This thesis aims to review and analyze the scheduling problem in general and Job Shop Scheduling Problem (JSSP) in particular and the solution techniques applied to these problems. The JSSP is the most general and popular hard combinational optimization problem in manufacturing systems. For the past sixty years, an enormous amount of research has been carried out to solve these problems. The literature review showed the inherent shortcomings of solutions to scheduling problems. This has directed researchers to develop hybrid approaches, as no single technique for scheduling has yet been successful in providing optimal solutions to these difficult problems, with much potential for improvements in the existing techniques. The hybrid approach complements and compensates for the limitations of each individual solution technique for better performance and improves results in solving both static and dynamic production scheduling environments. Over the past years, hybrid approaches have generally outperformed simple Genetic Algorithms (GAs). Therefore, two novel priority heuristic rules are developed: Index Based Heuristic and Hybrid Heuristic. These rules are applied to benchmark JSSP and compared with popular traditional rules. The results show that these new heuristic rules have outperformed the traditional heuristic rules over a wide range of benchmark JSSPs. Furthermore, a hybrid GA is developed as an alternate scheduling approach. The hybrid GA uses the novel heuristic rules in its key steps. The hybrid GA is applied to benchmark JSSPs. The hybrid GA is also tested on benchmark flow shop scheduling problems and industrial case studies. The hybrid GA successfully found solutions to JSSPs and is not problem dependent. The hybrid GA performance across the case studies has proved that the developed scheduling model can be applied to any real-world scheduling problem for achieving optimal or near-optimal solutions. This shows the effectiveness of the hybrid GA in real-world scheduling problems. In conclusion, all the research objectives are achieved. Finaly, the future work for the developed heuristic rules and the hybrid GA are discussed and recommendations are made on the basis of the results. / Board of Trustees, Endowment Fund Project, KPK University of Engineering and Technology (UET), Peshawar and Higher Education Commission (HEC), Pakistan

Yes / An Improved Discrete Cuckoo Search (IDCS) is proposed in this paper to solve resource-constrained project scheduling problems (RCPSPs). The original Cuckoo Search (CS) was inspired by the breeding behaviour of some cuckoo species and was designed specifically for application in continuous optimisation problems, in which the algorithm had been demonstrated to be effective. The proposed IDCS aims to improve the original CS for solving discrete scheduling problems by reinterpreting its key elements: solution representation scheme, Lévy flight and solution improvement operators. An event list solution representation scheme has been used to present projects and a novel event movement and an event recombination operator has been developed to ensure better quality of received results and improve the efficiency of the algorithm. Numerical results have demonstrated that the proposed IDCS can achieve a competitive level of performance compared to other state-of-the-art metaheuristics in solving a set of benchmark instances from a well-known PSPLIB library, especially in solving complex benchmark instances. / Partially funded by the Innovate UK project HARNET – Harmonised Antennas, Radios and Networks under contract no. 100004607.

In today's competitive market place, all the components of a supply chain must be well coordinated to achieve economic and service goals. This dissertation is devoted to the modeling, analysis, and development of solution approaches for some logistics problems with emphasis on coordination of various supply chain components and decisions. Specifically, we have addressed four problems in this domain that span various decision levels. The first problem deals with integrated production and shipping scheduling for a single manufacturer and multiple customers. We develop an optimum-seeking algorithm and a fast heuristic, both of which exploit structural properties of the problem. The second problem is a joint production and delivery scheduling problem in which a single vendor supplies goods to a single buyer over a finite horizon. We model this multi-period problem by using a dynamic programming framework and develop an effective Lagrange multiplier method for the solution of the single-period problem, which is then used to solve the multi-period problem. We show that the optimal shipments in each period follow a pattern of geometric-then-equal sizes except for the last shipment, which may be of a larger size. We also show that an optimal solution for the infinite horizon problem can be derived as a special case of our finite horizon approach. In addition, we propose two fast heuristic methods, which, as we show, can obtain almost optimal solutions. We also address the design and logistics operation of biomass feedstock supply chain. To that end, we consider two problems. The first of these problems arises in the context of delivering biomass sorghum to a biorefinery. We propose multi-period, mixed integer linear programming models, which prescribe the strategic and tactical logistics decisions. Our aim is to investigate different logistical configurations available in a sorghum biomass feedstock logistics system. The second of these problems further allows sharing of loadout equipment among storage facilities. We develop an efficient Benders decomposition-based algorithm, and also, two heuristic methods that are capable of effectively solving large-scale instances. We also show the advantage of using mobile equipment. / Doctor of Philosophy / Invariably, logistics cost constitutes a significant portion of the total cost incurred in operating a supply chain. In today’s fierce market competition, it is imperative to reduce this cost to a maximum extent. To that end, our work in this dissertation is devoted to the modeling, analysis, and development of solution approaches for some supply chain problems with the aim of reducing logistics cost. Specifically, we address four problems that span strategic-, tactical- and operational-level decisions in supply chain optimization. The first problem that we address deals with integrated production and shipping scheduling for a single manufacturer and multiple customers. Our aim is to integrate the production and shipping functions of a manufacturer for the objective of minimizing the sum of the shipping cost and the penalty incurred for late deliveries. We develop an optimum-seeking algorithm and a fast heuristic both of which exploit structural properties of the problem. The results of our computational investigation reveal efficacy of our approaches and a significant benefit that accrues from integrating the production and distribution functions. In the second problem, we address a joint production and delivery scheduling problem in which a single vendor supplies goods to a single buyer over a finite horizon. The vendor’s production rate and buyer’s demand rate can vary from period to period and are known in advance. The objective is to determine a production/shipment schedule that minimizes the total cost of production setup, shipment of orders, and holding of inventory at both the vendor and the buyer. We model this problem as a dynamic program, each stage of which constitutes a single-period problem with prescribed starting and ending inventory levels. We develop an effective approach for the solution of this single-period problem, which is then embedded within the dynamic programming framework. We show that the optimal shipments in each period follow a pattern of geometric-then-equal sizes except for the last shipment, which may be larger in size. We show that an optimal solution for the infinite horizon problem can be obtained as a special case of our finite horizon approach. In addition, we propose two fast heuristic methods, which, as we show, can obtain almost optimal solutions. For the third problem, we aim to address the design and operation of a biomass feedstock supply chain. We first present a comprehensive taxonomic literature review of the work in this area that exploits the operations research (OR) methodologies. Then, we study sorghum-biomass-to-biofuel logistics supply chain, and call it as a sorghum biomass feedstock logistics system (S-BFLS). We propose a multi-period, mixed integer linear programming model which prescribes the strategic locations and sizes of storage facilities, number of equipments to purchase, and allocation of farms to satellite storage facilities (SSLs), as well as tactical decisions including period-to-period biomass transportation flows and period-to-period biomass inventory plans. We study a wide spectrum of available harvest, preprocessing, transportation, and storage options as a part of the sorghum biomass feedstock logistics system. We have also investigated the option of just-in-time (JIT) delivery in conjunction with regular delivery, and call it as a hybrid delivery system. Our model is applied to a real-life-inspired case. Based on our analysis, the most cost-effective S-BFLS consists of forage-chopping for harvesting, bunkers or bags for ensiling, and hybrid delivery. Ensiling by modules is not found to be as cost-effective as by bags or bunkers due to the occurrence of high equipment ownership cost and operating cost. Compression of biomass is also not found to be cost-effective. It incurs extra equipment ownership and operating costs while not amounting to sufficient reduction in transportation cost because of the requirement of over 50% moisture content for ensiling. Forage-chop harvest and whole-stalk harvest have little difference in economic effectiveness. The hybrid delivery system is found to be effective since it reduces logistics cost for all the configurations. In the fourth and last problem, we permit sharing of mobile equipment among SSLs for loading biomass on tractor-trailers. We develop an efficient Benders decomposition algorithm (BA) to solve this problem. Our model formulation implicitly takes into account transportation of loadout equipment among SSLs. The BA further takes advantage of this feature of our formulation and Benders cuts. We have also proposed two fast approximate methods called Heuristics H1 and H2, both of which exploit the decision hierarchy of the problem. Computational experiments reveal efficacy of the proposed methods. Heuristic H2 generates fast and high quality solutions. However, the BA generates solution of desired accuracy (optimality gap) when not optimal. Our real-life-inspired case study has shown that 1.73–4.13% of cost reduction can be achieved by mobilizing loadout equipment in a BFSC. Also, expensive equipment leads to a greater benefit due to mobilization.

In this paper we investigate and compare multi-objective and weighted single objective approaches to a real world workforce scheduling problem. For this difficult problem we consider the trade off in solution quality versus population diversity, for different sets of fixed objective weights. Our real-world workforce scheduling problem consists of assigning resources with the appropriate skills to geographically dispersed task locations while satisfying time window constraints. The problem is NP-Hard and contains the Resource Constrained Project Scheduling Problem (RCPSP) as a sub problem. We investigate a genetic algorithm and serial schedule generation scheme together with various multi-objective approaches. We show that multi-objective genetic algorithms can create solutions whose fitness is within 2% of genetic algorithms using weighted sum objectives even though the multi-objective approaches know nothing of the weights. The result is highly significant for complex real-world problems where objective weights are seldom known in advance since it suggests that a multi-objective approach can generate a solution close to the user preferred one without having knowledge of user preferences.

Yes / In this paper, a new population-based and nature-inspired metaheuristic algorithm, Discrete Flower Pollination Algorithm (DFPA), is presented to solve the Resource Constrained Project Scheduling Problem (RCPSP). The DFPA is a modification of existing Flower Pollination Algorithm adapted for solving combinatorial optimization problems by changing some of the algorithm's core concepts, such as flower, global pollination, Lévy flight, local pollination. The proposed DFPA is then tested on sets of benchmark instances and its performance is compared against other existing metaheuristic algorithms. The numerical results have shown that the proposed algorithm is efficient and outperforms several other popular metaheuristic algorithms, both in terms of quality of the results and execution time. Being discrete, the proposed algorithm can be used to solve any other combinatorial optimization problems. / Innovate UK / Awarded 'Best paper of the Month'

Studiens syfte är att undersöka optimala orderstorlekar på kaffeproducenten Zoégas förpackningslinjer i Helsingborg. Bakgrunden är att företaget är omedvetna om hur dagens orderstorlekar påverkar produktionen, med hänsyn till produktivitet samt ställ- och lagerhållningskostnader. Fallföretaget har dessutom en vilja att på längre sikt att införa ”pull planning”, vilket understödjer vetskap om förpackningslinjernas förutsättningar. Studien är i huvudsak uppdelad i två distinkta moment; ett som jämför historisk produktivitet i förhållande till orderstorlek och ett som beräknar optimal orderstorlek med hjälp av schemaläggningsverktyget ”Economic lot scheduling problem” (ELSP). Ena förpackningslinjen utmynnar i ett klassiskt fall av ”basic period approach”, som frekvent har behandlats inom litteraturen för ELSP. De andra linjerna har ställtider som beror på ordning, vilket komplicerar tillvägagångssättet. Huvuddelen av datainsamlingen har möjliggjorts genom tillgång till data från företagets produktions- och affärssystem. ELSP-resultatet genererar orderstorlekar och tillverkningsscheman för samtliga förpackningslinjer; optimerade med kostnadsminimering. Studien fann även statistiskt signifikanta samband mellan orderstorlek och produktivitet på Zoégas; samband som resulterade i gränsvärden för mest produktiva orderstorlekar. Majoriteten av ELSP-storlekarna placerade sig innanför dessa gränser. Slutligen fann studien att företaget, vid implementering av studiens förbättringsförslag, kan öka sin produktomsättning och på så sätt vara bättre förberedda för införande av ”pull planning”. / The purpose of this thesis is to investigate optimal order quantities at the coffee producer Zoéga’s packaging lines in Helsingborg. The company is currently unaware of how order quantities affect their production; with regards to productivity as well as setup- and holding costs. With a long-term vision of incorporating “pull planning”, the case company also needs to evaluate the capabilities of their current production system. The case study mainly addresses two areas; one comparing historical productivity in relation to order quantity, and one determining optimal order quantity with the scheduling-tool known as “Economic lot scheduling problem”. One of the packaging lines results in a classic case of “Basic period approach”, a problem frequently reviewed in ELSP-literature. The other lines have sequence dependent setup times, which required a more complex model. The primarily data collection has been from internal production- and management systems. The ELSP-results generated optimal order quantities and production schedules for all packaging lines. The study also found statistically significant correlations between order quantity and productivity for Zoéga’s. These correlations compiled upper and lower limits for the most productive order quantities; where most of the ELSP-quantities placed inside these limits. Finally, the study shows that by implementing these suggestions, Zoéga’s could speed up their product turnover and be better prepared for “pull planning” implementation in the future.

Especialmente nas últimas quatro décadas, muitos estudos se voltaram às variáveis determinantes para a implementação efetiva de sistemas flexíveis de manufatura, tais como seu design, sequenciamento e controle. Neste ínterim, o manejo apropriado do conjunto de ferramentas necessárias para a fabricação de um respectivo lote de produtos foi destacado como fator crucial no desempenho do sistema de produção como um todo. Neste trabalho, abordamos a otimização do número de inserções e remoções de ferramentas no magazine de uma ou mais máquinas numericamente controladas, admitindo-se que uma parcela significativa do tempo de produção é dispensada com estas trocas de ferramentas. De forma mais precisa, a minimização do número de trocas de ferramentas consiste em determinar a ordem de processamento de um conjunto de tarefas, bem como o carregamento ótimo do(s) compartimento(s) de ferramentas da(s) máquina(s), a fim de que o número de trocas seja minimizado. Como demostrado na literatura, mesmo o caso restrito à existência de apenas uma máquina de manufatura (MTSP, do inglês Minimization of Tool Switches Problem) é um problema NP-difícil, o que pode justificar o fato observado de que a maioria dos métodos de solução existentes o abordam de maneira heurística. Consequentemente, concluímos que a extensão ao contexto de múltiplas máquinas é também um problema NP-difícil, intrinsecamente complicado de se resolver. Nosso objetivo consiste em estudar formas eficientes de otimizar o número de trocas de ferramentas em ambientes equipados com máquinas flexíveis de manufatura. Para tanto, abordamos o problema básico, MTSP, e duas de suas variantes, em níveis crescentes de abrangência, que consideram o sequenciamento de tarefas em um conjunto de: (i) máquinas paralelas e idênticas (IPMTC, do inglês Identical Parallel Machines problem with Tooling Constraints); e (ii) máquinas paralelas e idênticas inseridas em um ambiente do tipo job shop (JSSPTC, do inglês Job Shop Scheduling Problem with Tooling Constraints). Classificamos as principais contribuições desta tese com respeito a três aspectos. Primeiramente, empurramos as fronteiras da literatura do MTSP propondo formulações matemáticas para os problemas IPMTC e JSSPTC. Desenvolvemos, também, algoritmos baseados em diferentes técnicas de resolução, como redução de domínio, Path relinking, Adaptive large neighborhood search e a elaboração de regras de despacho. Por último, com o intuito de bem avaliar a eficiência e o alcance de nossos métodos, propomos três novos conjuntos de instâncias teste. Acreditamos, assim, que este trabalho contribui positivamente com pesquisas futuras em um cenário abrangente dentro da minimização das trocas de ferramentas em um sistema flexível de manufatura. / Several studies, especially in the last four decades, have focused on decisive elements for the effective implementation of flexible manufacturing systems, such as their design, scheduling and control. In the meantime, the appropriate management of the set of tools needed to manufacture a certain lot of products has been highlighted as a crucial factor in the performance of the production system as a whole. This work deals with the optimization of the number of insertions and removals from the magazine of one or more numerical controlled machines, assuming that a significant part of the production time is wasted with such tool switches. More precisely, the minimization of tool switches problem (MTSP) consists on determining the processing order of a set of jobs, as well as the optimal loading of the magazine(s) of the machine(s), so that the total number of switches is minimized. As formally demonstrated in the literature, the MTSP is a NP-hard problem even when considering the existence of only one manufacturing machine, which could justify the fact that most of the solution methods tackles it heuristically. We thus conclude that its extension to the case of multiples machines is also NP-hard and, therefore, a problem intrinsically difficult to solve. Our goal consists in studying efficient ways to optimize the number of tool switches in environments equipped with flexible manufacturing machines. For that, we address the basic problem, MTSP, and two MTSP variants, in increasing levels of reach, that consider the job sequencing in a set of: (i) identical parallel machines (Identical Parallel Machines problem with Tooling Constraints, IPMTC); and (ii) identical parallel machines inserted in a job shop environment (Job Shop Scheduling Problem with Tooling Constraints, JSSPTC). The main contributions of this thesis are classified according three aspects. First, we pushed the frontier of the MTSP literature by proposing mathematical formulations for IPMTC and JSSPTC. We also developed algorithms based on different solution techniques, such as domain reduction, Path Relinking, Adaptive Large Neighborhood Search and dispatching rules. Finally, to fully evaluate the effectiveness and limits of our methods, three new sets of benchmark instances were generated. We believe that this work contributes positively to the future of research in a broad scenario inside the minimization of tool switches in flexible manufacturing systems.

Made available in DSpace on 2016-06-02T19:53:32Z (GMT). No. of bitstreams: 1 PEREZ_MARTINEZ_Karim_2013.pdf: 1435441 bytes, checksum: dc4551ff55ece881790f51193596d2bd (MD5) Previous issue date: 2013-09-27 / Financiadora de Estudos e Projetos / This research deals with the production planning and scheduling problem in the molded pulp packing industry, studying particularly a plant of molded pulp packages for eggs. The production process can be subdivided into two processes: molding process and printing process. The main challenge for production planning activities is on the molding process, where products are produced through tooling that have several molds. These tooling are called "Molding Patterns" or " Conformation Patterns". Each one of the molding patterns can contain one, two, until three kind of molds, allowing to produce several products simultaneously. Producing several products at the time can generate large inventory for low demand products and also null inventory for high demand products. Thus, different inventory levels are defined for each product based on their demand behavior, in order to control inventory quantities. In this way, decisions related to the production planning and scheduling in this production process involve deciding which molding patterns should used, how much time they should be used, and how they should be sequenced. All these should be make taking into account a parallel machine system and sequenced-dependent setups time and costs, in order to minimize inventory and setups costs, as well as penalties associated to inventory out of the specified inventory levels of each product. To represent this problem we proposed two formulations: the first one based on the Capacitated Lot Sizing and Scheduling Problem (CLSP), and the second one based on the General Lot Sizing and Scheduling Problem (GLSP). The results from the models were compared in a set of real word instances of a plant, in order to check the adequacy to represent the decisions involved in the studied production process, as well as the main differences between their production plans and performance of the models. The proposed formulations were also adapted for a particular instance, in order to compare their productions plans against the production plan implement by the studied plant. Results show that the proposed models represent rightly decisions involved in the production planning and scheduling in the molded pulp. Furthermore, the solutions obtained by the proposed models represent production plans with costs significantly lower than the schedule implemented by a real plant in this kind of industry. / Este trabalho aborda o problema de planejamento e programação da produção na indústria de embalagens em polpa moldada, considerando particularmente uma fábrica de embalagens para acondicionamento de ovos. O processo de produção pode ser subdividido em dois processos: processo de moldagem e processo de estampagem. O principal desafio para as atividades de planejamento encontra-se no processo de moldagem, em que a obtenção dos produtos depende da utilização de diferentes ferramentais, formados por um conjunto de moldes. Estes ferramentais são chamados de padrões de conformação ou padrões de moldagem , e podem conter um, dois ou até três tipos de moldes, permitindo a produção simultânea de produtos diferentes. Esta produção simultânea de itens pode gerar grandes níveis de estoque de produtos de baixa demanda e estoques nulos para produtos de alta demanda, desta forma, são definidos diferentes níveis de estoque para cada produto com base no comportamento da sua demanda. As decisões envolvidas no planejamento e programação da produção neste tipo de processo envolve a escolha dos padrões de moldagem a serem utilizados, o tempo de produção de cada padrão, e a sequência em que estes devem ser programados, considerando um sistema de linhas paralelas idênticas e tempos e custos de preparação dependentes da sequência. Estas decisões devem ser definidas de modo a minimizar os custos de estocagem, preparação, e penalidades associadas ao desvio do volume do estoque em relação aos níveis estabelecidos para cada produto. Para representar o problema são propostas dois tipos de formulações: a primeira baseada no Problema de Dimensionamento de Lotes Capacitado (CLSP), e a segunda baseada no Problema de Dimensionamento e Sequenciamento de Lotes Geral (GLSP). Os resultados da resolução dos modelos são comparados com base em exemplares reais da fábrica em estudo, a fim de verificar sua adequação para representar as decisões envolvidas no sistema de produção, as principais diferenças nos planos de produção gerados, e o desempenho destes modelos. Os resultados obtidos demonstram que os modelos propostos representam adequadamente as decisões no sistema de produção estudado, e geram planos de produção significativamente melhores que os planos praticados pela fábrica.

La plupart des travaux de recherches sur les problèmes d'ordonnancement traitent le cas des ressources renouvelables, c'est-à-dire des ressources qui sont exigées en début d'exécution de chaque tâche et sont restituées en fin d'exécution. Peu d'entre eux abordent les problèmes à ressources consommables, c'est-à-dire des ressources non restituées en fin d'exécution. Le problème de gestion de projet à contraintes de ressources (RCPSP) est le problème à ressources renouvelables le plus traité dans la littérature. Dans le cadre de cette thèse, nous nous sommes intéressés à une généralisation du problème RCPSP qui correspond au cas où les tâches sont remplacées par des événements liés par des relations de précédence étendues. Chaque événement peut produire ou consommer une quantité de ressources à sa date d'occurrence et la fonction économique reste la durée totale à minimiser. Nous avons nommé cette généralisation ERCPSP (Extended RCPSP). Nous avons élaboré des modèles de programmation linéaire pour résoudre ce problème. Nous avons proposé plusieurs bornes inférieures algorithmiques exploitant les travaux de la littérature sur les problèmes cumulatifs. Ensuite, nous avons élargi la portée des méthodes utilisées pour la mise en place de méthodes de séparation et évaluation. Nous avons traité aussi des cas particuliers par des méthodes basées sur la programmation dynamique. / This thesis investigates the Extended Resource Constrained Project Scheduling Problem (ERCPSP). ERCPSP is a general scheduling problem where the availability of a resource is depleted and replenished at the occurrence times of a set of events. It is an extension of the Resource Constrained Project Scheduling Problem (RCPSP) where activities are replaced by events, which have to be scheduled subject to generalized precedence relations. We are interested in this thesis in proposing new methodologies and approaches to solve ERCPSP. First, we study some polynomial cases of this problem and we propose a dynamic programming algorithm to solve the parallel chain case. Then, we propose lower bounds, mixed integer programming models, and a branch-and-bound method to solve ERCPSP. Finally, we develop an instance generator dedicated to this problem.

Especialmente nas últimas quatro décadas, muitos estudos se voltaram às variáveis determinantes para a implementação efetiva de sistemas flexíveis de manufatura, tais como seu design, sequenciamento e controle. Neste ínterim, o manejo apropriado do conjunto de ferramentas necessárias para a fabricação de um respectivo lote de produtos foi destacado como fator crucial no desempenho do sistema de produção como um todo. Neste trabalho, abordamos a otimização do número de inserções e remoções de ferramentas no magazine de uma ou mais máquinas numericamente controladas, admitindo-se que uma parcela significativa do tempo de produção é dispensada com estas trocas de ferramentas. De forma mais precisa, a minimização do número de trocas de ferramentas consiste em determinar a ordem de processamento de um conjunto de tarefas, bem como o carregamento ótimo do(s) compartimento(s) de ferramentas da(s) máquina(s), a fim de que o número de trocas seja minimizado. Como demostrado na literatura, mesmo o caso restrito à existência de apenas uma máquina de manufatura (MTSP, do inglês Minimization of Tool Switches Problem) é um problema NP-difícil, o que pode justificar o fato observado de que a maioria dos métodos de solução existentes o abordam de maneira heurística. Consequentemente, concluímos que a extensão ao contexto de múltiplas máquinas é também um problema NP-difícil, intrinsecamente complicado de se resolver. Nosso objetivo consiste em estudar formas eficientes de otimizar o número de trocas de ferramentas em ambientes equipados com máquinas flexíveis de manufatura. Para tanto, abordamos o problema básico, MTSP, e duas de suas variantes, em níveis crescentes de abrangência, que consideram o sequenciamento de tarefas em um conjunto de: (i) máquinas paralelas e idênticas (IPMTC, do inglês Identical Parallel Machines problem with Tooling Constraints); e (ii) máquinas paralelas e idênticas inseridas em um ambiente do tipo job shop (JSSPTC, do inglês Job Shop Scheduling Problem with Tooling Constraints). Classificamos as principais contribuições desta tese com respeito a três aspectos. Primeiramente, empurramos as fronteiras da literatura do MTSP propondo formulações matemáticas para os problemas IPMTC e JSSPTC. Desenvolvemos, também, algoritmos baseados em diferentes técnicas de resolução, como redução de domínio, Path relinking, Adaptive large neighborhood search e a elaboração de regras de despacho. Por último, com o intuito de bem avaliar a eficiência e o alcance de nossos métodos, propomos três novos conjuntos de instâncias teste. Acreditamos, assim, que este trabalho contribui positivamente com pesquisas futuras em um cenário abrangente dentro da minimização das trocas de ferramentas em um sistema flexível de manufatura. / Several studies, especially in the last four decades, have focused on decisive elements for the effective implementation of flexible manufacturing systems, such as their design, scheduling and control. In the meantime, the appropriate management of the set of tools needed to manufacture a certain lot of products has been highlighted as a crucial factor in the performance of the production system as a whole. This work deals with the optimization of the number of insertions and removals from the magazine of one or more numerical controlled machines, assuming that a significant part of the production time is wasted with such tool switches. More precisely, the minimization of tool switches problem (MTSP) consists on determining the processing order of a set of jobs, as well as the optimal loading of the magazine(s) of the machine(s), so that the total number of switches is minimized. As formally demonstrated in the literature, the MTSP is a NP-hard problem even when considering the existence of only one manufacturing machine, which could justify the fact that most of the solution methods tackles it heuristically. We thus conclude that its extension to the case of multiples machines is also NP-hard and, therefore, a problem intrinsically difficult to solve. Our goal consists in studying efficient ways to optimize the number of tool switches in environments equipped with flexible manufacturing machines. For that, we address the basic problem, MTSP, and two MTSP variants, in increasing levels of reach, that consider the job sequencing in a set of: (i) identical parallel machines (Identical Parallel Machines problem with Tooling Constraints, IPMTC); and (ii) identical parallel machines inserted in a job shop environment (Job Shop Scheduling Problem with Tooling Constraints, JSSPTC). The main contributions of this thesis are classified according three aspects. First, we pushed the frontier of the MTSP literature by proposing mathematical formulations for IPMTC and JSSPTC. We also developed algorithms based on different solution techniques, such as domain reduction, Path Relinking, Adaptive Large Neighborhood Search and dispatching rules. Finally, to fully evaluate the effectiveness and limits of our methods, three new sets of benchmark instances were generated. We believe that this work contributes positively to the future of research in a broad scenario inside the minimization of tool switches in flexible manufacturing systems.