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.

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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.

Au sein d’un établissement hospitalier, le bloc opératoire représente un des secteurs les plus emblématiques et les plus coûteux. Le fonctionnement du bloc opératoire est orchestré par un programme opératoire qui consiste à construire un planning prévisionnel des interventions chirurgicales à réaliser pendant un horizon donné. La littérature abondante sur le sujet est unanime sur le fait que la construction du programme opératoire est une tâche complexe, car il s’agit non seulement de planifier et d’ordonnancer les interventions, mais aussi de satisfaire des exigences souvent antagonistes. Ce projet est le fruit d’une collaboration entre la Communauté d’Agglomération de Sarreguemines Confluences et la Région Lorraine, des membres du secteur hospitalier (Hôpital Robert Pax de Sarreguemines) et l’équipe Gestion Industrielle et Logistique (GIL) du Laboratoire de Génie Industriel, de Production et de Maintenance (LGIPM). L’objectif de cette recherche est d’apporter une aide aux gestionnaires du bloc opératoire, qui ont besoin de plus en plus des méthodes et des outils d’aide à la décision en vue d’optimiser leur fonctionnement. Pour répondre à ce besoin nous nous intéressons dans la première partie de cette thèse à la gestion des opérations électives en prenant en compte différentes contraintes et en particulier la disponibilité des chirurgiens. Nous nous plaçons dans le contexte d’une stratégie « open scheduling » et nous proposons deux modèles mathématiques permettant d’élaborer le programme opératoire. La complexité des modèles mathématiques et leur explosion combinatoire rendent difficile la recherche de l’optimum pour des tailles réalistes. Ceci nous a donc amené à proposer une heuristique constructive utilisant le modèle proposé et permettant d’obtenir des solutions là où la méthode exacte ne nous le permettait pas. Dans la seconde partie de cette thèse, nous considérons l’intégralité du processus opératoire (brancardage vers le bloc opératoire, préparation et anesthésie, acte chirurgicale et réveil). Nous modélisons ce processus comme un flow shop hybride à 4 étages avec contrainte de blocage de type RSb, et nous le résolvons à l’aide d’un algorithme génétique dont l’objectif est de synchroniser toutes les ressources nécessaires, en respectant au mieux le programme opératoire prévisionnel. Outre les opérations électives, nous nous intéressons dans la dernière partie aux opérations urgentes. Nous proposons un outil d’aide à la décision pour la gestion des opérations urgentes. En prenant en considération la pathologie et la gravité de l’état du patient, nous distinguons principalement 3 degrés d’urgences et proposons pour chacune un algorithme permettant d’intégrer en temps réel ces opérations dans le programme prévisionnel, tout en minimisant différents critères (temps d’attente avant affectation, heures supplémentaires, décalage par rapport aux anciennes dates de débuts) / The operating theater is one of the most critical and expensive hospital resources. Indeed, a high percentage of hospital admissions are due to surgical interventions. Rising expenditures spur health care organizations to organize their processes more efficiently and effectively. This thesis is supported by the urban community of Sarreguemines-France and the region of Lorraine-France, and is carried out in collaboration with the Centre Hospitalier de Sarreguemines - Hôpital Robert Pax. In the first part of this work, we propose two mathematical programming models to help operating theater managers in developing an optimal operating rooms scheduling. We also propose a constructive heuristic to obtain near optimal results for realistic sizes of the problem. In the second part of our work, the whole scheduling process is modeled as a hybrid four-stage flow shop problem with RSb blocking constraint, and is solved by a genetic algorithm. The objective is to synchronize all the needed resources around the optimal daily schedule obtained with the proposed mathematical model. The last part of our work is dedicated to non-elective surgeries. We propose a decision support tool, guiding the operating room manager, to handle this unpredictable flow of patients. Non-elective patients are classified according to their medical priority. The main contribution of the proposed decision support tool is to provide online assignment strategies to treat each non elective patient category. Proposed assignments are riskless on patient’s health. According to non-elective surgery classes, the proposed adjusted schedule minimizes different criteria such as patient’s waiting time, deviation from the firstly scheduled starting time of a surgery and the amount of resulting overtime