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Methods for Hybrid Modeling and Simulation-Based Optimization in Energy-Aware Production Planning

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... To demonstrate the application of the proposed GVN-S/SA method on the flow shop scheduling case study, the optimization was implemented as a prototype in MATLAB and coupled with the standalone hyPDEVS simulator [36]. Table 2 presents a simplified scenario of demand (Dplan) needing to be scheduled over the course of two days (i.e. ...
... These higher energy costs have to do with the fact that the energy demand is unevenly distributed throughout the day, not only due to production, but also, for example, due to intermittent filling of the heat and cold storage (that are part of the energy system infrastructure), which often occurs in times of above-average energy prices. Even if one could have suspected that this would balance out over the day, this does not seem to be the case here [36]. ...
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The prevailing system and software model in automation systems engineering is defined by the IEC 61131 norm. It is to date the best way we know how to express low-level logic and manipulate electrical hardware signals. However, the exponential technological growth is continuing to raise the expectations on what automation systems are supposed to be capable of doing. Fulfilling rising requirements and managing the exploding complexity requires a systematic support for high-level descriptions, structuring, and communication, which the original approach was not built to provide. This work proposes the introduction of an abstraction layer, a component-container infrastructure, defined on top of standard system and software models in automation and mirroring the world of cyber–physical systems, where independent components are interconnected to realize the systems’ purpose by using each other’s functionalities. The concept is implemented in the form of a domain-specific modeling language, applying a classical two-level Model-driven Software Engineering (MDSE) approach. By engineering distinct industrial use cases in accordance with the proposed approach, it is shown that the defined abstractions and mechanisms are capable of expressing the nuances of software design in different domains and can enable the streamlining of the automation systems engineering workflow into a virtual plug&produce process.
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Various development and validation methods for cyber-physical systems such as Controller-Hardware-in-the-Loop (C-HIL) testing strongly benefit from a seamless integration of (hardware) prototypes and simulation models. It has been often demonstrated that linking discrete event-based control systems and hybrid plant models can advance the quality of control implementations. Nevertheless, high manual coupling efforts and sometimes spurious simulation artifacts such as glitches and deviations are observed frequently. This work specifically addresses these two issues by presenting a generic, standard-based infrastructure referred to as virtual component, which enables the efficient coupling of simulation models and automation systems. A novel soft real-time coupling algorithm featuring event-accurate synchronization by extrapolating future model states is outlined. Based on considered standards for model exchange (FMI) and controls (IEC 61499), important properties such as real-time capabilities are derived and experimentally validated. Evaluation demonstrates that virtual components support engineers in efficiently creating C-HIL setups and that the novel algorithm can feature accurate synchronization when conventional approaches fail.
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Advanced production planning and scheduling approaches increasingly rely on simulation-based optimization methods. This entails the problem of a high computational effort due to complex models, resulting in limitations for the practical application of otherwise powerful methods. While machine-learning methods offer a potential for performance improvement, approaches for real-life applications with a high complexity are still lacking. This paper explores the potential for machine learning, especially artificial neural networks, used as surrogate models, to improve the performance of a recently developed planning method for real life production planning applications. The simulation considered in this paper is a complex hybrid discrete-continuous model, enabling the method to pursue energy efficiency simultaneously with economic goals, in a complex multi-criteria goal system. The artificial neural network is trained via offline learning and is meant to provide a computationally cheap evaluation of intermediate planning solutions, compiled by an optimization algorithm during an optimization run. The approach is developed and evaluated in a case-study on the food industry, indicating a basic feasibility of the approach but also pointing out necessary future challenges to be solved towards practical applicability.
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Theory of Modeling and Simulation: Discrete Event & Iterative System Computational Foundations, Third Edition, continues the legacy of this authoritative and complete theoretical work. It is ideal for graduate and PhD students and working engineers interested in posing and solving problems using the tools of logico-mathematical modeling and computer simulation. Continuing its emphasis on the integration of discrete event and continuous modeling approaches, the work focuses light on DEVS and its potential to support the co-existence and interoperation of multiple formalisms in model components. New sections in this updated edition include discussions on important new extensions to theory, including chapter-length coverage of iterative system specification and DEVS and their fundamental importance, closure under coupling for iteratively specified systems, existence, uniqueness, non-deterministic conditions, and temporal progressiveness (legitimacy).
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Hybrid simulation (defined as a modelling approach that combines two or more of the following methods: discrete-event simulation, system dynamics, and agent-based simulation) has experienced near-exponential growth in popularity in the past two decades. However, a large proportion of the academic literature on hybrid simulation is found in computer science and engineering journals. Given the importance of this emerging area and its relevance to operational research, this paper provides a review of the topic from an OR perspective. The results of a review of the hybrid simulation literature are presented, using a novel framework based on the simulation lifecycle that will be useful for future modellers and authors alike. Promising areas for future research are identified: these include the development of new methods for conceptual modelling and for model validation. Currently the main application areas are healthcare, supply chain management and manufacturing, and the majority of published models combine discrete-event simulation and system dynamics.
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An energy-efficient production is imperative and can reduce costs. Despite the acknowledged potential to increase energy efficiency in production systems through production planning and control (PPC), adequate planning methods are lacking. This article presents an interdisciplinary approach for a simulation-based multi-criteria optimization, integrating energy efficiency into PPC objectives. The method considers production equipment together with HVAC and technical building services. It features a novel integrated hybrid discrete/continuous simulation method enabling to accurately capture dynamic interactions between material and energy flows. The approach is evaluated in a case study on the food industry, indicating potential energy efficiency gains of up to 30%.
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In the challenge of achieving environmental sustainability, industrial production plants, as large contributors to the overall energy demand of a country, are prime candidates for applying energy efficiency measures. A modelling approach using cubes is used to decompose a production facility into manageable modules. All aspects of the facility are considered, classified into the building, energy system, production and logistics. This approach leads to specific challenges for building performance simulations since all parts of the facility are highly interconnected. To meet this challenge, models for the building, thermal zones, energy converters and energy grids are presented and the interfaces to the production and logistics equipment are illustrated. The advantages and limitations of the chosen approach are discussed. In an example implementation, the feasibility of the approach and models is shown. Different scenarios are simulated to highlight the models and the results are compared. © 2018, International Centre for Sustainable Development of Energy, Water and Environment Systems SDEWES. All rights reserved.
Conference Paper
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This presented research comprises the development of an optimization module for use in a novel production optimization tool-similar in function but not mode of operation to an Advanced Planning System-, with energy efficiency incorporated into its goal system. The optimization features a hybrid-simulation of production systems as an evaluation function. A hybrid simulation has been developed and presented in preceding publications, in order to enable a sufficient consideration of interactions between material flow and the thermal-physical behavior of the production system. The size of the search space for the complex optimization problem necessitates a customized two-phase-optimization method, which is based on a Genetic Algorithm, with the consideration of linear constraints and extended customizations. The results, obtained in a case study featuring a food production facility, show energy savings of around 20 percent together with significant productivity gains.
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Demand Response can be seen as one effective way to harmonize demand and supply in order to achieve high self-coverage of energy consumption by means of renewable energy sources. This paper presents two different simulation-based concepts to integrate demand-response strategies into energy management systems in the customer domain of the Smart Grid. The first approach is a Model Predictive Control of the heating and cooling system of a low-energy office building. The second concept aims at industrial Demand Side Management by integrating energy use optimization into industrial automation systems. Both approaches are targeted at day-ahead planning. Furthermore, insights gained into the implications of the concepts onto the design of the model, simulation and optimization will be discussed. While both approaches share a similar architecture, different modelling and simulation approaches were required by the use cases. © 2018, International Centre for Sustainable Development of Energy, Water and Environment Systems SDEWES. All rights reserved.
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In the design of manufacturing systems the consideration of resource usage, especially energy consumption, is getting more attention. However, the inclusion of all relevant physical processes in a unified modeling approach is a non-trivial task, if detailed analyses are required. The commonly used modeling approach for manufacturing systems is the discrete event modeling technique. However, models of physical processes are often continuous in nature and are modeled using ordinary differential equations or differential algebraic equations. Indeed, the investigation of such physical processes in manufacturing systems often demands a more specific consideration of process control operations, which are favorably modeled using state machines. To combine those different paradigms a multimodeling approach for manufacturing systems is proposed. The approach is illustrated by the example of a production line with an industrial furnace facility.
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Deutsch: In diesem Beitrag werden unterschiedliche Ansätze von Automatisierungspyramiden gegenübergestellt. Leider gibt es in der Literatur keinen Konsens über die Benennung und die Anzahl an Ebenen, die eine Automatisierungspyramide umfassen sollte (es exis-tieren Modelle mit drei bis sieben Ebenen). Im Folgenden werden die unterschiedlichen Ansätze tabellarisch erfasst und eine Einordnung vorgenommen. Diese Publikation soll Autoren und Forschern als Orientierungshilfe zur Auswahl eines der Konzepte dienen. English: In this article, different approaches to automation pyramids are compared. Unfortunate-ly, there is no consensus in the literature about the naming and the number of levels that an automation pyramid should comprise (models with three to seven levels are existing). In the following, the different approaches are tabulated and a classification is carried out. This publication is intended to help authors and researchers to provide guidance on how to select one of the concepts.
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The acquisition of data and the development of different options in production system and factory planning requires up to 2/3rds of the total needed time resources. The digitization of production systems offers the possibility of automated data acquisition. Nevertheless, approaches concerning fully automated data acquisition systems are not widely spread among SME (small and medium sized enterprises). On the one hand, this is caused by the heterogeneous databases, on the other hand by insufficient data processing systems. Furthermore, the advantages of The Digital Twin are not sufficiently known due to the lack of competence in SME concerning matters of Industry 4.0. In order to transfer knowledge about the benefits of digitalization, the development of demonstrating platforms is crucial. This paper introduces a learning factory based concept to demonstrate the potentials and advantages of real time data acquisition and subsequent simulation based data processing. Therefore, an existing learning factory will be upgraded regarding both, multi-modal data acquisition technologies as well as a locally independent optimization environment. Thereby the requirements of SME concerning flexible, easy to use, scalable and service oriented digitization applications are met. The approach is part of a concept for the realization of a Cyber Physical Production System (CPPS) in SME that ensures the development of an image of the production with the aid of a multi-modal data acquisition.
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This work presents an approach to determine relevant energy efficiency and productivity KPIs of machining processes based on a real-time interpretation of sensor data and machine control data. A comparison of the actual power consumption during machining with an energetic model of the load-free condition enables the calculation of energetic efficiency and primary processing time. The approach was tested on a CNC turning and milling center equipped with power meters and compressed air sensors. Sensor data as well as relevant machine control data are read, processed and recorded via SCADA software in order to automatically calculate certain KPIs.
Conference Paper
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MATLAB/Simulink is a popular software environment used by engineers and scientists. It offers integrated modeling and simulation tools and numerous toolboxes employable in conjunction with modeling and simulation tasks. However, modeling and simulation of discrete event systems is often confusing and sometimes not compliant with the established system theory. This contribution presents a MATLAB toolbox for hybrid system modeling and simulation, based on an extended Parallel Discrete Event System Specification (PDEVS) formalism, called hybrid PDEVS. The formalism is introduced, focusing on the integration of MATLAB's built-in solvers for differential equations. Additionally, specific features regarding the modeling and simulation process and debugging support are discussed. Finally, the combination with other MATLAB methods and toolboxes is illustrated by means of two examples. The toolbox should contribute to bringing the PDEVS formalism into the engineering community and to support research in system simulation in conjunction with other numerical methods.
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This research aims to develop a novel planning tool able to increase both the energy efficiency and general performance of production systems using a hybrid-simulation based, multi-criteria optimization, with this particular paper focusing on the optimization method. Lacking necessary planning tools for an energy aware production planning and control, companies are unable harness the associated optimization potential. State-of-the-art tools are not able to sufficiently consider interactions between the discrete system behavior of material flow and the continuous thermal-physical behavior of equipment. This paper presents a planning method addressing this deficiency. The developed genetic-algorithm based optimization module optimally fits the requirements.
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The trading activity in the German intraday electricity market has increased significantly over the last years. This is partially due to an increasing share of renewable energy, wind and photovoltaic, which requires power generators to balance out the forecasting errors in their production. We investigate the bidding behaviour in the intraday market by looking at both last prices and continuous bidding, in the context of a reduced-form econometric analysis. A unique data set of 15-minute intraday prices and intraday-updated forecasts of wind and photovoltaic has been employed. Price bids are explained by prior information on renewables forecasts and demand/supply market-specific exogenous variables. We show that intraday prices adjust asymmetrically to both forecasting errors in renewables and to the volume of trades dependent on the threshold variable demand quote, which reflects the expected demand covered by the planned traditional capacity in the day-ahead market. The location of the threshold can be used by market participants to adjust their bids accordingly, given the latest updates in the wind and photovoltaic forecasting errors and the forecasts of the control area balances.
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Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization. Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. Ant colony optimization exploits a similar mechanism for solving optimization problems. From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available. Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO. The goal of this article is to introduce ant colony optimization and to survey its most notable applications
Thesis
This thesis documents the development of a planning method that simultaneously increases the energy efficiency as well as the economic performance of complex production systems. The method utilizes a multi-criteria optimization based on a hybrid simulation that is able to exhibit both continuous and discrete behaviour. Energy efficiency has become an important goal for manufacturing enterprises, largely due to a combination of rising societal and political pressure towards a more sustainable way of conducting business and growing energy prices in the long-term. There is considerable potential in this area to be found in the optimized production planning and control (PPC), as well as the synchronized control of equipment in the periphery, together with a goal system that features both classical economic goals and energy efficiency in the objective function of an integrated planning approach. However, despite the recognized demand, the necessary planning methods are missing for manufacturing companies, resulting in wasted potential. Even the scientific approaches publicized thus far are lacking crucial elements: Especially the integrated planning of the discrete material flow and order processing together with the thermal-physical behaviour of equipment – e.g. industrial ovens or chillers – and its interdependencies is not sufficiently provided by available approaches. As a result, the associated optimization potential is left unutilized. This research aims at developing a planning method that integrates energy efficiency into the goal system of production planning optimization. To achieve this, a modelling concept supporting the integrated consideration of production systems is being developed. Next, a corresponding novel hybrid simulation method that supports both the discrete and continuous behaviour of production and energy systems is evolved. Following that, an optimization module utilizing the simulation as an evaluation function and enabling the simultaneous optimization of the short-term production plan and optimized control of relevant equipment in the periphery is engineered. The resulting overall method is then evaluated in a case study featuring a food production facility, revealing an overall optimization potential of up to 50%, amid energy savings of up to 30%.
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Digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and physical spaces. The importance of DTs is increasingly recognized by both academia and industry. It has been almost 15 years since the concept of the DT was initially proposed. To date, many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields. However, at present, no paper has focused on the review of DT applications in industry. In an effort to understand the development and application of DTs in industry, this paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development of DTs, and the major DT applications in industry. This paper also outlines the current challenges and some possible directions for future work.
Conference Paper
In vielen produzierenden Unternehmen ist Energie ein wesentlicher Kostenfaktor. Energieaspekte werden des-halb in das Entscheidungssystem der Produktionsplanung und-steuerung einbezogen, um die Herstellungskosten zu senken. Die Simulation von Produktionsprozessen erfordert neben der Berücksichtigung technischer und logistischer Pro-duktionsfaktoren auch die Integration von kontinuierlichen Energieverbräuchen. Da Fertigungssysteme im Allgemeinen in diskreten Simulationsmodellen beschrieben werden, könnte ein Ansatz, der die beiden Systemdynamiken kombi-niert, vorteilhaft sein. Die kombinierte Simulation nutzt einen kontinuierlichen Simulationsansatz zur Abbildung des Ener-giebedarfs relevanter Produktionsprozesse und kombiniert diesen mit einem diskreten Simulationsansatz zur Abbildung von Material-und Logistikprozessen. Durch die Zusammen-führung der Modelle können die Wechselwirkungen zwi-schen Materialfluss und Energieverbrauch in der Produktion realitätsnäher simuliert werden.
Article
The Digital Twin (DT) is commonly known as a key enabler for the digital transformation, however, in literature is no common understanding concerning this term. It is used slightly different over the disparate disciplines. The aim of this paper is to provide a categorical literature review of the DT in manufacturing and to classify existing publication according to their level of integration of the DT. Therefore, it is distinct between Digital Model (DM), Digital Shadow (DS) and Digital Twin. The results are showing, that literature concerning the highest development stage, the DT, is scarce, whilst there is more literature about DM and DS.
Article
The paper discusses the basics of communication systems for open, safe, secure, near realtime, standardized communication interfaces and examines the problem of a unified architecture to comply with the principles of Industry 4.0 as applied to enterprises of the future. The first part of the analysis presents Open Platform Communication - Unified Architecture (OPC UA), a common SW basis to interconnect communication interfaces and control plus information protocols for the purposes of Industry 4.0. The platform enables readers from different branches of engineering to understand the fundamentals of state-of-the-art open communication, a necessity for further development of the Industrial Internet of Things (IIoT) and Industry 4.0 concepts. The following portion of the text then presents the most topical activities within enhancing the real - time properties of Internet of Things and Industry 4.0. The basics of Time Sensitive Networks (TSN) are explained and compared with the features and possibilities of standard public networks (the Internet) in relation to the onset of the 4th industrial revolution as outlined in the article.
Article
Modeling and simulation techniques are today extensively used both in industry and science. Parts of larger systems are, however, typically modeled and simulated by different techniques, tools, and algorithms. In addition, experts from different disciplines use various modeling and simulation techniques. Both these facts make it difficult to study coupled heterogeneous systems. Co-simulation is an emerging enabling technique, where global simulation of a coupled system can be achieved by composing the simulations of its parts. Due to its potential and interdisciplinary nature, co-simulation is being studied in different disciplines but with limited sharing of findings. In this survey, we study and survey the state-of-the-art techniques for co-simulation, with the goal of enhancing future research and highlighting the main challenges. To study this broad topic, we start by focusing on discrete-event-based co-simulation, followed by continuous-time-based co-simulation. Finally, we explore the interactions between these two paradigms, in hybrid co-simulation. To survey the current techniques, tools, and research challenges, we systematically classify recently published research literature on co-simulation, and summarize it into a taxonomy. As a result, we identify the need for finding generic approaches for modular, stable, and accurate coupling of simulation units, as well as expressing the adaptations required to ensure that the coupling is correct.
Article
The inventory routing problem (IRP) combines inventory management and delivery route‐planning decisions. This work presents a simheuristic approach that integrates Monte Carlo simulation within a variable neighborhood search (VNS) framework to solve the multiperiod IRP with stochastic customer demands. In this realistic variant of the problem, our goal is to establish the optimal refill policies for each customer–period combination, that is, those individual refill policies that minimize the total expected cost over the periods. This cost is the aggregation of both expected inventory and routing costs. Our simheuristic algorithm allows to consider the inventory changes between periods generated by the realization of the random demands in each period, which have an impact on the quantities to be delivered in the next period and, therefore, on the associated routing plans. A range of computational experiments are carried out in order to illustrate the potential of our simulation–optimization approach.
Book
The application of modelling and simulation is unconstrained by discipline boundaries. It provides support for the planning, design and evaluation of system behaviour, as well as the evaluation of strategies for dynamic system transformation and change. Modelling and Simulation: Exploring Dynamic System Behaviour provides the reader with a balanced and integrated presentation of the modelling and simulation activity for both Discrete Event Dynamic Systems (DEDS) and Continuous Time Dynamic Systems (CTDS). Unconstrained by discipline boundaries, this book presents the fundamentals necessary to understand the many important facets of the modeling and simulation methodology. For example, a novel project-oriented approach clearly reveals the dependency of model structure and granularity on project goals. Features include: • A goal (i.e. project) oriented approach for developing the modelling and simulation methodology • Simulation program development is illustrated in a variety of programming environments • Extensively illustrated with numerous practical examples and useful optimization algorithms • Presentation and use of a novel conceptual modelling approach for DEDS • Describes the process for incorporating parameter optimization into the modeling and simulation activity • Includes primer appendices for each of two (public domain) simulation software environments • Public domain simulation software and a variety of teaching support material are available at a website maintained by the authors Written for students taking a first course in modelling and simulation at either the senior undergraduate & junior graduate levels, this interdisciplinary presentation provides an essential foundation on the topic. It is also ideally suited for self-study by professionals and others who require insight into the features and potential of this rapidly evolving problem solving paradigm.
Book
Please note that Springer has given (temporary) free access to the book due to the Corona crisis at https://link.springer.com/book/10.1007%2F978-3-642-55309-7. Supply Chain Management, Enterprise Resources Planning (ERP), and Advanced Planning Systems (APS) are important concepts in order to organize and optimize the flow of materials, information and financial funds. This book, already in its fifth edition, gives a broad and up-to-date overview of the concepts underlying APS. Special emphasis is given to modeling supply chains and implementing APS successfully in industry. Understanding is enhanced by several case studies covering APS from various software vendors. The fifth edition contains updated material, rewritten chapters and an additional case study.
Book
Die Produktionsplanung und -steuerung (PPS) ist vor dem Hintergrund des tief greifenden strukturellen Wandels des Wettbewerbsumfeldes sowie einer zunehmenden Volatilität der Märkte von großer Bedeutung für die produzierende Industrie. Die Auftragsabwicklung erfolgt heute in Netzwerken und die PPS erstreckt sich über die Unternehmensgrenzen hinweg. Für den Praktiker fehlen insbesondere für die unternehmensübergreifende PPS anwendbare Gestaltungsmethoden, die auf fundierten theoretischen Grundlagen basieren. Der Band 2 zeigt neuere technologische und organisatorische Entwicklungen rund um das Produktionsmanagement und bietet so eine valide Orientierungshilfe zur mittel- und langfristig tragfähigen Gestaltung der unternehmensinternen und -übergreifenden PPS.
Book
Das Buch bietet eine Einführung in die Organisation der Produktion in Industriebetrieben. Es behandelt Fragen der Produktionsziele, der Wirtschaftlichkeit, der Aufgaben der an der Produktion beteiligten Funktionsbereiche sowie ganzheitliche Konzepte der modernen Produktion und Methoden der Produktionsoptimierung. Es liefert ein Grundverständnis für die Funktionsweise industriell arbeitender Unternehmen und für die Konzepte der Planung, des Betriebs- und der Leistungsoptimierung von Produktionsbetrieben. Das Buch ermöglicht einen schnellen Einstieg in die Thematik und ist zu empfehlen für Studierende an Universitäten und Fachhochschulen sowie für Quereinsteiger in die Unternehmenspraxis.
Article
Modelling complex software systems requires multiple modelling formalisms adapted to the nature of each part of the system (control, signal processing, etc.), to the aspect on which the model focuses (functionality, time, fault tolerance, etc.) and to the level of abstraction at which the system, or one of its parts, is studied. The use of different modelling formalisms during the development cycle is therefore both unavoidable and essential. As a consequence, system designers deal with a large variety of models that relate to a given system but do not form a global model of this system. A major difficulty is then to answer questions about properties of the whole system, and in particular about its behaviour. Multi-Formalism Modelling allows the joint use of different modelling formalisms in a given model to overcome issues related to the integration of heterogeneous models. It applies to different tasks of the development cycle such as simulation, verification or testing.¹ We propose an approach to multi-formalism modelling, called ModHel’X, which is based on the concept of Model of Computation and focuses on the simulation of models. Our approach addresses two important issues in this particular field: (a) providing support for the specification of the execution semantics of a modelling formalism, and (b) allowing the specification of the interactions between parts of a model described using different modelling formalisms.
Article
This book discusses how model-based approaches can improve the daily practice of software professionals. This is known as Model-Driven Software Engineering (MDSE) or, simply, Model-Driven Engineering (MDE). MDSE practices have proved to increase efficiency and effectiveness in software development, as demonstrated by various quantitative and qualitative studies. MDSE adoption in the software industry is foreseen to grow exponentially in the near future, e.g., due to the convergence of software development and business analysis. The aim of this book is to provide you with an agile and flexible tool to introduce you to the MDSE world, thus allowing you to quickly understand its basic principles and techniques and to choose the right set of MDSE instruments for your needs so that you can start to benefit from MDSE right away. The book is organized into two main parts. The first part discusses the foundations of MDSE in terms of basic concepts (i.e., models and transformations), driving principles, a...
Article
To assess cost, time investment, energy consumption and carbon emission of manufacturing on a per-piece basis, a bottom-up approach for aggregating a real-time product footprint is proposed. This method allows the evaluation of the environmental impact of a batch or even single product using monitoring or simulation data. To analyze the infrastructure, the production plant is decomposed into modules that are in relation to each other via inputs and outputs. Distinguishing between modules for production, logistics, energy system, buildings and auxiliary systems, the different approaches for distributing resource consumption between the products are presented. Special attention is paid to typical scenarios that occur in production plants and problems that may arise from them. For example, the incorporation of standby-, setup- and ramp-up times, the energy consumption of the administration and the allocation of different products and by-products manufactured at a machine are taken into account. © 2016, International Centre for Sustainable Development of Energy, Water and Environment Systems SDEWES. All rights reserved.
Article
Multi-level modeling, i.e., the explicit use of multiple levels of classification in modeling, is a conservative extension of the well-established, traditional two-level object-oriented paradigm. Two-level object-oriented technology has been tremendously successful in both modeling (e.g., UML) and programming (e.g., Java). However, it has been shown that attempting to capture certain domains or systems with only two classification levels (i.e., objects and their types) results in accidental complexity that stems from an impedance mismatch between the subject at hand and the solution technology used to capture it [2]. Examples for domains that can be much more elegantly captured using multiple classification levels are biological taxonomies, process (meta-) modeling, enforced software architectures, and systems with dynamic type levels [4]. In particular, two-level technologies suffer from a simplistic type/instance dichotomy. Modeling elements must either play the role of a type or the role of an instance but not both. This means that types themselves cannot be treated as first class citizens of a system, i.e., as instances of (meta-) types, and thus cannot themselves be added dynamically to a system in a type-safe way.
Chapter
The Internet of Things (IoT) is an information network of physical objects (sensors, machines, cars, buildings, and other items) that allows interaction and cooperation of these objects to reach common goals [2]. While the IoT affects among others transportation, healthcare, or smart homes, the Industrial Internet of Things (IIoT) refers in particular to industrial environments. In this context Cyber Manufacturing Systems (CMS) evolved as a significant term. This opening chapter gives a brief introduction of the development of IIoT introducing also the Digital Factory and cyber-physical systems. Furthermore, the challenges and requirements of IIoT and CMS are discussed as well as potentials regarding the application in Industry 4.0 are identified. In this process aspects as economic impact, architectural pattern and infrastructures are taken into account. Besides, also major research initiatives are presented. In addition to that, an orientation to the reader is given in this chapter by providing brief summaries of the chapters published in this book. Hereby, the following research areas are addressed: “Modeling for CPS and CPS”, “Architectural Design Patterns for CMS and IIoT”, “Communication and Networking”, “Artificial Intelligence and Analytics”, and “Evolution of Workforce and Human-Machine-Interaction”. The chapter closes with a discussion about future trends of IIoT and CMS within Industry 4.0.
Article
Factories consist of production equipment, technical building services and a building shell, which are dynamically connected through energy and resource flows. An isolated analysis of flows cannot sufficiently consider the strong interdependencies and mutual relationship between resources. Because of that, it is relevant to acquire a sound understanding of conflicting and synergetic interactions between resources, in order to assess risks and to avoid problem shifting. The close intertwining of water and energy demand (water-energy nexus) in a factory exemplary represents a prominent relationship of coupled resources at all factory elements through different flows. To analyze and evaluate potential problem shifts as well as dynamic system/factory behavior, simulation has proven to be an appropriate method. However, simulation approaches often have a limited scope and address only isolated manufacturing system levels. Moreover, existing multi-level and multi-model approaches do not clearly state the method used for level selection, transferred parameters and coupling options for different models. This paper presents a multi-level simulation framework and recommendations for selecting coupling concepts. The recommendations refer to the simulation goals and the involved data to be exchanged between the different simulation models and factory elements. The framework supports developing coupled simulation models and it helps to address and assess problem shift issues. This is shown by exemplarily applying the framework in the context of the water-energy nexus of an automotive factory. The application reveals amplifying as well as attenuating effects of potential improvement measures on the water and energy demand indicating the importance of gaining a holistic factory perspective.
Article
This paper aims to develop a green building meta-model for a representative passively designed high-rise residential building in Hong Kong. Modelling experiments are conducted with EnergyPlus to explore a Monte Carlo regression approach, which intends to interpret the relationship between input parameters and output indices of a generic building model and provide reliable building performance predictions. Input parameters are selected from different passive design strategies including the building layout, envelop thermophysics, building geometry and infiltration & air-tightness, while output indices are corresponding indoor environmental indices of the daylight, natural ventilation and thermal comfort to fulfil current green building requirements. The variation of sampling size, application of response transformation and bootstrap method, as well as different statistical regression models are tested and validated through separate modelling datasets. A sampling size of 100 per regression coefficient is determined from the variation of sensitivity coefficients, coefficients of determination and prediction uncertainties. The rank transformation of responses can calibrate sensitivity coefficients of a non-linear model, by considering their variation obtained from sufficient bootstrapping replications. Furthermore, the acquired meta-model with MARS (Multivariate Adaptive Regression Splines) is proved to have better model fitting and predicting performances. This research can accurately identify important architectural design factors and make robust building performance predictions associated with the green building assessment. Sensitivity analysis results and obtained meta-models can improve the efficiency of future optimization studies by pruning the problem space and shorten the computation time.
Conference Paper
The introduction of ontological classification to support domain-metamodeling has been pivotal in the emergence of multi-level modeling as a dynamic research area. However, existing expositions of ontological classification have only used a limited context to distinguish it from the historically more commonly used linguistic classification. In important areas such as domain-specific languages and classic language engineering the distinction can appear to become blurred and the role of ontological classification is obscured, if not fundamentally challenged. In this paper we therefore examine critical points of confusion regarding the distinction and provide an expanded explanation of the differences. We maintain that optimally utilizing ontological classification, even for tasks that traditionally have only been viewed as language engineering, is critical for mastering the challenges in complex systems modeling including the validation of multi-language models.
Chapter
The vision of the Digital Twin itself refers to a comprehensive physical and functional description of a component, product or system, which includes more or less all information which could be useful in all—the current and subsequent—lifecycle phases. In this chapter we focus on the simulation aspects of the Digital Twin. Today, modelling and simulation is a standard process in system development, e.g. to support design tasks or to validate system properties. During operation and for service first simulation-based solutions are realized for optimized operations and failure prediction. In this sense, simulation merges the physical and virtual world in all life cycle phases. Current practice already enables the users (designer, SW/HW developers, test engineers, operators, maintenance personnel, etc) to master the complexity of mechatronic systems.