This work should serve as a source of information on the issue of production scheduling. The work is particularly focused on the relatively new terms in optimization of processing of production batches area, such as flowshop and its modifications in the form of so-called hybrid flowshop. The work is divided into five chapters. The first chapter consists of an introduction. In the second chapter I mention the theory of scheduling, the third part deals with the history of flowshop and in the fourth part I check the functionality of new models on real data. In conclusion I summarize the contents of work, comment resulting calculations and deliver the practical usage of flowshop.

CoordenaÃÃo de AperfeiÃoamento de NÃvel Superior / Neste trabalho foram tratados dois problemas: o primeiro à denominado Continuous Permutation Flowshop Scheduling Problem (CPFSP), que possui a restriÃÃo de que nenhuma tarefa pode esperar por processamento entre mÃquinas consecutivas; o segundo à denominado de Permutation Flowshop Scheduling Problem (PFSP), em que a restriÃÃo anterior nÃo existe. A metaheurÃstica Algoritmo GenÃtico (AG) tem sido aplicada com sucesso ao PFSP, mas atà o momento nÃo foi encontrado na literatura algo que mostre que o AG à um bom mÃtodo para o CPFSP. O objetivo deste trabalho foi desenvolver um AG eficiente paras esses dois problemas, mas que nÃo precisa utilizar inicializaÃÃo eficiente e/ou hibridizaÃÃo com outra tÃcnica de busca. O desenvolvimento do AG proposto levou em consideraÃÃo as caracterÃsticas, diversificaÃÃo e a intensificaÃÃo, que inspiraram a criaÃÃo de trÃs procedimentos que melhoraram o desempenho do AG proposto. Foram realizados vÃrios experimentos com as instÃncias de Taillard (1993), Reeves (1995) e Heller (1960). Os resultados foram comparados com outros mÃtodos encontrados na literatura. Foram construÃdos polinÃmios com a utilizaÃÃo de InterpolaÃÃo Lagrangeana para determinar o tempo execuÃÃo do AG proposto. Por fim, o mÃtodo foi aplicado num problema real. Os resultados mostraram que o AG proposto à o melhor mÃtodo para o CPFSP e que fica muito prÃximo do melhor AG encontrado na literatura com inicializaÃÃo eficiente para o PFSP

No description available.

Estimation of Distribution Algorithm (EDA) is a branch of evolutionary computation that learn a probabilistic model of good solutions. Probabilistic models are used to represent relationships between solution variables which may give useful, human-understandable insights into real-world problems. Also, developing an effective PM has been shown to significantly reduce function evaluations needed to reach good solutions. This is also useful for real-world problems because their representations are often complex needing more computation to arrive at good solutions. In particular, many real-world problems are naturally represented as permutations and have expensive evaluation functions. EDAs can, however, be computationally expensive when models are too complex. There has therefore been much recent work on developing suitable EDAs for permutation representation. EDAs can now produce state-of-the-art performance on some permutation benchmark problems. However, models are still complex and computationally expensive making them hard to apply to real-world problems. This study investigates some limitations of EDAs in solving permutation and scheduling problems. The focus of this thesis is on addressing redundancies in the Random Key representation, preserving diversity in EDA, simplifying the complexity attributed to the use of multiple local improvement procedures and transferring knowledge from solving a benchmark project scheduling problem to a similar real-world problem. In this thesis, we achieve state-of-the-art performance on the Permutation Flowshop Scheduling Problem benchmarks as well as significantly reducing both the computational effort required to build the probabilistic model and the number of function evaluations. We also achieve competitive results on project scheduling benchmarks. Methods adapted for solving a real-world project scheduling problem presents significant improvements.

In today's competitive environment, the importance of continuous production, quality improvement, and fast delivery has forced production and delivery processes to become highly reliable. Keeping equipment in good condition through maintenance activities can ensure a more reliable system. However, maintenance leads to temporary reduction in capacity that could otherwise be utilized for production. Therefore, the coordination of maintenance and production is important to guarantee good system performance. The central thesis of this dissertation is that integrating maintenance and production decisions increases efficiency by ensuring high quality production, effective resource utilization, and on-time deliveries. Firstly, we study the problem of integrated maintenance and production planning where machines are preventively maintained in the context of a periodic review production system with uncertain yield. Our goal is to provide insight into the optimal maintenance policy, increasing the number of finished products. Specifically, we prove the conditions that guarantee the optimal maintenance policy has a threshold type. Secondly, we address the problem of integrated maintenance planning and production scheduling where machines are correctively maintained in the context of a dynamic aircraft repair shop. To solve the problem, we view the dynamic repair shop as successive static repair scheduling sub-problems over shorter periods. Our results show that the approach that uses logic-based Benders decomposition to solve the static sub-problems, schedules over longer horizon, and quickly adjusts the schedule increases the utilization of aircraft in the long term. Finally, we tackle the problem of integrated maintenance planning and production scheduling where machines are preventively maintained in the context of a multi-machine production system. Depending on the deterioration process of machines, we design decomposed techniques that deal with the stochastic and combinatorial challenges in different, coupled stages. Our results demonstrate that the integrated approaches decrease the total maintenance and lost production cost, maximizing the on-time deliveries. We also prove sufficient conditions that guarantee the monotonicity of the optimal maintenance policy in both machine state and the number of customer orders. Within these three contexts, this dissertation demonstrates that the integrated maintenance and production decision-making increases the process efficiency to produce high quality products in a timely manner.