Hybrid Machine Learning/Simulation Approaches for Logistic Systems Optimization

Hoje em dia, tem-se testemunhado um abrupto crescimento e desenvolvimento da indústria, refletido no elevado grau de complexidade e inteligência que os sistemas de produção correntes apresentam, onde se destacam os sistemas logísticos. Esta incessante procura pela inovação e melhoramento contínuo são muito recorrentes na época atual, traduzindo-se em constantes transformações no conceito da qualidade de um produto. Deste modo, emerge a necessidade em otimizar os layouts fabris conduzindo a um aumento da flexibilidade face aos seus comportamentos dinâmicos. Neste seguimento surge a imprescindibilidade de aprimoramento do comportamento do veículo autónomo associado, com vista a finalidades comuns como o aumento da produtividade e minimização de custos e lead times. Neste âmbito, esta dissertação, para além da implementação do modelo de simulação do sistema logístico, desenvolve numa fase inicial comportamentos elementares a aplicar ao veículo, implementadas no próprio ambiente de simulação. Posteriormente, dado que a área de Machine Learning tem obtido tanto sucesso noutras áreas tecnológicas, surgiu o desafio da introdução do conceito de rede neuronal, através da criação de uma nova entidade designada Agente e caraterizada pela técnica de aprendizagem baseada em Reinforcement Learning. Por fim, nesta dissertação, para além de se concluir que a abordagem baseada em Reinforcement Learning proporcionou os melhores resultados de produtividade, retiraram-se ainda conclusões no que à robustez destes modelos diz respeito, a fim de avaliar a sua flexibilidade quando sujeitos a diferentes contextos, simulando um ambiente real.Nowadays, we have been witnessing an abrupt growth and development of the industry, reflected in the high level of complexity and intelligence that the current production systems present, in which the logistics systems stand out. This incessant search for innovation and continuous improvement are very common today, reproducing into constant changes in the product quality concept. In this sense, the need to optimize the factory layouts emerges, leading to an increase in flexibility because of their dynamic behaviours. In this segment, there is an essential need to improve the behaviour of the associated autonomous vehicle, to reach common objectives such as increasing the productivity and minimizing costs and lead times. In this context, this dissertation, beyond the implementation of the simulation model of the logistics system, develops, in an initial phase, elementary behaviours to be applied to the vehicle, implemented in the simulation environment itself. Subsequently, given that the Machine Learning area has been so successful in other technological areas, the challenge of introducing the concept of the neural network appears, through the creation of a new entity called Agent and characterized by the Reinforcement Learning technique. Finally, in this dissertation, in addition to concluding that the Reinforcement Learning-based approach provided the best productivity results, conclusions were also drawn regarding the robustness of these models, in order to assess their flexibility when subject to different contexts, simulating a real environment

Improving productivity of road surfacing operations using value stream mapping and discrete event simulation

The Highways Infrastructure is one of the most valuable asset owned by the public sector. Efficient operations of Highways have the success of national and local economies as well as the quality of life of the general public, dependent on it. Ensuring smooth traffic operations requires maintenance and improvements of the highest standard. This research investigates integration of Discrete Event Simulation (DES) and Value Stream mapping (VSM) to enhance the productivity of the delivery of road surfacing operations by achieving higher production rates and minimum road closure times. Research approach involved use of primary data, collected from direct observation, interviews, review of archival records and productivity databases. Based on this, process maps and value stream maps were developed, which were subsequently used to produce discrete event simulation models, for exploration of different optimisation scenarios

Discrete event simulation and lean production: quantification of waste in a pharmaceutical industry

Nowadays it is imperative that companies seek constant improvements in their operational performance so as not to become obsolete in relation to the new cutting edge trends of smart manufacturing or industry 4.0. In this context, it is noteworthy that manufacturing must occur in the presence of variability and uncertainty, and manufacturing systems must be complex, efficient and lean. Therefore, a conduct aimed at interventions focused on reducing waste in manufacturing and service operations are essential actions. A tool that can help in this purpose is the discrete event simulation (DES). In this context, the objective of this research is to apply DES and quantify the financial waste arising from non-value-adding activities. The object of study was a production line of a pharmaceutical industry and as a research method an approach was used combining modeling and simulation (quantitative) and case study (qualitative) methods. The software chosen was Flexsim®, a powerful simulation and process analysis tool that helps professionals in decision making. Finally, the results obtained through this research show the great financial waste in the analyzed assembly line. This impactful result on losses in the operation serves as a warning so that intervention measures are planned and executed to eliminate or mitigate the consequences of this waste

Simulation Modelling and Analysis of Impact of 3D Feedback Workflow on Prefabrication of Industrial Construction

The construction industry has not been experiencing the same level of productivity increase as the manufacturing industry, due to their divergent production methods. While traditional construction projects are unique, craft-based, and typically done on-site, manufacturing is able to mass produce standardized products on assembly lines in a controlled environment. Efforts to improve construction productivity take advantage of the more established and mature manufacturing processes and techniques, such as modularization and off-site assembly. As civil industry work requirements become more demanding, and modular component tolerance continues to decrease for more complex projects, there exists a need to incorporate and utilize quality control technologies similar to what have been used in the manufacturing and automotive industries for years. Rework of items that failed quality checks leads to significant waste of resources, resulting in reduced overall productivity represented by additional time and manpower spent on correcting the errors. The solution set to this problem ultimately needs to address lost productivity due to rework, and generate value from its operation in the industrial fabrication workflow. The use of 3D data acquisition and 3D feedback is proposed to be part of the quality control process of pipe spool fabrication, which takes place during fitting and before shipment to site. The existing prevailing workflow and the proposed workflow using the new technology are assessed using discrete-event simulation, and three implementation scenarios are investigated, which are: (1) nuclear projects, (2) small bore non-nuclear projects, and (3) large bore non-nuclear projects. They represent different quality control processes for their particular requirements, as well as their specific activity process times given the nature of their assemblies. The analysis of the simulation results show that the revised workflow improved performance for all three project types, specifically in rework reduction and overall fabrication time reduction. Risk assessment was also carried out, in order to quantify the risk mitigation and accrued benefits by implementing the revised fabrication workflow for pipe spool assembly. The difference in risk was considered as a project benefit under economic analysis, and it was found that the relatively short payback period for the fabricator justifies the initial technology investment required to set up the platform for 3D feedback in the revised workflows

Productivity and flexibility improvement of assembly lines for high-mix low-volume production. A white goods industry case

Las tendencias globales de la personalización e individualización en masa impulsan la producción industrial en serie corta y variada; y por tanto una gran variedad de productos en pequeñas cantidades. Por ello, la customización en masa precisa de sistemas de ensamblaje que sean a la vez altamente productivos y flexibles, a diferencia de la tradicional oposición entre ambas características. La llamada cuarta revolución industrial trae diversas tecnologías habilitadoras que podrían ser útiles para abordar este problema. Sin embargo, las metodologías para implementar el ensamblaje 4.0 todavía no han sido resueltas. De hecho, para aprovechar todas las ventajas potenciales de la Industria 4.0, es necesario contar con un nivel previo de excelencia operacional y un análisis holístico de los sistemas productivos. Esta tesis tiene como objetivo entender y definir cómo mejorar la productividad y la flexibilidad de las operaciones de montaje en serie corta y variada.Esta meta se ha dividido en tres objetivos. El primer objetivo consiste en comprender las relaciones entre la Industria 4.0 y las operaciones de ensamblaje, así como sus implicaciones para los operarios. El segundo objetivo consiste en desarrollar una metodología y las herramientas necesarias para evaluar el rendimiento de diferentes configuraciones de cadenas de ensamblaje. El último objetivo consiste en el diseño de sistemas de ensamblaje que permitan incrementar su productividad al menos un 25 %, produciendo en serie corta y variada, mediante la combinación de puestos de montaje manual y estaciones automatizadas.Para abordar la fase de comprensión y definición del problema, se llevó a cabo una revisión bibliográfica sistemática y se desarrolló un marco conceptual para el Ensamblaje 4.0. Se desarrollaron, verificaron y validaron dos herramientas de evaluación del rendimiento: un modelo matemático analítico y varios modelos de simulación por eventos discretos. Para la verificación, y como punto de partida para los análisis, se ha utilizado un caso de estudio industrial de un fabricante global de electrodomésticos. Se han empleado múltiples escenarios de simulación y técnicas de diseño de experimentos para investigar tres cuestiones clave.En primer lugar, se identificaron los factores más críticos para el rendimiento de líneas de montaje manuales multi-modelo. En segundo lugar, se analizó el rendimiento de líneas de montaje semiautomáticas paralelas con operarios móviles en comparación con líneas semiautomáticas o manuales con operarios fijos, empleando diversos escenarios de demanda en serie corta y variada. Por último, se investigó el uso de trenes milkrun para la logística interna de líneas de ensamblaje multi-modelo bajo la influencia de perturbaciones.Los resultados de las simulaciones muestran que las líneas paralelas con operarios móviles pueden superar a las de operarios fijos en cualquier escenario de demanda, alcanzando como mínimo el objetivo de mejorar la productividad en un 25% o más. También permiten reducir cómodamente el número de operarios trabajando en la línea sin afectar negativamente al equilibrado de la misma, posibilitando la producción eficiente de bajo volumen. Los resultados de las simulaciones de logística interna indican que los milkrun pueden proteger las líneas de ensamblaje de las perturbaciones originadas en procesos aguas arriba.Futuras líneas de investigación en base a los resultados obtenidos en esta tesis podrían incluir la expansión e integración de los modelos de simulación actuales para analizar las cadenas de montaje paralelas con operarios móviles incorporando logística, averías y mantenimiento, problemas de control de calidad y políticas de gestión de los retrabajos. Otra línea podría ser el uso de diferentes herramienta para el análisis del desempeño como, por ejemplo, técnicas de programación de la producción que permitan evaluar el desempeño operacional de diferentes configuraciones de cadenas de montaje con operarios móviles, tanto en términos de automatización como de organización en planta. Podrían incorporarse tecnologías de la Industria 4.0 a los modelos de simulación para evaluar su impacto operacional global ¿como cobots para ensamblaje o para la manipulación de materiales, realidad aumentada para el apoyo cognitivo a los operarios, o AGVs para la conducciónde los trenes milkrun. Por último, el trabajo presentado en esta tesis acerca las líneas de ensamblaje semiautomáticas con operarios móviles a su implementación industrial.<br /

Simulation Modeling and Analysis of Adjustable Service-Rate Queueing Models that Incorporate Feedback Control

Research shows that in a system model, when the production rate is adjusted based on the number of items in queue, the nature of the model changes from an open-loop queueing system to a closed-loop feedback control system. Service-rate adjustment can be implemented in a discrete event simulation model, but the effect of this adjustment has not been thoroughly analyzed in the literature. This research considers the design of feedback signals to generate realistic simulation models of production system behavior. A series of simulation experiments is conducted to provide practical guidance for simulation modelers on how adding a service-rate adjustment feedback loop to a queueing system affects system performance