Publicação
Modelling Smart Manufacturing Assets Targeting Scheduling Optimisation
| Resumo: | The industry sector has evolved faster and faster over the past few decades, driven by increas- ingly complex market demands. Customers now hold greater decision power, and factories are pressured not only to deliver products fast but also to optimize production processes, reducing costs, inefficiencies, and delays. Companies must ensure they can meet customer expectations without compromising operational efficiency. Thus, modern manufacturing systems must be robust and agile, capable of reacting smoothly to external events, and adaptable to unexpected changes. In this world that is be- coming more and more connected, the rise of smart factories, characterized by interconnected and autonomous entities, is transforming how production systems operate. These entities are becoming able to adapt to real-time events but also share critical data between them to opti- mize workflow, minimize downtime, and ensure continuous production. To maintain production efficiency, meet KPIs like makespan, reduce downtimes, and im- prove energy efficiency, or be more prepared for unexpected disturbances in the system, it is important that companies are equipped with robust and adaptable manufacturing scheduling systems. While numerous solutions have been proposed over the years to implement sched- uling systems, in many cases, those approaches focus on specific cases and do not fulfill the necessary requirements to be applied in industry. This research addresses these limitations by providing a more generic, comprehensive, and adaptable approach for smart manufacturing environments. The design and implementa- tion of scheduling solutions in smart manufacturing systems is not standardized and there is not a reference model to develop scheduling solutions that reflect real industrial environments, leading to a gap between reference architectures and scheduling systems. Therefore, the pro- posed research intends to study the main challenges related to manufacturing scheduling and to model manufacturing components targeting the scheduling optimization based on one of the most prosperous reference architectures, RAMI 4.0. Through an extensive literature review, both functional and non-functional requirements were identified and, after analyzing them, the design principles to develop a manufacturing scheduling system were established. Additionally, a methodology was proposed to serve as the foundation for designing scheduling solutions aligned with RAMI4.0, including the identi- fication of the main assets and the development of their corresponding Asset Administration Shells, while addressing key design principles such as data uniformity, KPI harmonization, and automatic rescheduling. Finally, the proposed approach was applied to various use cases, in- cluding the KITT4SME and PERFoRM projects, to demonstrate its efficiency and adaptability. This work aims to fill a critical gap in existing literature but also offers a practical roadmap for industry professionals aiming to fully integrate production scheduling into RAMI4.0, paving the way for smarter, more responsive manufacturing systems in the era of Industry 4.0. |
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| Autores principais: | Alemão, Duarte José Marques |
| Assunto: | Manufacturing Scheduling Scheduling Methodology Industry 4.0 Cyber- Physical Production Systems RAMI4.0 Assets Modelling |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | tese de doutoramento |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The industry sector has evolved faster and faster over the past few decades, driven by increas- ingly complex market demands. Customers now hold greater decision power, and factories are pressured not only to deliver products fast but also to optimize production processes, reducing costs, inefficiencies, and delays. Companies must ensure they can meet customer expectations without compromising operational efficiency. Thus, modern manufacturing systems must be robust and agile, capable of reacting smoothly to external events, and adaptable to unexpected changes. In this world that is be- coming more and more connected, the rise of smart factories, characterized by interconnected and autonomous entities, is transforming how production systems operate. These entities are becoming able to adapt to real-time events but also share critical data between them to opti- mize workflow, minimize downtime, and ensure continuous production. To maintain production efficiency, meet KPIs like makespan, reduce downtimes, and im- prove energy efficiency, or be more prepared for unexpected disturbances in the system, it is important that companies are equipped with robust and adaptable manufacturing scheduling systems. While numerous solutions have been proposed over the years to implement sched- uling systems, in many cases, those approaches focus on specific cases and do not fulfill the necessary requirements to be applied in industry. This research addresses these limitations by providing a more generic, comprehensive, and adaptable approach for smart manufacturing environments. The design and implementa- tion of scheduling solutions in smart manufacturing systems is not standardized and there is not a reference model to develop scheduling solutions that reflect real industrial environments, leading to a gap between reference architectures and scheduling systems. Therefore, the pro- posed research intends to study the main challenges related to manufacturing scheduling and to model manufacturing components targeting the scheduling optimization based on one of the most prosperous reference architectures, RAMI 4.0. Through an extensive literature review, both functional and non-functional requirements were identified and, after analyzing them, the design principles to develop a manufacturing scheduling system were established. Additionally, a methodology was proposed to serve as the foundation for designing scheduling solutions aligned with RAMI4.0, including the identi- fication of the main assets and the development of their corresponding Asset Administration Shells, while addressing key design principles such as data uniformity, KPI harmonization, and automatic rescheduling. Finally, the proposed approach was applied to various use cases, in- cluding the KITT4SME and PERFoRM projects, to demonstrate its efficiency and adaptability. This work aims to fill a critical gap in existing literature but also offers a practical roadmap for industry professionals aiming to fully integrate production scheduling into RAMI4.0, paving the way for smarter, more responsive manufacturing systems in the era of Industry 4.0. |
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