed stochastic modeling approach was demonstrated to increase daily expected profits by about 3% (which translates to about $160 million per year) in comparison with the traditional deterministic model in present usage, which considers only the expected demand. Only 1.6% of the second-stage binary variables turn out to be fractional in the first variant, and this number is further reduced to 1.2% by using the tighter variant. Furthermore, when attempting to solve the deterministic equivalent formulation for these two variants using a commercial mixed-integer programming package (CPLEX 8.1), both the corresponding runs were terminated after reaching a 25-hour cpu time limit. At termination, the software was still processing the initial LP relaxation at the root node for each of these runs, and no feasible basis was found. Using the proposed algorithms, on the other hand, the solution times were significantly reduced to 5 and 19 hours for the two variants, respectively. Considering that the fleet assignment models are solved around three months in advance of departure, this solution time is well acceptable at this early planning stage, and the improved quality in the solution produced by considering the stochasticity in the system is indeed highly desirable. Finally, we address another practical workforce planning problem encountered by a global financial firm that seeks to manage multi-category workforce for functional areas located at different service centers, each having office-space and recruitment-capacity constraints. The workforce demand fluctuates over time due to market uncertainty and dynamic project requirements. To hedge against the demand fluctuations and the inherent uncertainty, we propose a two-stage stochastic programming model where the first stage makes personnel recruiting and allocation decisions, while the second stage, based on the given personnel decision and realized workforce demand, decides on the project implementation assignment. The second stage of the proposed model contains binary variables that are used to compute and also limit the number of changes to the original plan. Since these variables are concerned with only one quality aspect of the resulting workforce plan and do not affect feasibility issues, we replace these binary variables with certain conservative policies regarding workforce assignment change restrictions in order to obtain more manageable subproblems that contain purely continuous variables. Numerical experiments reveal that the stochastic programming approach results in significantly fewer alterations to the original workforce plan. When using a commercial linear programming package CPLEX 9.0 to solve the deterministic equivalent form directly, except for a few small-sized problems, this software failed to produce solutions due to memory limitations, while the proposed Benders' decomposition-based solution approach consistently solved all the practical-sized test problems with reasonable effort. To summarize, this dissertation provides a significant advancement in the algorithmic development for solving two-stage stochastic mixed-integer programs having 0-1 mixed-integer variables in both stages, as well as in its application to two important contemporary real-world applications. The framework for the proposed solution approaches is to formulate tighter relaxations via partial convex hull representations and to exploit the resulting structure using suitable decomposition methods. As decision robustness is becoming increasingly relevant from an economic viewpoint, and as computer technological advances provide decision-makers the ability to explore a wide variety of scenarios, we hope that the proposed algorithms will have a notable positive impact on solving stochastic mixed-integer programs. In particular, the proposed stochastic programming airline fleet assignment and the workforce planning approaches studied herein are well-poised to enhance the profitability and robustness of decisions made in the related industries, and we hope that similar improvements are adapted by more industries where decisions need to be made in the light of data that is shrouded by uncertainty. / Ph. D.

This paper presents a method that uses designed experiments and statistical models to extract information about how solver parameter settings perform for classes of mixed integer programs. The use of experimental design facilitates fitting a model that describes the response surface across all combinations of parameter settings, even those not explicitly tested, allowing identification of both desirable and poor settings. Identifying parameter settings that give the best expected performance for a specific class of instances and a specific solver can be used to more efficiently solve a large set of similar instances, or to ensure solvers are being compared at their best.

Each year the Railway Applications Section (RAS) of the Institution for Operations Research and the Management Sciences (INFORMS) posits a research problem to the world in the form of a competition. For 2012, the contest involved solving the Train Dispatching Problem (TDP) on a realistic 85 edge network for three different sets of input data. This work is an independent attempt to match or improve upon the results of the top three finishers in the contest using mixed integer programming (MIP) techniques while minimizing the use of heuristics. The primary focus is to partition the network in a manner that reduces the number of binary variables in the formulation as much as possible without compromising the ability to satisfy any of the contest requirements. This resulted in the ability to optimally solve this model for RAS Data Set 1 in 29 seconds without any problem-specific heuristics, variable restrictions, or variable fixing. Applying some assumptions about train movements allowed the same Data Set 1 solution to be found in 5.4 seconds. After breaking the larger Data Sets 2 and 3 into smaller sub-problems, solutions for Data Sets 2 and 3 were 28% and 1% better, respectively, than those of the competition winner. The time to obtain solutions for Data Sets 2 and 3 was 90 and 318 seconds, respectively.

Effective aeromedical evacuation of casualties is one of the most important problems in military medical systems because high-priority casualties will not survive without timely medical care. The decision making process for aeromedical evacuation consists of the following components: (1) identifying which aeromedical evacuation asset (see figure 1) to dispatch to the casualty, (2) locating aeromedical evacuation assets strategically in anticipation of incoming demand, and (3) deciding which medical treatment facility to transport the casualty. These decisions are further complicated because prioritization of casualties is based on severity of injury while aeromedical evacuation assets and medical treatment facilities operate with varying capabilities. In this dissertation, discrete optimization models are developed to examine dispatch, delivery, and location logistics for the effective aeromedical evacuation of casualties in military medical systems.

Scheduling refinery operation promises a big cut in logistics cost, maximizes efficiency, organizes allocation of material and resources, and ensures that production meets targets set by planning team. Obtaining accurate and reliable schedules for execution in refinery plants under different scenarios has been a serious challenge. This research was undertaken with the aim to develop robust methodologies and solution procedures to address refinery scheduling problems with uncertainties in process parameters. The research goal was achieved by first developing a methodology for short-term crude oil unloading and transfer, as an extension to a scheduling model reported by Lee et al. (1996). The extended model considers real life technical issues not captured in the original model and has shown to be more reliable through case studies. Uncertainties due to disruptive events and low inventory at the end of scheduling horizon were addressed. With the extended model, crude oil scheduling problem was formulated under receding horizon control framework to address demand uncertainty. This work proposed a strategy called fixed end horizon whose efficiency in terms of performance was investigated and found out to be better in comparison with an existing approach. In the main refinery production area, a novel scheduling model was developed. A large scale refinery problem was used as a case study to test the model with scheduling horizon discretized into a number of time periods of variable length. An equivalent formulation with equal interval lengths was also presented and compared with the variable length formulation. The results obtained clearly show the advantage of using variable timing. A methodology under self-optimizing control (SOC) framework was then developed to address uncertainty in problems involving mixed integer formulation. Through case study and scenarios, the approach has proven to be efficient in dealing with uncertainty in crude oil composition.

Nesta tese é realizado um estudo de caso em uma indústria química brasileira, no qual busca-se representar características da tomada de decisões para a programação da produção em plantas de bateladas. Para isso, foi proposto um modelo matemático do tipo MIP (Mixed Integer Programming) que considerou a disponibilidade de matérias-primas, múltiplas tarefas produtivas para um mesmo produto, tanques de armazenamento multiproduto, envase de produtos e demanda de produtos a granel e envasados. O objetivo principal desse estudo era permitir a obtenção de soluções compatíveis com a prática da empresa em tempo de processamento viável. A partir desse estudo de caso, foi efetuado um segundo estudo com objetivo de avaliar o desempenho de formulações matemáticas para a resolução de um problema de programação da produção. Foram considerados modelos clássicos das comunidades científicas de pesquisa operacional e de engenharia de sistemas de processo, além de um terceiro modelo desenvolvido a partir de conceitos dessas duas comunidades. Algumas características do estudo de caso não foram retratadas, como o consumo de matérias-primas e o envase dos produtos, porém, foram consideradas duas características comumente observadas em problemas da indústria de processos: bateladas com quantidade produzida flexível e tarefas que produzem mais de um produto. Por fim, um terceiro estudo foi realizado com base no estudo de caso da indústria química brasileira, porém, com um foco decisões mais próximas ao nível tático. Sendo assim, foi considerado apenas o dimensionamento de lotes, sem o sequenciamento da produção. Por outro lado, foram acrescentadas características pertinentes à aquisição de matérias-primas, como custos das matérias-primas e descontos por quantidade adquirida. O objetivo deste último trabalho era avaliar a influência da integração das decisões de dimensionamento de lotes e de aquisição das matérias-primas nos custos da cadeia produtiva durante todo o horizonte de planejamento. / In this thesis we developed a study case in a Brazilian chemical industry, in which the aim was to represent the characteristics of decision-making for production scheduling in batch plants. For this, a mixed integer programming model was proposed to consider the availability of raw materials, multiple productive tasks for the same product, multi-product storage tanks, product packaging and demand for products in bulk and packaged. The main objective of this study was develop a model that is able to obtain solutions that clould be used in practice for this chemical industry in viable processing time. From this study case, a second work was carried out to evaluate the performance of mathematical formulations to solve a problem of production scheduling. Classic models of operational research and process system engineering communities were considered, and a third model was developed from concepts of these two communities. Some features of the case study were not modelled, such as the consumption of raw materials and the product packaging, however, two characteristics usually present in process industries were considered: flexible batch production quantity and multi-product task production. Finally, a third study developed based on the study case of the Brazilian chemical industry, but with focus on decisions more familiar to the tactical level. Thus, only lot sizing was modelled, without production scheduling. On the other hand, features relevant raw material purchasing were included, such as raw material costs and discounts for quantity purchased. The objective of this last work was to evaluate the influence of integrating lot sizing decisions and raw material purchasing decisions in the overall costs of the production chain during the entire planning horizon.

Diversas pesquisas e estudos científicos mostram que uma grande porcentagem das pessoas com deficiência é excluída do mercado de trabalho, sobretudo em países em desenvolvimento. Com o intuito de alterar essa realidade, destacam-se, entre outras medidas, a criação de Centros de Trabalho para Deficientes (CTDs). Tais organizações empregam trabalhadores com deficiência em vários setores empresariais, dando-lhes oportunidades iniciais e preparando-os para que possam, mais tarde, ser inseridos no mercado de trabalho convencional. Vários destes centros operam linhas de produção, principal objeto de estudo desta tese. Nosso estudo é situado em uma etapa idealmente posterior aos CTDs, referente à inserção de trabalhadores com deficiência em linhas de produção convencionais. A demanda por estudos neste contexto tem crescido nos últimos anos, devido sobretudo a políticas corporativas de responsabilidade social e exigências legislativas, como a \"Lei das Cotas\", presentes em diversos países. O planejamento da operação de linhas de produção na presença de trabalhadores com deficiência envolve uma série de desafios, devido à heterogeneidade entre trabalhadores, que faz com que o tempo de execução das tarefas seja dependente de cada indivíduo. Nos deparamos, assim, com um problema de dupla alocação, em que as variáveis de decisão determinam as tarefas a serem inseridas em estações e a alocação de trabalhadores para as mesmas, de modo a otimizar alguma medida de eficiência. O balanceamento de linhas de produção convencionais com uma parcela de trabalhadores com deficiência é denominado problema de balanceamento de linhas de produção e integração de trabalhadores (ALWIBP, do inglês: assembly line worker integration and balancing problem), sendo um caso particular do problema de balanceamento de linhas de produção e designação de trabalhadores (ALWABP, do inglês: assembly line worker assignment and balancing problem), cuja ocorrência é mais comum em linhas de CTDs. Nosso objetivo consiste em estudar formas eficientes de proporcionar a integração de trabalhadores com deficiência em linhas convencionais. Para tanto, abordamos variações do ALWIBP que consideram: (i) minimização de diferentes funções objetivo (número de estações ou tempo de ciclo); (ii) linha de produção com leiautes distintos (simples ou em U); (iii) incertezas quanto ao tempo de execução de cada tarefa (abordagem robusta); (iv) estratégias de rotação de tarefas ou alocação de trabalhadores com deficiência na linha com espaçamento regular. Para cada uma destas extensões, foram desenvolvidos formulações matemáticas, métodos de resolução e novos conjuntos de instâncias teste. Experimentos computacionais indicam possibilidades de adaptação de linhas de produção convencionais à inserção de trabalhadores com deficiência, a custos adicionais baixos ou quase nulos. Portanto, este trabalho oferece alternativas para uma maior flexibilidade na integração de pessoas com deficiência, tornando-os tão eficientes quanto qualquer outro trabalhador denominado \"convencional\". / A number of studies show that a large percentage of disabled people are excluded from the labor market, in particular in developing countries. In order to deal with this problem, one can highlight the importance of Sheltered Work Centers for Disabled (SWDs). These organizations employ disabled workers in various corporate sectors, giving them initial opportunities and preparing them so that they can be later integrated into the conventional labor market. Many of these centers operate assembly lines, the main object of study of this thesis. Our study considers an ideally later stage of SWDs, related with the insertion of disabled workers in conventional assembly lines. The demand for studies in this field has grown over the years, due to corporate social responsibility policies and legal requirements such as \"quotas legislations\", present in many countries. Planning the operation of assembly lines with disabled workers involves a series of challenges due to the heterogeneity among workers, which are reflected in task times being worker dependent. This results in a double allocation problem, where decisions must determine both the tasks and the workers to be assigned to the stations, in order to optimize some efficiency measure. The conventional assembly line balancing with a parcel of disabled workers is known as the assembly line worker integration and balancing problem (ALWIBP), being a particular case of the assembly line worker assignment and balancing problem (ALWABP), which occurance is more common in SWDs. Our goal consists in studying efficient ways to promote the integration of people with disabilities in conventional assembly lines. For that, we address ALWIBP variants that consider: (i) minimization of different objective functions (number of stations or cycle time); (ii) different assembly line layouts (simple or U-shaped); (iii) uncertainties on task execution times (robust approach); (iv) job rotation strategies or allocation of disabled workers in the line with regular spacing. For each of these extensions, we develop mathematical formulations, solution methods and new sets of benchmark instances. Computational experiments indicate possibilities for adapting conventional assembly lines to the insertion of disabled workers, at low or close to null additional costs. Therefore, this study offers alternatives ways of increasing exibility in the integration of people with disabilities, making them as efficient as any other conventional worker.

Nesta tese é realizado um estudo de caso em uma indústria química brasileira, no qual busca-se representar características da tomada de decisões para a programação da produção em plantas de bateladas. Para isso, foi proposto um modelo matemático do tipo MIP (Mixed Integer Programming) que considerou a disponibilidade de matérias-primas, múltiplas tarefas produtivas para um mesmo produto, tanques de armazenamento multiproduto, envase de produtos e demanda de produtos a granel e envasados. O objetivo principal desse estudo era permitir a obtenção de soluções compatíveis com a prática da empresa em tempo de processamento viável. A partir desse estudo de caso, foi efetuado um segundo estudo com objetivo de avaliar o desempenho de formulações matemáticas para a resolução de um problema de programação da produção. Foram considerados modelos clássicos das comunidades científicas de pesquisa operacional e de engenharia de sistemas de processo, além de um terceiro modelo desenvolvido a partir de conceitos dessas duas comunidades. Algumas características do estudo de caso não foram retratadas, como o consumo de matérias-primas e o envase dos produtos, porém, foram consideradas duas características comumente observadas em problemas da indústria de processos: bateladas com quantidade produzida flexível e tarefas que produzem mais de um produto. Por fim, um terceiro estudo foi realizado com base no estudo de caso da indústria química brasileira, porém, com um foco decisões mais próximas ao nível tático. Sendo assim, foi considerado apenas o dimensionamento de lotes, sem o sequenciamento da produção. Por outro lado, foram acrescentadas características pertinentes à aquisição de matérias-primas, como custos das matérias-primas e descontos por quantidade adquirida. O objetivo deste último trabalho era avaliar a influência da integração das decisões de dimensionamento de lotes e de aquisição das matérias-primas nos custos da cadeia produtiva durante todo o horizonte de planejamento. / In this thesis we developed a study case in a Brazilian chemical industry, in which the aim was to represent the characteristics of decision-making for production scheduling in batch plants. For this, a mixed integer programming model was proposed to consider the availability of raw materials, multiple productive tasks for the same product, multi-product storage tanks, product packaging and demand for products in bulk and packaged. The main objective of this study was develop a model that is able to obtain solutions that clould be used in practice for this chemical industry in viable processing time. From this study case, a second work was carried out to evaluate the performance of mathematical formulations to solve a problem of production scheduling. Classic models of operational research and process system engineering communities were considered, and a third model was developed from concepts of these two communities. Some features of the case study were not modelled, such as the consumption of raw materials and the product packaging, however, two characteristics usually present in process industries were considered: flexible batch production quantity and multi-product task production. Finally, a third study developed based on the study case of the Brazilian chemical industry, but with focus on decisions more familiar to the tactical level. Thus, only lot sizing was modelled, without production scheduling. On the other hand, features relevant raw material purchasing were included, such as raw material costs and discounts for quantity purchased. The objective of this last work was to evaluate the influence of integrating lot sizing decisions and raw material purchasing decisions in the overall costs of the production chain during the entire planning horizon.

Este texto trata de um dos temas fundamentais no planejamento de produção, o problema de dimensionamento de lotes de um único item. Uma descrição sucinta e informal do problema segue abaixo. Considere um intervalo de tempo dividido em períodos e que a cada período de tempo está associada a demanda de um item. Dados os custos e as eventuais restrições na produção e no armazenamento, determine os períodos em que se produzirá e em que quantidade para que as demandas sejam atendidas com o menor custo possível, respeitando as restrições impostas. Apresentamos aqui resultados sobre a estrutura ótima do problema, sobre complexidade e algoritmos para os casos básicos do problema / This text studies one of the core subjects in production planning, the single-item lot-sizing problem. A brief and informal description of this problem follows below. Considering a time interval split into time periods and that there is a demand of an item associated with each time period. Given production and holding costs and possibly production and holding restrictions, determine in which periods the production must occur and in which quantity, in order to attend the demands with a minimum cost, without violate any restriction. Here, it will be shown some results about the optimal structure of the problem, about the complexity and algorithms for the simpler cases