Modeling and Decision Making in Manufacturing and Logistics in the Age of Industry 4.0
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ItemOn-demand Shared Digital Twins – An Information Architectural Model to Create Transparency in Collaborative Supply Networks( 2021-01-05)Industrial companies already apply digital twins for the digital representation of the physical world. In addition, information sharing becomes increasingly decisive for the competition, e.g. in supply networks where products and logistics data such as demands and capacities are exchanged. As many companies are, however, highly reluctant to share data across the supply chain, this paper applies the methodology of design science research to, first, state the requirements for shared digital twins based on five industrial use cases. It turns out that with regard to data decentralism, sovereignty and compatibility through global standardization are key success factors. Hence, second, this paper presents a concept for a shared digital twin providing data on demand, i.e. at the right time and in particular with data condensed to the concrete need, thus, minimizing volume. The concept covers both the company-internal and the supply network perspective.
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ItemMatrix Production Systems - Requirements and Influences on Logistics Planning for Decentralized Production Structures( 2021-01-05)In the context of the digital transformation of industry and within the framework of Industrie 4.0 and Factory Planning 4.0, new production-organizational principles with decentralized, modular and freely linked production cells are increasingly being discussed. The principle of matrix production with categorized and standardized work-stations offers an extremely versatile production environment. This can be used to meet the challenge of an increasing number of product variants in variable quantities. This concept is predominantly only considered from a theoretical point of view. Therefore, many aspects regarding the planning and operation of such systems are still open. With the focus on logistics processes, this paper describes the requirements for such flexible, dynamic routing and self-organizing resources in material supply. Fur-thermore, they are investigated in a generic, conceptual model for a matrix production. Based on a reference scenario from the automotive industry, classical parameters from logistics and production organization are taken up. The influences with regard to decentralized material supply concepts and structural differences to flow production are shown by the results of simulation experiments with the generic model.
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ItemDigital Twins in Order Picking Systems for Operational Decision Support( 2021-01-05)Digital twins are arousing great interest in both science and industry. There are a large number of papers that demonstrate and evaluate the potential of Digital Twins in different application areas. However, it must be noted that there is still no uniform definition of Digital Twins. This paper first examines the concept of Digital Twins and highlight how they differ in level, compared with other digital models. The focus of this paper lies in the conceptual development of a digital twin in order picking systems. The described approach in the paper aims at supporting the operational control in order picking systems. Both the architectural structure and the functions, e.g. the simulation, are described in detail. Overall, this thesis shows the benefits of Digital Twins. However, some functional extensions are still needed before the full potential can be achieved.
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ItemAn adaptive scheduling framework for solving multi-objective hybrid flow shop scheduling problems( 2021-01-05)The proposed new technologies in the context of industry 4.0 challenge the current practices of scheduling in industry and their associated research in academia. The conventional optimization techniques that are employed for solving scheduling problems are either computationally expensive or lack the required quality. Therefore, in this paper, we propose an adaptive scheduling framework to address scheduling problems taking into account multi-objective optimality measures. The framework is motivated by a hybrid design to combine the use of heuristic and metaheuristic approaches. The main idea behind the presented concept is to achieve an acceptable tradeoff between the quality of the suggested solutions for a problem and the required computational effort to obtain them. The perused narrative in such implementation is combining some advantages of heuristic and metaheuristic approaches such as: the light execution time of heuristics and the robustness as well as the quality of metaheuristic approaches. The framework is evaluated for solving hybrid flow shop scheduling problems that are derived from a real use case.
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