Simulation and the Fourth Industrial Revolution

Advancements in systems simulation over the past decade have propelled simulationinto a new position as a decision-making tool in Industry 4.0 applications. This paperaddresses the specific benefits of simulation which can be utilized to enable greaterflexibility in decision making in the Industry 4.0 environment. It is stressed thatboth discrete event simulation (DES) and agent-based simulation (ABS) can be usedto represent complex interactions in a fully integrated set of virtual and physicalsystems

Dynamic allocation of operators in a hybrid human-machine 4.0 context

La transformation numérique et le mouvement « industrie 4.0 » reposent sur des concepts tels que l'intégration et l'interconnexion des systèmes utilisant des données en temps réel. Dans le secteur manufacturier, un nouveau paradigme d'allocation dynamique des ressources humaines devient alors possible. Plutôt qu'une allocation statique des opérateurs aux machines, nous proposons d'affecter directement les opérateurs aux différentes tâches qui nécessitent encore une intervention humaine dans une usine majoritairement automatisée. Nous montrons les avantages de ce nouveau paradigme avec des expériences réalisées à l'aide d'un modèle de simulation à événements discrets. Un modèle d'optimisation qui utilise des données industrielles en temps réel et produit une allocation optimale des tâches est également développé. Nous montrons que l'allocation dynamique des ressources humaines est plus performante qu'une allocation statique. L'allocation dynamique permet une augmentation de 30% de la quantité de pièces produites durant une semaine de production. De plus, le modèle d'optimisation utilisé dans le cadre de l'approche d'allocation dynamique mène à des plans de production horaire qui réduisent les retards de production causés par les opérateurs de 76 % par rapport à l'approche d'allocation statique. Le design d'un système pour l'implantation de ce projet de nature 4.0 utilisant des données en temps réel dans le secteur manufacturier est proposé.The Industry 4.0 movement is based on concepts such as the integration and interconnexion of systems using real-time data. In the manufacturing sector, a new dynamic allocation paradigm of human resources then becomes possible. Instead of a static allocation of operators to machines, we propose to allocate the operators directly to the different tasks that still require human intervention in a mostly automated factory. We show the benefits of this new paradigm with experiments performed on a discrete-event simulation model based on an industrial partner's system. An optimization model that uses real-time industrial data and produces an optimal task allocation plan that can be used in real time is also developed. We show that the dynamic allocation of human resources outperforms a static allocation, even with standard operator training levels. With discrete-event simulation, we show that dynamic allocation leads to a 30% increase in the quantity of parts produced. Additionally, the optimization model used under the dynamic allocation approach produces hourly production plans that decrease production delays caused by human operators by up to 76% compared to the static allocation approach. An implementation system for this 4.0 project using real-time data in the manufacturing sector is furthermore proposed

Simulation of an automotive supply chain in Simio: Data model validation

This paper presents a simulation model of the supply chain of a company of the automotive industry. The purpose of this paper is to use the presented model to validate the considered set of variables that we think are relevant to the problem. This approach was important as it allowed to consider a set of variables that could have been ignored if a different approach had been followed. It should be stressed that, due to privacy concerns, real data was not used, but rather random distributions assigned by the modeler. Notwithstanding, by recognizing that, for the data used, the outputs are in accordance to what happens in the real system, the authors concluded that the set of variables can be considered as validated. Yet, it is still necessary to further complement the model with additional available variables that were not included at this stage, due to its complexity, e.g., customer demand variability, uncertainty associated to suppliers' and impact of external events, such as transportation delays.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT –Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, thePortuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operacional Programme for Human Capital (POCH)

A novel approach for planning of shipbuilding processes

Shipbuilding is acknowledged as an uncertain, complex, and unique industrial effort that yields massive products consisting of numerous parts and is vulnerable to unexpected events. The industry is also dominated by customer requirements through designs tailor-made for a specific ship. Planning in shipbuilding is therefore considered a formidable process. Consequently, many studies have been conducted to develop a planning framework for the industry to efficiently handle planning process. Yet none of these studies are deemed substantial enough to be regarded as holistic, straightforward, well-accepted, and compatible with the nature of shipbuilding. This study is therefore an important contribution by presenting a novel, hybrid, and integrated general-purpose planning framework applicable to all shipbuilding processes. The novel method exploits historical ship construction scheduling data, synthesizing hierarchical planning, dynamic scheduling, and discrete-event simulation, which is validated through an empirical study in this paper

Real-time supply chain simulation: a big data-driven approach

Simulation of Supply Chains comprises huge amounts of data, resulting in numerous entities flowing in the model. These networks are highly dynamic systems, where entities' relationships and other elements evolve with time, paving the way for real-time Supply Chain decision-support tools capable of using real data. In light of this, a solution comprising of a Big Data Warehouse to store relevant data and a simulation model of an automotive plant, are being developed. The purpose of this paper is to address the modelling approach, which allowed the simulation model to automatically adapt to the data stored in a Big Data Warehouse and thus adapt to new scenarios without manual intervention. The main characteristics of the conceived solution were demonstrated, with emphasis to the real-time and the ability to allow the model to load the state of the system from the Big Data Warehouse.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

A distributed approach for AGV scheduling

Se adjuntan 6 archivos de Simio como soporte que contienen 6 modelos desarrollados durante el trabajo de grado. Además, se anexa un link que redirecciona a un sitio web seguro (Microsoft Stream) dónde se encuentra un video explicativo del modelo final de Simio desarrollado para el trabajo. Adicionalmente se adjuntan 2 archivos Excel, uno que contiene los modelos estáticos desarrollados (heurística y metaheurística) para validación del modelo final y otro que contiene análisis estadísticos realizados Por último, se anexan todos los documentos solicitados por la dirección de Trabajo de Grado en formato PDF junto con 2 adicionales que corresponden a memoria de cálculos para validaciones estadísticas y resultados de modelos estáticos.The implementation of Industry 4.0, where robotics mix with information and communication technologies to increase efficiency in Flexible Manufacturing Systems (FMS), is at its peak. Automated Guided Vehicles (AGVs) have become increasingly popular because they increase transportation flexibility, reducing transportation costs and overall process times. The AGV scheduling problem has been mostly pointed towards time optimization only using centralized approaches where the scheduling of production does not change and it is considered static. FMS in real life are dynamic environments that demand flexibility, as well as reactivity, to deal with changes in production conditions, such as machine breakdowns, rush orders, layout changes, lack of raw materials, among others. Therefore, there is a need for a dynamic approach to the AGV scheduling problem that addresses real life unexpected situations more efficiently, aiming for time saving at the same time. The purpose of this project is to design and implement, in a simulation environment, a distributed approach to the AGV scheduling problem that deals better with real-life FMS changing conditions. Results show that although our approach is based on the MSM heuristic, good performance measures in real time were obtained comparing with other optimization algorithms.Ingeniero (a) IndustrialPregrad

Efficient scheduling of batch processes in continuous processing lines

This thesis focuses mainly on the development of efficient formulations for scheduling in industrial environments. Likewise, decisions over the processes more related to advanced process control or production planning are included in the scheduling; in this way, the schedule obtained will be more efficient than it would be if the additional restrictions were not considered. The formulations have to emphasize obtaining online implementations, as they are planned to be used in real plants. The most common scheduling problems handled in the industrial environments are: the assignment of tasks to units, the distribution of production among parallel units and the distribution of shared resources among concurrent processes. Most advances in this work are the result of a collaborative work.Departamento de Ingeniería de Sistemas y AutomáticaDoctorado en Ingeniería Industria

Contribution for a Simulation Framework for Designing and Evaluating Manufacturing Systems

[Under development

Towards Developing a Digital Twin Implementation Framework for Manufacturing Systems

This research studies the implementation of digital twins in manufacturing systems. Digital transformation is relevant due to changing manufacturing techniques and user demands. It brings new business opportunities, changes organizations, and allows factories to compete in the digital era. Nevertheless, digital transformation presents many uncertainties that could bring problems to a manufacturing system. Some potential problems are loss of data, cybersecurity threats, unpredictable behavior, and so on. For instance, there are doubts about how to integrate the physical and virtual spaces. Digital twin (DT) is a modern technology that can enable the digital transformation of manufacturing companies. DT works by collecting real-time data of machines, products, and processes. DT monitors and controls operations in real-time helping in the identification of problems. It performs simulations to improve manufacturing processes and end-products. DT presents several benefits for manufacturing systems. It gives feedback to the physical system, increases the system’s reliability and availability, reduces operational risks, helps to achieve organizational goals, reduces operations and maintenance costs, predicts machine failures, etc. DT presents all these benefits without affecting the system’s operation. xv This dissertation analyzes the implementation of digital twins in manufacturing systems. It uses systems thinking methods and tools to study the problem space and define the solution space. Some of these methods are the conceptagon, systemigram, and the theory of inventive problem solving (TRIZ in Russian acronym). It also uses systems thinking tools such as the CATWOE, the 9-windows tool, and the ideal final result (IFR). This analysis gives some insights into the digital twin implementation issues and potential solutions. One of these solutions is to build a digital twin implementation framework Next, this study proposes the development of a small-scale digital twin implementation framework. This framework could help users to create digital twins in manufacturing systems. The method to build this framework uses a Model-Based Systems Engineering approach and the systems engineering “Vee” model. This framework encompasses many concepts from the digital twin literature. The framework divides these concepts along three spaces: physical, virtual, and information. It also includes other concepts such as digital thread, data, ontology, and enabling technologies. Finally, this dissertation verifies the correctness of the proposed framework. The verification process shows that the proposed framework can develop digital twins for manufacturing systems. For that purpose, this study creates a process digital twin simulation using the proposed framework. This study presents a mapping and a workflow diagram to help users use the proposed framework. Then, it compares the digital twin simulation with the digital twin user and system requirements. The comparison finds that the proposed framework was built right