Made available in DSpace on 2015-05-14T12:36:29Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2138461 bytes, checksum: b91d7f14f64fd9d32fd38aee2b19b970 (MD5) Previous issue date: 2010-07-16 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This work approaches the problem of the tasks scheduling (Job Shop Scheduling) through the combination of the GRASP and Tabu Search metaheuristics. The study consists of using the GRASP (Greedy Randomized Adaptive Search Procedure) in the construction phase of the initial solution, suggesting for that, a specific procedure based on Coffman Gramah algorithm. However, such procedure was the great differential in this study, since it offers an initial solution qualitatively better, what allows the reduction of the local search time, where it was utilized the Tabu Search metaheuristic in the local search phase, which provided best results when it was compared with other metaheuristics that have the same structural base. The combination GRASP and Tabu Search were evaluated under two differentiated implementations, being one with Tabu Search in its form more simplified and another one with Tabu Search developing a process of optimized search, using an aditional mathematical model, which demonstrated great advancements in relation at processing time and the obtained results. The computational results, when compared with the existing ones in literature, they had shown that GRASP and Tabu Search combination are capable to produce good solutions. / Este trabalho aborda o problema de escalonamento de tarefas (Job Shop Scheduling) através da combinação das metaheurísticas GRASP e Busca Tabu. O estudo consiste em utilizar o GRASP na fase de construção da solução inicial, sugerindo, para tanto, um procedimento específico baseado no algoritmo de Coffman Gramah. Tal procedimento foi o grande diferencial deste trabalho, visto que oferece uma solução inicial qualitativamente superior, permitindo a redução do tempo de busca local, onde foi utilizada a metaheurística Busca Tabu, a qual demonstrou proporcionar melhores resultados, comparando-se com outras metaheurísticas que seguem a mesma base estrutural. A combinação GRASP e Busca Tabu foi avaliada sob duas implementações diferenciadas, sendo uma com a Busca Tabu em sua forma mais simplificada e outra com a Busca Tabu desenvolvendo um processo de busca otimizada com o auxílio de um modelo matemático , a qual demonstrou grandes progressos quanto ao tempo de processamento e a obtenção de resultados. Os resultados computacionais obtidos, quando comparados com os existentes na literatura, mostraram que a combinação GRASP e Busca Tabu é capaz de produzir boas soluções.

Esta dissertação estuda o problema da programação de operações em sistemas job shop de manufatura onde itens com estruturas de materiais são produzidos a partir de componentes fabricados e montados. Tais sistemas são denominados assembly shops. O caso geral do problema de programação de operações em sistemas job shop, no qual não existem restrições quanto ao número de operações a serem programadas nem quanto ao número de máquinas a serem alocadas, é considerado, até o presente momento, intratável do ponto de vista computacional devido à explosão combinatória inerente ao processo de programação, independente da escolha do critério de desempenho. Isto significa dizer que não existe nenhum método eficiente de programação que resolva globalmente instâncias de porte real do problema dentro de um tempo computacional considerado satisfatório. Devido a este fato, nas últimas três décadas, diversos métodos aproximados e heurísticos foram propostos e avaliados para o problema. Nesta pesquisa, é proposto e avaliado um novo método heurístico de programação. Fundamentado na pressuposição de que a melhoria na sincronização de operações de montagem em sistemas assembly shop leva ao melhor atendimento de datas de entrega de pedidos, o método implementa duas abordagens de programação: uma abordagem backward que satisfaz completamente as datas de entrega e outra forward que satisfaz completamente a restrição de capacidade de máquina. Ambas trabalham iterativamente dentro de dois modelos de simulação do sistema de produção um determinístico e outro probabilístico na busca pela melhoria da sincronização das operações e no atendimento das datas de entrega. Os resultados experimentais demonstraram que o desempenho do novo método foi em média melhor que os dos métodos não iterativos (regras) avaliados e tão bom quanto o desempenho do melhor método não iterativo (regra) testado. / This dissertation studies the problem of scheduling operations in manufacturing job shop environments where items with bill of materials are made of many fabricated and assembled components. Such systems are known as assembly shops. The general job shop scheduling problem, which no restrictions exist neither for the number of operations to be scheduled nor for the number of machines to be allocated, is considered at the present date intractable from the computational point of view, whatever the performance criterion used, due to the combinatorial explosion inherent to the scheduling process. It means that there is not an efficient computational method that solves globally real size instances of the problem within a satisfactory period of time. Due to this fact, in the last three decades several approximated and heuristic methods were created and evaluated for the problem. This research proposes and evaluate a new heuristic method which is based on the assumption that the improvement in operations synchronization at the assembly stations brings forth better achievement of due dates. The method implements two scheduling approaches: a backward approach satisfying due date completely and a forward approach satisfying capacity restriction completely. The two approaches work iteratively within two different simulation models of the production system one deterministic e other probabilistic in searching for operations synchronization improvement and due date achievement. The experimental results have shown the new method was better than the single-pass methods (rules) on average and as good as the better single-pass method (rule) tested.

In this work, we investigate job-shop problems where limited capacity buffers to store jobs in non-processing periods are present. In such a problem setting, after finishing processing on a machine, a job either directly has to be processed on the following machine or it has to be stored in a prespecified buffer. If the buffer is completely occupied the job may wait on its current machine but blocks this machine for other jobs. Besides a general buffer model,also specific configurations are considered.The key issue to develop fast heuristics for the job-shop problem with buffers is to find a compact representation of solutions. In contrast to the classical job-shop problem,where a solution may be given by the sequences of the jobs on the machines, now also the buffers have to be incorporated in the solution representation. In this work, we propose two solution representations for the job-shop problem with buffers. Furthermore, we investigate whether the given solution representations can be simplified for specific buffer configurations. For the general buffer configuration it is shown that an incorporation of the buffers in the solution representation is necessary, whereas for specific buffer configurations possible simplifications are presented. Based on the given solution representations we develop local search heuristics in the second part of this work. Therefore, the well-known block approach for the classical job-shop problem is generalized to the job-shop problem with specific buffer configurations.

No description available.

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.

Made available in DSpace on 2015-03-05T13:58:26Z (GMT). No. of bitstreams: 0 Previous issue date: 1 / Universidade do Vale do Rio dos Sinos / Neste trabalho é abordado o Problema de Escalonamento em um job shop, considerando restrições de datas de entrega, turnos de produção e tempo de setup entre operações. Considera-se um ambiente de Sistema de Manufatura flexível, que dado ao alto nível de automação, permite a previsibilidade dos processos de carregamento dos recursos à área de processamento. O problema foi modelado através de uma Função Objetivo fn composta de três variáveis de decisão. A importância da contribuição de cada variável para o valor de fn é gerida pela atribuição de valores aos pesos associados às variáveis. Na abordagem proposta, são utilizadas técnicas de Tecnologia de Grupo e Busca Tabu. O modelo implementado é uma modificação da técnica i TSAB, proposta por Nowicki e Smutnicki, a qual apresenta bons resultados no tratamento do Problema de Escalonamento em um job shop PEJS clássico. A consideração das restrições adicionais ao PEJS aumenta a complexidade do modelo implementado, porém, deixa o problema mais próximo da realidade. / In this work the Job Shop Scheduling Problem is studied, considering due dates, production turns and tooling constraints. This problem is applied in a Flexible Manufacturing System, which possesses high degree of automation, allowing previsibility in the processes of loading and unloading jobs on the machines. The problem is modeled through a objective function fn composed by three weighted decision variables. The importance of each variable in the fn final value is managed through assignment of values to the weights of these variables. In the proposed approach, it was used Group Technology and Tabu Search techniques. The implemented model is a modification of the i TSAB technique, proposed by Nowicki and Smutniki. The consideration of adicional constraints in the Job Shop Scheduling Problem increases the complexity of the implementation, otherwise, makes the problem closer to the industrial reality. The model was validated using benchmark instances, in which the data from the addional constraints were added.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-06-16T12:13:46Z No. of bitstreams: 2 Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) / Made available in DSpace on 2017-06-16T12:13:47Z (GMT). No. of bitstreams: 2 Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) Previous issue date: 2017-03-23 / CNPQ – Conselho Nacional de Desenvolvimento Científico e Tecnológico / A contribuição de metaheurísticas, em especial a dos algoritmos evolutivos, na área de otimização combinatória é de extrema relevância, pois auxiliam na busca de soluções próximas ao ótimo para problemas complexos da vida real cuja resolução em tempo aceitável é inviável devido a sua complexidade computacional, oferecendo uma flexibilidade importante na modelagem do problema. Este trabalho se propõe a apresentar e implementar um modelo computacional a ser utilizado na otimização do escalonamento de ordens de produção utilizando um Algoritmo Memético (AM), que permite a busca tanto da melhor sequência das ordens de produção quanto dos lotes de tamanho variável em que a quantidade de cada operação pode ser subdividida. A possibilidade de utilização de máquinas alternativas, de recursos secundários, de intervalos de indisponibilidade e de lotes de transferência, é apresentada no modelo, o que lhe proporciona grande robustez e aplicabilidade em ambientes de manufatura flexível, permitindo uma modelagem do Flexible Job Shop Scheduling Problem (FJSSP) que reflete com maior fidedignidade a realidade do ambiente fabril, gerando como resultado um escalonamento otimizado e aderente às necessidades da fábrica. Várias instâncias do FJSSP são utilizadas nos testes e os resultados obtidos comprovam que o algoritmo proposto consegue otimizar o escalonamento das ordens de produção de cada instância de maneira eficiente. / The contribution of meta-heuristics, especially evolutionary algorithms, in combinatorial optimization area is extremely important, as they help in finding near optimal solutions to complex real-life problems whose resolution is infeasible in acceptable time due to its computational complexity, offering an important flexibility in the modeling of problem. This study propose to present and implement a computational model to be used in optimizing the production scheduling of manufacturing orders using a Memetic Algorithm that allows to search both the best sequence of jobs as of variable size batches that the quantity of each operation can be subdivided. The possibility of using alternative resources, operations with secondary resources, unavailability intervals and batch transfer lots are features presented in the model, which lends it great robustness and applicability to flexible manufacturing environments, allowing the modeling of Flexible Job Shop Scheduling Problem (FJSSP) that reflects with higher accuracy the real manufacturing environment, generating optimized scheduling results that are adhering to the plant needs. Multiple instances of FJSSP are used in the tests and the results show that the proposed algorithm succeeds in optimizing the scheduling of production orders for each instance so efficient.

Pour la résolution de problèmes d'ordonnancement d'ateliers de type flow-shop en industries pharmaceutiques et d'ateliers de type job-shop flexible, deux méthodes d'optimisation ont été développées : une méthode utilisant les algorithmes génétiques dotés d'un nouveau codage proposé et une méthode d'optimisation par essaim particulaire modifiée pour être exploitée dans le cas discret. Les critères retenus dans le cas de lignes de conditionnement considérées sont la minimisation des coûts de production ainsi que des coûts de non utilisation des machines pour les problèmes multi-objectifs relatifs aux industries pharmaceutiques et la minimisation du Makespan pour les problèmes mono-objectif des ateliers job-shop flexibles.Ces méthodes ont été appliquées à divers exemples d'ateliers de complexités distinctes pour illustrer leur mise en œuvre. L'étude comparative des résultats ainsi obtenus a montré que la méthode basée sur l'optimisation par essaim particulaire est plus efficace que celle des algorithmes génétiques, en termes de rapidité de la convergence et de l'approche de la solution optimale

Cette thèse -spécialisée en Recherche Opérationnelle- traite de l'intégration, dans le processus décisionnel industriel, de deux facteurs-clés: la planification des ressources humaines et l'ordonnancement de la production. Un premier cas de ce genre de problématiques est tout d'abord étudié. Deux bornes inférieures obtenues par relaxation lagrangienne et deux méthodes de résolution exacte par décomposition et génération de coupes sont présentées. Si la première approche relève d'une technique connue de la littérature (décomposition de Benders), la seconde se veut plus spécifique. Une technique de génération de coupes énergétiques valides, applicable en préprocess de toute méthode de résolution, est également proposée. La seconde partie traite d'un autre cas particulier, déjà évoqué dans la littérature, de la problématique générale. Ces travaux prolongent ceux effectués lors de la première étude dans le sens où le problème traité est intrinsèquement plus complexe et le but avoué est d'expérimenter les techniques de décomposition et génération de coupes, a priori efficaces, sur une autre problématique. Une technique de génération d'inégalités valides, applicable elle aussi en préprocess de toute méthode de résolution, est tout d'abord mise en place. Deux méthodes de résolution exacte sont ensuite développées. La première est analogue à la technique spécifique de décomposition décrite auparavant. La seconde, plus novatrice, exploite la décomposition intuitive de la problématique et la génération de coupes dédiées dans un cadre où les solutions à valider sont construites via une approche arborescente de type Procédure de Séparation et Evaluation Séquentielle.

This Master’s thesis has been carried out within the subject area of Production Development and Management and aims to reduce internal lead times in make-to-order (MTO) and job-shop production environments with the use of identified theoretical methods. The reason this particular production environment was chosen was the flexibility and satisfaction it provides its customers. Today, customers expect customised products, a situation which causes problems for manufacturers as they are unable to produce such products in large amounts. In order to investigate problems with these kinds of environments and the causes for long lead times, we have conducted a literature study where we identified the problems these particular kinds of production environments experience regarding production planning and control which are related to the immense amount of time consumed by changeovers because of high demand variance and high requirements for customisation. To affirm the theoretical findings, we opted to undertake a case study and chose Talent Plastics Gislaved AB as our case, because this company utilises an MTO and job-shop production environment for its production of highly customised products with high demand variance. In the analysis of our case, we found that the wastes in the organisation were similar to those identified in the theoretical findings. The current planning system and the current state of the manufacturing lead time system were evaluated and a theoretical framework using a combination of lean production, work load control and constant work-in-process theories was suggested. We claim that concentrating on the reduction of setup times can lead the job-shop towards drastically decreased lead times and a much more effective use of time throughout the organisation. Because the organisation will continue to face problems due to the ever-increasing demand variance and requirements for customisation, there are plenty of opportunities for further research in these kinds of production environments. Emerging theories, such as quick response manufacturing, may also be tested to construct an efficient framework.