Implementing Digital Twins of Smart Factories with Interval Algebra

This paper presents an interval algebra that can be used to create an analytic description of a smart factory. Such a description (recently termed a `digital twin' of the factory) is used to evaluate alternative manufacturing configurations as part of a search-based optimisation process. Several extensions are proposed to the interval algebra for specifying smart factory production line details. A number of real-life manufacturing scenarios are described, related to Wire-cut Electrical Discharge Machining. The experimental results show the applicability and scalability of the proposed method

Smart Manufacturing

This book is a collection of 11 articles that are published in the corresponding Machines Special Issue “Smart Manufacturing”. It represents the quality, breadth and depth of the most updated study in smart manufacturing (SM); in particular, digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the Internet to expand system capabilities, (3) supporting data-driven decision-making activities at various domains and levels of businesses, or (4) reconfiguring systems to adapt to changes and uncertainties. System smartness can be evaluated by one or a combination of performance metrics such as degree of automation, cost-effectiveness, leanness, robustness, flexibility, adaptability, sustainability, and resilience. This book features, firstly, the concepts digital triad (DT-II) and Internet of digital triad things (IoDTT), proposed to deal with the complexity, dynamics, and scalability of complex systems simultaneously. This book also features a comprehensive survey of the applications of digital technologies in space instruments; a systematic literature search method is used to investigate the impact of product design and innovation on the development of space instruments. In addition, the survey provides important information and critical considerations for using cutting edge digital technologies in designing and manufacturing space instruments

Impact of IIoT and Lean bundles configurations on proactive work behaviors

This thesis investigates the impact of IIoT and Lean bundles configurations on team proactivity, that is calculated as a mean of individual proactivity values. It is divided in four chapters: in the first chapter an introduction to Industry 4.0 and a description of its main technologies is performed, the second chapter is characterized by a literature review on the integration between Industry 4.0 an Lean Production, the third chapter consists on a description of the Qualitative Comparative Analysis approach and the last chapter discusses the results of the analysis

Cloud-based Dynamic Distributed Optimisation of Integrated Process Planning and Scheduling in Smart Factories

In smart factories, process planning and scheduling need to be performed every time a new manufacturing order is received or a factory state change has been detected. A new plan and schedule need to be determined quickly to increase the responsiveness of the factory and enlarge its profit. Simultaneous optimisation of manufacturing process planning and scheduling leads to better results than a traditional sequential approach but is computationally more expensive and thus difficult to be applied to real-world manufacturing scenarios. In this paper, a working approach for cloud-based distributed optimisation of process planning and scheduling is presented. It executes a multi-objective genetic algorithm on multiple subpopulations (islands). The number of islands is automatically decided based on the current optimisation state. A number of test cases based on two real-world manufacturing scenarios are used to show the applicability of the proposed solution

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Lean and industry 4.0: a QCA analysis

This work proposes an empirical study on the diffusion of lean practices and industry's 4.0 technologies among italian manufacturing companies. The qualitative comparative analysis (QCA) identifies the combination of practices and technologies that assure a high probability to the enterprises of the sample.ope

Cloud-based Integrated Process Planning and Scheduling Optimisation via Asynchronous Islands

In this paper, we present Optimisation as a Service (OaaS) for an integrated process planning and scheduling in smart factories based on a distributed multi-criteria genetic algorithm (GA). In contrast to the traditional distributed GA following the island model, the proposed islands are executed asynchronously and exchange solutions at time points depending solely on the optimisation progress at each island. Several solutions' exchange strategies are proposed, implemented in Amazon Elastic Container Service for Kubernetes (Amazon EKS) and evaluated using a real-world manufacturing problem

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems