An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan

This paper deals with a variant of flowshop scheduling, namely, the hybrid or flexible flowshop with sequence dependent setup times. This type of flowshop is frequently used in the batch production industry and helps reduce the gap between research and operational use. This scheduling problem is NP-hard and solutions for large problems are based on non-exact methods. An improved genetic algorithm (GA) based on software agent design to minimise the makespan is presented. The paper proposes using an inherent characteristic of software agents to create a new perspective in GA design. To verify the developed metaheuristic, computational experiments are conducted on a well-known benchmark problem dataset. The experimental results show that the proposed metaheuristic outperforms some of the well-known methods and the state-of-art algorithms on the same benchmark problem dataset.The translation of this paper was funded by Universidad Politecnica de Valencia, Spain.Gómez Gasquet, P.; Andrés Romano, C.; Lario Esteban, FC. (2012). An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan. Expert Systems with Applications. 39(9):8095-8107. https://doi.org/10.1016/j.eswa.2012.01.158S8095810739

Evolving Neural Networks to Solve a Two-Stage Hybrid Flow Shop Scheduling Problem with Family Setup Times

We present a novel strategy to solve a two-stage hybrid flow shop scheduling problem with family setup times. The problem is derived from an industrial case. Our strategy involves the application of NeuroEvolution of Augmenting Topologies - a genetic algorithm, which generates arbitrary neural networks being able to estimate job sequences. The algorithm is coupled with a discrete-event simulation model, which evaluates different network configurations and provides training signals. We compare the performance and computational efficiency of the proposed concept with other solution approaches. Our investigations indicate that NeuroEvolution of Augmenting Topologies can possibly compete with state-of-the-art approaches in terms of solution quality and outperform them in terms of computational efficiency

Intelligent Product Agents for Multi-Agent Control of Manufacturing Systems

The current manufacturing paradigm is shifting toward more flexible manufacturing systems that produce highly personalized products, adapt to unexpected disturbances in the system, and readily integrate new manufacturing system technology. However, to achieve this type of flexibility, new system-level control strategies must be developed, tested, and integrated to coordinate the components on the shop floor. One strategy that has been previously proposed to coordinate the resources and parts in a manufacturing system is multi-agent control. The manufacturing multi-agent control strategy consists of agents that interface with the various components on the shop floor and continuously interact with each other to drive the behavior of the manufacturing system. Two of the most important decision-making agents for this type of control strategy are product agents and resource agents. A product agent represents a single product and a resource agent represents a single resource on the plant floor. The objective of a product agent is to make decisions for an individual product and request operations from the resource agents based on manufacturer and customer specifications. A resource agent is the high-level controller for a resource on the shop floor (e.g., machines, material-handling robots, etc.). A resource agent communicates with other product and resource agents in the system, fulfills product agent requests, and interfaces with the associated resource on the plant floor. While both product agents and resource agents are important to ensure effective performance of the manufacturing system, the work presented in this dissertation improves the intelligence and capabilities of product agents by providing a standardized product agent architecture, models to capture the dynamics and constraints of the manufacturing environment, and methods to make improved decisions in a dynamic system. New methods to explore the manufacturing system and cooperate with other agents in the system are provided. The proposed architecture, models, and methods are tested in a simulated manufacturing environment and in several manufacturing testbeds with physical components. The results of these experiments showcase the improved flexibility and adaptability of this approach. In these experiments, the model-based product agent effectively makes decisions to meet its production requirements, while responding to unexpected disturbances in the system, such as machine failures or new customer orders. The model-based product agent proposed in this dissertation pushes the fields of manufacturing and system-level control closer to realizing the goals of increased personalized production and improved manufacturing system flexibility.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162893/1/ikoval_1.pd

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Collaborative planning in non-hierarchical networks - an intelligent negotiation-based framework

In today’s competing business market, companies are constantly challenged to dynamically adapt to customer expectations by diminishing the time response that goes from the beginning of the business opportunity to the satisfaction of the customer need. Simultaneously, there is increased recognition of the advantages that companies obtain in focusing on their core business and seeking other competencies through partnerships with other partners by forming collaborative networks. These new collaborative organizational structures require a new set of methods and tools to support the management of manufacturing processes across the entire supply chain. The present paper addresses the collaborative production planning problem in networks of non-hierarchical, decentralized, and independent companies. By proposing a collaborative planning intelligent framework composed of a web-based set of methods, tools, and technologies, the present study intends to provide network stakeholders with the necessary means to responsively and efficiently address each one of the market business opportunities. Through this new holistic framework, the managers of the networked companies can address the challenges posed during collaborative network formation and supply chain production planning.The research leading to these results received funding from the European Union’s Seventh Framework Program (FP7/2007-2013) under grant agreement No. 260169. This work was also financed by national funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020

An Industrial Data Analysis and Supervision Framework for Predictive Manufacturing Systems

Due to the advancements in the Information and Communication Technologies field in the modern interconnected world, the manufacturing industry is becoming a more and more data rich environment, with large volumes of data being generated on a daily basis, thus presenting a new set of opportunities to be explored towards improving the efficiency and quality of production processes. This can be done through the development of the so called Predictive Manufacturing Systems. These systems aim to improve manufacturing processes through a combination of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time Data Analytics in order to predict future states and events in production. This can be used in a wide array of applications, including predictive maintenance policies, improving quality control through the early detection of faults and defects or optimize energy consumption, to name a few. Therefore, the research efforts presented in this document focus on the design and development of a generic framework to guide the implementation of predictive manufacturing systems through a set of common requirements and components. This approach aims to enable manufacturers to extract, analyse, interpret and transform their data into actionable knowledge that can be leveraged into a business advantage. To this end a list of goals, functional and non-functional requirements is defined for these systems based on a thorough literature review and empirical knowledge. Subsequently the Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with a detailed description of each of its main components. Finally, a pilot implementation is presented for each of this components, followed by the demonstration of the proposed framework in three different scenarios including several use cases in varied real-world industrial areas. In this way the proposed work aims to provide a common foundation for the full realization of Predictive Manufacturing Systems

Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold: Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction

Metodología dirigida por modelos para las pruebas de un sistema distribuido multiagente de fabricación

Las presiones del mercado han empujado a las empresas de fabricación a reducir costes a la vez que mejoran sus productos, especializándose en las actividades sobre las que pueden añadir valor y colaborando con especialistas de las otras áreas para el resto. Estos sistemas distribuidos de fabricación conllevan nuevos retos, dado que es difícil integrar los distintos sistemas de información y organizarlos de forma coherente. Esto ha llevado a los investigadores a proponer una variedad de abstracciones, arquitecturas y especificaciones que tratan de atacar esta complejidad. Entre ellas, los sistemas de fabricación holónicos han recibido una atención especial: ven las empresas como redes de holones, entidades que a la vez están formados y forman parte de varios otros holones. Hasta ahora, los holones se han implementado para control de fabricación como agentes inteligentes autoconscientes, pero su curva de aprendizaje y las dificultades a la hora de integrarlos con sistemas tradicionales han dificultado su adopción en la industria. Por otro lado, su comportamiento emergente puede que no sea deseable si se necesita que las tareas cumplan ciertas garantías, como ocurren en las relaciones de negocio a negocio o de negocio a cliente y en las operaciones de alto nivel de gestión de planta. Esta tesis propone una visión más flexible del concepto de holón, permitiendo que se sitúe en un espectro más amplio de niveles de inteligencia, y defiende que sea mejor implementar los holones de negocio como servicios, componentes software que pueden ser reutilizados a través de tecnologías estándar desde cualquier parte de la organización. Estos servicios suelen organizarse como catálogos coherentes, conocidos como Arquitecturas Orientadas a Servicios (‘Service Oriented Architectures’ o SOA). Una iniciativa SOA exitosa puede reportar importantes beneficios, pero no es una tarea trivial. Por este motivo, se han propuesto muchas metodologías SOA en la literatura, pero ninguna de ellas cubre explícitamente la necesidad de probar los servicios. Considerando que la meta de las SOA es incrementar la reutilización del software en la organización, es una carencia importante: tener servicios de alta calidad es crucial para una SOA exitosa. Por este motivo, el objetivo principal de la presente Tesis es definir una metodología extendida que ayude a los usuarios a probar los servicios que implementan a sus holones de negocio. Tras considerar las opciones disponibles, se tomó la metodología dirigida por modelos SODM como punto de partida y se reescribió en su mayor parte con el framework Epsilon de código abierto, permitiendo a los usuarios que modelen su conocimiento parcial sobre el rendimiento esperado de los servicios. Este conocimiento parcial es aprovechado por varios nuevos algoritmos de inferencia de requisitos de rendimiento, que extraen los requisitos específicos de cada servicio. Aunque el algoritmo de inferencia de peticiones por segundo es sencillo, el algoritmo de inferencia de tiempos límite pasó por numerosas revisiones hasta obtener el nivel deseado de funcionalidad y rendimiento. Tras una primera formulación basada en programación lineal, se reemplazó con un algoritmo sencillo ad-hoc que recorría el grafo y después con un algoritmo incremental mucho más rápido y avanzado. El algoritmo incremental produce resultados equivalentes y tarda mucho menos, incluso con modelos grandes. Para sacar más partidos de los modelos, esta Tesis también propone un enfoque general para generar artefactos de prueba para múltiples tecnologías a partir de los modelos anotados por los algoritmos. Para evaluar la viabilidad de este enfoque, se implementó para dos posibles usos: reutilizar pruebas unitarias escritas en Java como pruebas de rendimiento, y generar proyectos completos de prueba de rendimiento usando el framework The Grinder para cualquier Servicio Web que esté descrito usando el estándar Web Services Description Language. La metodología completa es finalmente aplicada con éxito a un caso de estudio basado en un área de fabricación de losas cerámicas rectificadas de un grupo de empresas español. En este caso de estudio se parte de una descripción de alto nivel del negocio y se termina con la implementación de parte de uno de los holones y la generación de pruebas de rendimiento para uno de sus Servicios Web. Con su soporte para tanto diseñar como implementar pruebas de rendimiento de los servicios, se puede concluir que SODM+T ayuda a que los usuarios tengan una mayor confianza en sus implementaciones de los holones de negocio observados en sus empresas

Réduction du comportement myope dans le contrôle des FMS : une approche semi-hétérarchique basée sur la simulation-optimisation

Heterarchical-based control for flexible manufacturing systems (FMS) localizes control capabilities in decisional entities (DE), resulting in highly reactive and low complex control architectures. However, these architectures present myopic behavior since DEs have limited visibility of other DEs and their behavior, making difficult to ensure certain global performance. This dissertation focuses on reducing myopic behavior. At first, a definition and a typology of myopic behavior in FMS is proposed. In this thesis, myopic behavior is dealt explicitly so global performance can be improved. Thus, we propose a semi-heterarchical architecture in which a global decisional entity (GDE) deals with different kinds of myopic decisions using simulation-based optimization (SbOs). Different optimization techniques can be used so myopic decisions can be dealt individually, favoring GDE modularity. Then, the SbOs can adopt different roles, being possible to reduce myopic behavior in different ways. More, it is also possible to grant local decisional entities with different autonomy levels by applying different interaction modes. In order to balance reactivity and global performance, our approach accepts configurations in which some myopic behaviors are reduced and others are accepted. Our approach was instantiated to control the assembly cell at Valenciennes AIPPRIMECA center. Simulation results showed that the proposed architecture reduces myopic behavior whereby it strikes a balance between reactivity and global performance. The real implementation on the assembly cell verified the effectiveness of our approach under realistic dynamic scenarios, and promising results were obtained.Le contrôle hétérarchique des systèmes de production flexibles (FMS) préconise un contrôle peu complexe et hautement réactif supporté par des entités décisionnelles locales (DEs). En dépit d'avancées prometteuses, ces architectures présentent un comportement myope car les DEs ont une visibilité informationnelle limitée sue les autres DEs, ce qui rend difficile la garantie d'une performance globale minimum. Cette thèse se concentre sur les approches permettant de réduire cette myopie. D'abord, une définition et une typologie de cette myopie dans les FMS sont proposées. Ensuite, nous proposons de traiter explicitement le comportement myope avec une architecture semi-hétérarchique. Dans celle-ci, une entité décisionnelle globale (GDE) traite différents types de décisions myopes à l'aide des différentes techniques d'optimisation basée sur la simulation (SbO). De plus, les SbO peuvent adopter plusieurs rôles, permettant de réduire le comportement myope de plusieurs façons. Il est également possible d'avoir plusieurs niveaux d'autonomie en appliquant différents modes d'interaction. Ainsi, notre approche accepte des configurations dans lesquelles certains comportements myopes sont réduits et d'autres sont acceptés. Notre approche a été instanciée pour contrôler la cellule flexible AIP- PRIMECA de l'Université de Valenciennes. Les résultats des simulations ont montré que l'architecture proposée peut réduire les comportements myopes en établissant un équilibre entre la réactivité et la performance globale. Des expérimentations réelles ont été réalisées sur la cellule AIP-PRIMECA pour des scenarios dynamiques et des résultats prometteurs ont été obtenus