L’accroissement des profits, à travers l’amélioration de la productivité et la réduction des pertes de matières, représente un objectif primordial pour les entreprises industrielles. Dans cette thèse, nous nous intéressons à la résolution d’un problème industriel complexe réel avec des contraintes de temps. Nous nous sommes intéressés, tout d’abord, à un objectif principal, soit la minimisation des dates de fin de production, suivi d’un objectif secondaire qui est la minimisation des quantités de déchets non recyclables. Dans un premier temps, nous avons modéliséle problème par des modèles mathématiques, que nous avons résolu à l’aide d’un solveur. Dans un second temps, nous avons proposé une méthode approchée en forme d’algorithmes évolutionnistes. Cette méthode est appliquée aux deux objectifs mentionnés ci-dessus séparément. Une troisième méthode est ensuite appliquée à l’objectif principal, à savoir une méthode arborescente approchée. Nous avons testé les algorithmes proposés sur des instances inspirées d’un cas réel ; issues d’une entreprise du secteur agroalimentaire et sur des instances inspirées de la littérature. / Increasing profits, through the improvement of productivity and minimizing waste, is a primary objective for industrial companies. In this thesis, we are interested insolving a real complex industrial problem with time constraints. Firstly, we were interested in minimizing completion time (Cmax). Secondly, we focused on minimizing of non-recyclable waste. As a first step, we formulated the problem by mathematical models, which we solved using a solver. In a second step, we proposed an approximate method in the form of evolutionary algorithms. Both methods were applied to the two objectives mentioned above separately. Then, a third method which is a tree-search algorithm was applied only to the main objective. We tested the proposed algorithms on instances inspired from a real case; from an agri-food business, and also on instances inspired from the literature.

Este trabalho trata do problema de programação de operações no ambiente flow shop com máquinas múltiplas, com seus tempos de preparação (setup) assimétricos e dependentes da seqüência de processamento das tarefas. Este ambiente de produção é comum em indústrias gráficas, químicas, têxteis, de papel e de tinta, caracterizadas por sistemas com amplo mix de produtos. Qualquer processo produtivo requer um gerenciamento eficaz por meio do Planejamento e Controle da Produção (PCP). Esta atividade inclui a programação da produção, ou seja, a alocação de recursos para a execução de tarefas em uma base de tempo. A atividade de programação é uma das tarefas mais complexas no gerenciamento de produção, pois há a necessidade de lidar com diversos tipos diferentes de recursos e atividades simultaneamente. Além disso, o número de soluções possíveis cresce exponencialmente em várias dimensões, de acordo com a quantidade de tarefas, operações ou máquinas, conferindo uma natureza combinatorial ao problema. No ambiente estudado neste trabalho as operações de cada tarefa são executadas em múltiplos estágios de produção, podendo variar a quantidade de máquinas em cada um deles. Cada operação é processada por apenas uma máquina em cada estágio. Os tempos de preparação das máquinas possuem uma variabilidade relevante em função da ordem de execução das tarefas nas máquinas. A função-objetivo considerada é a minimização da duração total da programação (makespan). Foram desenvolvidos quatro métodos heurísticos construtivos com base em algoritmos reportados na literatura para solução de problemas flow shop permutacional e máquinas paralelas no ambiente cujo tempo de setup é dependente da seqüência. Como não foram encontrados na literatura métodos para programação no ambiente tratado neste trabalho, os algoritmos construídos foram comparados entre si. O foco da pesquisa foi o estudo da influência da relação entre as ordens de grandeza dos tempos de processamento e de setup em cada método de solução. Os resultados obtidos na experimentação computacional foram analisados e discutidos com base na porcentagem de sucesso, desvio relativo (%), desvio-padrão do desvio relativo e tempo médio de computação / This work adressess the hybrid flow shop scheduling problem with asymmetric sequence dependent setup times. This environment of production system is common in graphical, chemical, fabric, paper and ink industries. It’s characterized by systems with large mix of products. Any productive process requires an efficient management by means of Production Planning and Control. This activity includes scheduling, i.e., the resources allocation for the execution of jobs in a time base. Scheduling is one of the tasks most complex in production management, since it deals simultaneously with different types of resources and activities. Moreover, the number of possible solutions grows exponentially in some dimensions, in accordance with the number of jobs, operations or machines, conferring a combinatorial nature to the problem. In the environment studied in this work, the operations of each job are processed in multiple production stages. The number of machines in each stage can be different. Each operation is processed by only one machine in each stage. The setup times have a significant variability in function of the sequence of job processing on the machines. The objective is minimizing the total time to complete the schedule (makespan). Four constructive heuristic methods were developed on the basis of algorithms reported in the literature for solving permutation flow shop and parallel machine problems with sequence dependent setup times. The proposed heuristic methods have been compared between themselves, since no constructive heuristics have been found in the literature for the scheduling problem considered in this work. The focus of the research was the study of the influence of the relations among the range of the times processing and setup times in each method. The statistics used in order to evaluate the heuristic performances were the percentage of success (in finding the best solution), relative deviation, standard deviation of relative deviation and average computation time. Results from computational experience are discussed

A teoria de programação da produção preocupa-se em fornecer diretrizes e métodos eficientes para a utilização dos recursos nas atividades produtivas. Este trabalho investiga o problema de programação da produção em ambientes flow shop com máquinas múltiplas e tempos de preparação das máquinas assimétricos e dependentes da seqüência de execução das tarefas. A atividade de programação da produção constitui uma das várias funções executadas pelo planejamento e controle da produção, que tem como objetivo comandar e gerenciar o processo produtivo, e caracteriza uma das atividades mais complexas no gerenciamento dos sistemas produtivos. A programação da produção preocupa-se com a alocação de recursos sobre o tempo para executar um conjunto de tarefas. No ambiente estudado neste trabalho as operações de cada tarefa são executadas em múltiplos estágios de produção, podendo variar a quantidade de máquinas em cada um deles. Cada operação é processada por apenas uma máquina em cada estágio. Os tempos de preparação das máquinas possuem uma variabilidade relevante em função da ordem de execução das tarefas nas mesmas. A função-objetivo considerada é a minimização do tempo médio de fluxo. Foram desenvolvidos quatro métodos heurísticos construtivos com base em algoritmos reportados na literatura para solução do problema flow shop permutacional e máquinas paralelas cujo tempo de setup é dependente da seqüência de execução das tarefas. Como não foram encontrados na literatura métodos de solução para o problema investigado neste trabalho, os algoritmos propostos foram comparados entre si. Foi efetuado um estudo da influência da relação entre as ordens de grandeza dos tempos de processamento das tarefas e do setup das máquinas em cada método de solução. Os resultados obtidos na experimentação computacional foram analisados e discutidos com base na porcentagem de sucesso, desvio relativo, desvio-padrão do desvio relativo e tempo médio de computação. / Scheduling theory attempts to provide guidelines and efficient methods to the use of the resources in the productive activities. This study investigates the hybrid flow shop problem with asymmetric sequence dependent setup times. The activity of production scheduling constitute is one of the several functions carried by production planning and control, which has as the objective command and management the production system, and characterize is one of the tasks most complex in production management. This activity of the scheduling aims within the allocation of the resources for the execution of jobs in a time base. In the environment studied in this work, the operations of each job are processed in multiple production stages. The number of machines in each stage can be different. Each operation is processed by only one machine in each stage. The setup times have a significant variability in function of the sequence of job processing on the machines. The objective is minimizing the mean flow time. Four constructive heuristic methods were proposed on the basis of algorithms reported in the literature for solving permutation flow shop and parallel machine problems with sequence dependent setup times. The proposed heuristic methods will have compared between themselves, since no constructive heuristics have been found in the literature for the scheduling problem considered in this work. It was carried out the study of the influence of the relations among the range of the times processing and setup times in each method. The statistics used in order to evaluate the heuristic performances were the percentage of success (in finding the best solution), relative deviation, standard deviation of relative deviation and average computation time. Results from computational experience are discussed.

As the Army transitions to the Army Force Generation (ARFORGEN) deployment cycle, it must adjust its many operations in support of ARFORGEN. Specifically, the Initial Military Training (IMT) must be able to adjust the scheduling of its classes for newly enlisted service members to finish training such that they fulfill Brigade Combat Team (BCT) requirements within their common due windows. We formulate this problem as a lot streaming problem. Lot streaming splits a batch of jobs into sublots,which are then processed over the machines in an overlapping fashion. To schedule classes for the IMT, there are two stages that must be coordinated: Basic Training (BT) and Advanced Individual Training (AIT). For the Army Force Generation problem, the classes are considered as sublots that are streamed from one stage to the next. For this process, the model formulation must address determination of class sizes and scheduling of soldiers and classes at the two stages such that (1) the start times of the soldiers at Stage 2 are greater than their completion times at Stage 1, and (2) the assignment of requisite number of soldiers is made to each BCT, so as to minimize the total flow time. We propose a decomposition-based approach for the solution of this problem. In an effort to decompose the problem, the original lot streaming problem is reformulated such that the master problem selects an optimal combination of schedules for training classes and assigning soldiers to BCTs. A complete schedule selected in the master problem includes the assignments of soldiers to classes in BT, AIT, and their assignments to the BCTs, so as to minimize the total flow time as well as earliness and tardiness for regular Army units. Earliness and Tardiness are defined as the length of the time a soldier waits before and after the due date, respectively, of the BCT to which he or she is assigned. Our decomposition-based method enables solution of larger problem instances without running out of memory, and it affords CPU time reductions when compared with the CPU times required for these problem instances obtained via direct application of CPLEX 12.1. Our investigation into the structure of the problem has enabled further improvement of the proposed decomposition-based method. This improvement is achieved because of a result, which we show, that the first and second-stage scheduling problems need not be solved as one combined subproblem, but rather, they can be solved sequentially, the first stage problem followed by the second stage problem. The combination of Stage 1 and Stage 2 problems as one subproblem creates several additional enumerations of possible schedules the model must generate. By reducing this number of enumerations, the computational effort involved in solving the model reduces significantly, thereby allowing reductions in CPU time. In the Sequential approach, the completion times of soldiers determined at Stage 1 are passed to Stage 2 as bounds on their completion times at Stage 2. We prove that solving the combined subproblem sequentially as two subproblems is optimal when the first stage has no limit on the batch size and the ready times of the soldiers at Stage 1 are the same. For the Army Force Generation problem, we use unequal ready times, and therefore, solving the scheduling problems for the first two stages as sequential subproblems can lead to suboptimal solutions. Our experimental investigation shows efficacy of solving larger-sized problem instances with this method. We also recommend various potential additions to improve the Sequential approach for application to the overall Army problem. We have also demonstrated the use of our methodology to a real-life problem instance. Our methodology results in schedules for IMT with an estimated 28% reduction in mean flow time for soldiers over what is currently experienced in practice. We apply this Sequential approach to various extensions of the problem on hand that pertain to hybrid flow shop and agile manufacturing environments. Results of our computational investigation show the effectiveness of using the Sequential approach over direct solution by CPLEX from the viewpoint of both optimality gap and the CPU time required. In particular, we consider two different model configurations for a hybrid flow shop and three different model configurations for an agile manufacturing facility. / Ph. D.

In this work, we address a variety of problems with applications to `ethanol production from biomass', `agile manufacturing' and `mass customization' domains. Our motivation stems from the potential use of biomass as an alternative to non-renewable fuels, the prevalence of `flexible manufacturing systems', and the popularity of `mass customization' in today's highly competitive markets. Production scheduling and design and optimization of logistics network mark the underlying topics of our work. In particular, we address three problems, Biomass Logistics Problem, Hybrid Flow Shop Scheduling Problem, and Stochastic Demand Assembly Job Scheduling Problem. The Biomass Logistics Problem is a strategic cost analysis for setup and operation of a biomass supply chain network that is aimed at the production of ethanol from switchgrass. We discuss the structural components and operations for such a network. We incorporate real-life GIS data of a geographical region in a model that captures this problem. Consequently, we develop and demonstrate the effectiveness of a `Nested Benders' based algorithm for an efficient solution to this problem. The Hybrid Flow Shop Scheduling Problem concerns with production scheduling of a lot over a two-stage hybrid flow shop configuration of machines, and is often encountered in `flexible manufacturing systems'. We incorporate the use of `lot-streaming' in order to minimize the makespan value. Although a general case of this problem is NP-hard, we develop a pseudo-polynomial time algorithm for a special case of this problem when the sublot sizes are treated to be continuous. The case of discrete sublot sizes is also discussed for which we develop a branch-and-bound-based method and experimentally demonstrate its effectiveness in obtaining a near-optimal solution. The Stochastic Demand Assembly Job Scheduling Problem deals with the scheduling of a set of products in a production setting where manufacturers seek to fulfill multiple objectives such as `economy of scale' together with achieving the flexibility to produce a variety of products for their customers while minimizing delivery lead times. We design a novel methodology that is geared towards these objectives and propose a Lagrangian relaxation-based algorithm for efficient computation. / Doctor of Philosophy / In this work, we organize our research efforts in three broad areas - Biomass Supply Chain, Hybrid Flow Shop, and Assembly Job Shop, which are separate in terms of their application but connected by scheduling and logistics as the underlying functions. For each of them, we formulate the problem statement and identify the challenges and opportunities from the viewpoint of mathematical decision making. We use some of the well known results from the theory of optimization and linear algebra to design effective algorithms in solving these specific problems within a reasonable time limit. Even though the emphasis is on conducting an algorithmic analysis of the proposed solution methods and in solving the problems analytically, we strive to capture all the relevant and practical features of the problems during formulation of each of the problem statement, thereby maintaining their applicability. The Biomass Supply Chain pertains to the production of fuel grade ethanol from naturally occurring biomass in the form of switchgrass. Such a system requires establishment of a supply chain and logistics network that connects the production fields at its source, the intermediate points for temporary storage of the biomass, and bio-energy plant and refinery at its end for conversion of the cellulosic content in the biomass to crude oil and ethanol, respectively. We define the components and operations necessary for functioning of such a supply chain. The Biomass Logistics Problem that we address is a strategic cost analysis for setup and operation of such a biomass supply chain network. We focus our attention to a region in South Central Virginia and use the detailed geographic map data to obtain land use pattern in the region. We conduct survey of existing literature to obtain various transportation related cost factors and costs associated with the use of equipment. Our ultimate aim here is to understand the feasibility of running a biomass supply chain in the region of interest from an economic standpoint. As such, we represent the Biomass Logistics Problem with a cost-based optimization model and solve it in a series of smaller problems. A Hybrid Flow Shop (HFS) is a configuration of machines that is often encountered in the flexible manufacturing systems, wherein a particular station of machines can execute processing of jobs/tasks simultaneously. In our work, we approach a specific type of HFS, with a single machine at the first stage and multiple identical machines at the second stage. A batch or lot of jobs/items is considered for scheduling over such an HFS. Depending upon the area of application, such a batch is either allowed to be split into continuous sections or restricted to be split in discrete sizes only. The objective is to minimize the completion time of the last job on its assigned machine at the second stage. We call this problem, Hybrid Flow Shop Scheduling Problem, which is known to be a hard problem in literature. We aim to derive the results which will reduce the complexity of this problem, and develop both exact as well as heuristic methods in order to obtain near-optimal solution to this problem. An Assembly Job Shop is a variant of the classical Job Shop which considers scheduling a set of assembly operations over a set of assembly machines. Each operation can only be started once all the other operations in its precedence relationship are completed. Assembly Job Shop are at the core of some of the highly competitive manufacturing facilities that are principled on the philosophy of Mass Customization. Assuming an inherent nature of demand uncertainty, this philosophy aims to achieve ‘economy of scale’ together with flexibility to produce a variety of products for the customers while minimizing the delivery lead times simultaneously. We incorporate some of these challenges in a concise framework of production scheduling and call this problem as Stochastic Demand Assembly Job Scheduling Problem. We design a novel methodology that is geared towards achieving the set objectives and propose an effective algorithm for efficient computation.

Este trabalho trata o problema multi-estágios de programação da produção em ambientes Flow Shop com máquinas paralelas, apresentando um estágio de produção dominante (máquina única), no qual os tempos de preparação (setup) da máquina são assimétricos e dependentes da seqüência de execução das tarefas. Tal ambiente é constituído de k estágios de produção, com k = {4, 7}, divididos em três etapas assim definidas: na etapa um, o número de estágios de produção pode variar de um até cinco e cada estágio será constituído de m1 máquinas paralelas idênticas , com m1 &#8712 {2, 3, 4}, o que determina m1 flow shops paralelos. A etapa dois constitui o estágio dominante d, cuja localização oscila dependendo do número de estágios das etapas um e três. Por ultimo, a etapa três, semelhante à etapa um, possui m2 máquinas paralelas idênticas, onde m2 &#8712 {2, 3, 4} e m1 e m2 são gerados aleatoriamente. Todas as tarefas são processadas nas três etapas e o critério de desempenho é a otimização da duração total da programação (makespan) e também a análise do deslocamento do estágio dominante. A programação das tarefas é feita separadamente em cada uma das etapas. Na primeira etapa foi utilizado o método heurístico N&M para cada um dos m1 flow shops paralelos. Para segunda etapa foram desenvolvidos quatro regras e dois métodos heurísticos construtivos com base nos problemas do caixeiro viajante (TSP). Na última etapa, a alocação das tarefas é feita por ordem de chegada na máquina disponível ou com menor carga. Não foram encontrados na literatura trabalhos que retratassem ambientes dessa natureza, logo os métodos desenvolvidos foram comparados entre si. A experimentação computacional analisou os resultados obtidos por meio da porcentagem de sucesso de cada regra, desvio relativo entre os resultados de cada regra, deslocamento da posição do estágio dominante, influência das ordens de grandeza dos tempos de processamento e setup e tempo médio de computação. / This dissertation deals with problem multi-periods of production scheduling of the in Flow Shop environment with parallel machines, presenting a period of dominant production (single machine), in which the setup times for the processing of the jobs is asymmetric and sequence dependent on the execution of the jobs. Such environment is constituted by k periods of production, with k = {4, 7} divided in to three stages defined as: First stage: In stage one the number of production periods can vary from one to five, and each period will be constituted of m1 &#8712 {2, 3, 4} identical parallel machines, determining m1 parallel flow shops. Stage two - It constitutes the dominant period d, whose localization oscillates between the periods of stages one and three. Finally stage three it is similar to stage one, and has m2 &#8712 {2, 3, 4} identical parallel machines, where m1 and m2 Randomly generated. All the jobs are processed in the three stages and the objective is to optimize the total time to complete the scheduling (makespan) and also to analyze the displacement of the dominant period position. The scheduling of the jobs was performed separately in each of the stages. In the first stage the heuristic method N&M was used for each m1 parallel flow shops. In the second stage four constructive rules and two heuristic methods were developed based on traveling salesman problems (TSP). In the last stage the allocation of the jobs was performed according to the arrival time of the available machine or with lesser load. This type of work has not been found in literature; therefore the developed methods were compared among themselves. The statistics used in order to evaluate the heuristic performances were the percentage of success (in finding the best solution), relative deviation and average computational time. The displacement of the dominant period position as well as the influence of the relation of setup-times and processing-times, were also studied. The results of computational experience are discussed.

Este trabalho trata o problema multi-estágios de programação da produção em ambientes Flow Shop com máquinas paralelas, apresentando um estágio de produção dominante (máquina única), no qual os tempos de preparação (setup) da máquina são assimétricos e dependentes da seqüência de execução das tarefas. Tal ambiente é constituído de k estágios de produção, com k = {4, 7}, divididos em três etapas assim definidas: na etapa um, o número de estágios de produção pode variar de um até cinco e cada estágio será constituído de m1 máquinas paralelas idênticas , com m1 &#8712 {2, 3, 4}, o que determina m1 flow shops paralelos. A etapa dois constitui o estágio dominante d, cuja localização oscila dependendo do número de estágios das etapas um e três. Por ultimo, a etapa três, semelhante à etapa um, possui m2 máquinas paralelas idênticas, onde m2 &#8712 {2, 3, 4} e m1 e m2 são gerados aleatoriamente. Todas as tarefas são processadas nas três etapas e o critério de desempenho é a otimização da duração total da programação (makespan) e também a análise do deslocamento do estágio dominante. A programação das tarefas é feita separadamente em cada uma das etapas. Na primeira etapa foi utilizado o método heurístico N&M para cada um dos m1 flow shops paralelos. Para segunda etapa foram desenvolvidos quatro regras e dois métodos heurísticos construtivos com base nos problemas do caixeiro viajante (TSP). Na última etapa, a alocação das tarefas é feita por ordem de chegada na máquina disponível ou com menor carga. Não foram encontrados na literatura trabalhos que retratassem ambientes dessa natureza, logo os métodos desenvolvidos foram comparados entre si. A experimentação computacional analisou os resultados obtidos por meio da porcentagem de sucesso de cada regra, desvio relativo entre os resultados de cada regra, deslocamento da posição do estágio dominante, influência das ordens de grandeza dos tempos de processamento e setup e tempo médio de computação. / This dissertation deals with problem multi-periods of production scheduling of the in Flow Shop environment with parallel machines, presenting a period of dominant production (single machine), in which the setup times for the processing of the jobs is asymmetric and sequence dependent on the execution of the jobs. Such environment is constituted by k periods of production, with k = {4, 7} divided in to three stages defined as: First stage: In stage one the number of production periods can vary from one to five, and each period will be constituted of m1 &#8712 {2, 3, 4} identical parallel machines, determining m1 parallel flow shops. Stage two - It constitutes the dominant period d, whose localization oscillates between the periods of stages one and three. Finally stage three it is similar to stage one, and has m2 &#8712 {2, 3, 4} identical parallel machines, where m1 and m2 Randomly generated. All the jobs are processed in the three stages and the objective is to optimize the total time to complete the scheduling (makespan) and also to analyze the displacement of the dominant period position. The scheduling of the jobs was performed separately in each of the stages. In the first stage the heuristic method N&M was used for each m1 parallel flow shops. In the second stage four constructive rules and two heuristic methods were developed based on traveling salesman problems (TSP). In the last stage the allocation of the jobs was performed according to the arrival time of the available machine or with lesser load. This type of work has not been found in literature; therefore the developed methods were compared among themselves. The statistics used in order to evaluate the heuristic performances were the percentage of success (in finding the best solution), relative deviation and average computational time. The displacement of the dominant period position as well as the influence of the relation of setup-times and processing-times, were also studied. The results of computational experience are discussed.

Only if humanity acts quickly and resolutely can we limit global warming' conclude more than 25,000 academics with the statement of SCIENTISTS FOR FUTURE. The concern about global warming and the extinction of species has steadily increased in recent years.

<p>  </p> <p>The development of the semiconductor industry is encountering a giant leap recently as Moorse’s is extended to the next levels. Advanced nanomanufacturing technology is the major challenge in the way. Higher resolution down to a few nanometers as well as higher throughput is always the key. As the optical lithography determines the feature size, the photomask is still in need of a low-cost and high resolution maskless patterning tool. In another aspect, the growing information allows the generation and storage of data at ever faster rates, which has led to the era of big data reaching a heroic amount of 7 zettabytes of total data in 2020. Future growth requires the total shipment of data storage capacity to double roughly every two years or less. For the future generation of magnetic data storage, the bit patterned medium (BPM) in combination with the current heat assisted magnetic recording (HAMR) is expected to increase the areal storage capacity by another order of magnitude by physically isolating magnetic bits at the nanoscale. Electron beam lithography (EBL) as a universal maskless lithography technique shows great resolution but has a high tool cost and low process throughput. Scanning probe lithography (SPL) is another family of nanoscale patterning techniques with low tool cost but the practical throughput is still limited. For example, dip pen nanolithography utilizes an AFM probe as a writing pen in direct patterning, but the ink delivery is limited by the rate of ink’s capillary transport. Other SPLs such as thermal probes with capabilities of 3D fabrication and surface oxidation via chemical reactions are all facing similar limitations in throughput. One way of breaking this limitation is to use parallel writing with millions of probes which also faces uniformity problems. </p> <p>In this Ph.D. dissertation, we report our Surfing Scanning Probe lithography (SSPL) method which can boost the scanning speed of SPL by several orders of magnitudes at a low cost by using a hydro-aero-dynamic scanning scheme. We use a homemade patterning head to continuously scan over a partially-wet spinning substrate at a linear speed of meters per second. The head carries several metallic tips which emit electrons and induce electrochemical reactions inside a gap of 10 nm scale. We use a liquid phase precursor and deliver it using the near-field electrospinning method and microfluid structures during the fast patterning. The best linewidth demonstrated is about 15 nm in full-width half maximum (FWHM) which can be further improved using smaller scanning gaps and sharp probe tips. Besides direct writing with a liquid precursor, SSPL can work with gas precursors as well enabled by nano plasma. The rate of material deposition is much high than conventional SPL. The SSPL system is a low-cost nanopatterning technology to produce patterns at high throughput and high resolution.</p>

Olofsfors is a steel product manufacturer in Nordmaling, Sweden, producing steel edges for snowplows, tracks for forest machines, and wear edges for buckets on heavy equipment. Most of their products are heated to 900◦ C and then cooled down in water, so-called quenching, during the hardening process. A group of ovens and quench machines together form an oven system and this is used for the hardening. Since it takes a long time for the ovens to reach operating temperature, they are always kept on, which is why it is important to utilize them as effectively as possible. This project investigates the potential utilization increase of one of the three oven systems in the wear edge production unit. This oven system is part of a production line that consists of a saw and a mill, and can process products up to two meters in length, and is hereon called the two-meter line. The two-meter line has a natural inflow through the saw, but raw material produced in other parts of the factory can also be fetched from another inlet. The use of the other inlet is limited by the operator of the two-meter line who has to fetch the material with a forklift. This could be automated so that the operator would not have to handle this inlet. The purpose is to investigate the potential increases in utilization of the oven system for different degrees of automation in order to make the most of the machines and the operator at the two-meter line. In the end, a recommendation is given with a set of ideal properties of the investment that could improve productivity the most. The main method applied in order to explore the potential use of the oven system is a re-entrant flow shop scheduling model. As preceding steps, the production line is first mapped in order to find potential routes for different product families, then the order quantities in the production data are translated into jobs to be scheduled with the help of packing problems and batching rules. The scheduling model of the production line is then solved heuristically with a genetic algorithm based on the sequence of jobs entering the production line followed by a method for creating a deterministic schedule based on this initial sequence of jobs. Lastly, a sensitivity analysis is applied to the processing time for the steps performed by the operator to evaluate the results' robustness. The conclusion is that there is a substantial potential to increase the utilization of the oven system of the two-meter line. The largest potential is when the operator is not actively working at the production line; a maximum of 15.6 h on average. There does also exist a potential to increase utilization while the operator is working at the production line; a maximum of 3.9 h on average. The automation degree needed is high in both cases but due to different reasons. When the operator is not working, the automatic solution needs to work without supervision for longer periods of time, while, in the other case, it needs to be smart enough to adjust to not disturb the operator’s work. For the future, the recommendation is to focus the next step on finding investment options that could exploit the time when the operator is not working. By further specifying the potential investment alternatives, the cost factor can be added to the analysis as well. / Olofsfors AB är en stålproduktstillverkare i Nordmaling, Sverige, som producerar vägstål till bland annat snöplogar, band till skogsmaskiner och slitstål till entreprenadmaskiner. De flesta av deras produkter hettas upp till 900 C och släcks sedan i vatten under härdningsprocessen. En grupp av ugnar och härdmaskiner kallas tillsammans för ett ungsystem och det används till härdningen. Eftersom det tar lång tid att värma upp ugnarna står de alltid på-slagna och det är därför viktigt att använda dem så effektivt som möjligt.  I detta projekt har potentialen att öka användandet av ett av tre ugnsystem i slitstålsproduktionen undersökts.  Ugnsystemet i fråga är en del av en produktionslinje som också består av en såg och en fräs och kan härda artiklar med längder upp till två meter och kallas därför här tvåmeterslinjen. Den naturliga ingången för råmaterial i produktionslinjen är genom sågen, men det finns även en alternativ ingång för råmaterial som förbehandlats i tidigare produktionssteg i fabriken. Användandet av den andra ingången till produktionlinjen begränsas av att operatören i produktionslinjen måste hämta materialet med truck. Detta in-flöde skulle gå att automatisera så att operatören inte skulle behöva hämta dessa artiklar.  Syftet är att undersöka det potentiella ökade nyttjandet av ugnsystemet för olika grader av automation för att bäst använda maskiner och operatör i tvåmeterslinjen. I slutet ges en rekommendation gällande vilka egenskaper investeringen bör ha för att öka produktiviteten mest.  Huvudmetoden för att undersöka möjligt ökat nyttjande av ugnarna är en schemaläggningsmodel. Som underliggande steg kartläggs först produktionslinjen och de olika rutter som olika produktfamiljer tar genom produktionslinjen. Produktkvantiteterna för varje order i produktionsdatan omvandlas sedan till jobb som kan schemaläggas genom packningsproblem och regler för laststorlekar i de olika maskinerna. Schemaläggningsmodellen löses sedan heuristiskt med hjälp av en genetisk algoritm som bestämmer den initiala sekvensen av jobben i första steget, tillsammans med en deterministisk metod för att skapa ett helt schema baserat på den initiala sekvensen av jobben. Slutligen genomförs en känslighetsanalys på processtiderna för steg som motsvarar operatören för att undersöka hur robust resultatet är.    Slutsatsen är att det finns en stor potential att öka nyttjandet av ugnsystemet i tvåmeterslinjen. Den största potentialen är när operatören inte arbetar aktivt vid produktionslinjen, med ett maximum på ca 15,6 h per dag. Det finns också en möjlighet att utöka nyttjandet av ugnarna under tiden som operatören arbetar aktivt med ordrar och outnyttjad tid då är 3,9 h i genomsnitt. Graden av automation är hög oberoende av vilken tid som ska utnyttjas men på grund av olika anledning. Om tiden då operatören inte aktivt jobbar utnyttjas, måste den automatiserade lösningen fungera autonomt under längre tid. Om den istället förväntas fungera parallellt med operatören måste den anpassas smart så att den inte stör operatörens arbete och flöde. Rekommendationen är att fokusera på att hitta konkreta investeringsalternativ som utnyttjar tiden då operatören inte aktivt arbetar för att få bättre kostnadsunderlag att ha med i den vidare analysen.