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Political Science has traditionally employed empirical research and analytical resources to understand, explain and predict political phenomena. One of the long-standing criticisms against empirical modeling targets the static perspective provided by the model-invariant paradigm. In political science research, this issue has a particular relevance since political phenomena prove sophisticated degrees of context-dependency whose complexity could be hardly captured by traditional approaches. To cope with the complexity challenge, a new modeling paradigm was needed. This book is concerned with this challenge. Moreover, the book aims to reveal the power of computational modeling of political attitudes to reinforce the political methodology in facing two fundamental challenges: political culture modeling and polity modeling. The book argues that an artificial polity model as a powerful research instrument could hardly be effective without the political attitude and, by extension, the political culture computational and simulation modeling theory, experiments and practice. This book: -Summarizes the state of the art in computational modeling of political attitudes, with illustrations and examples featured throughout.-Explores the different approaches to computational modeling and how the complexity requirements of political science should determine the direction of research and evaluation methods.-Addresses the newly emerging discipline of computational political science.-Discusses modeling paradigms, agent-based modeling and simulation, and complexity-based modeling.-Discusses model classes in the fundamental areas of voting behavior and decision-making, collective action, ideology and partisanship, emergence of social uprisings and civil conflict, international relations, allocation of public resources, polity and institutional function, operation, development and reform, political attitude formation and change in democratic societies. This book is ideal for students who need a conceptual and operational description of the political attitude computational modeling phases, goals and outcomes in order to understand how political attitudes could be computationally modeled and simulated. Researchers, Governmental and international policy experts will also benefit from this book.

The Law of Industrial Dynamics ensures that if a production control system can amplify then it will surely find a way of doing so despite the best efforts of production schedulers to take corrective action. In fact, practical studies show that such human intervention frequently aggravates the situation with both stock levels and order rates fluctuating alarmingly. The solution is to design an effective system via simulation. This requires the selection of the appropriate control system structure, agreement on the test cases to be used to mimic the operating environment, and finally setting the system parameters to achieve best performance for this scenario. Demonstrates a system which has three controllers utilizing sales, inventory and work in progress (WIP) data to set production order rates. The resulting decision support system (DSS) is a generic tool that can be used by production schedulers with confidence in the knowledge that the Law of Industrial Dynamics effects may be minimized. Simulation experiments can determine the best available trade‐off in any particular situation such as achieving the lean logistics aim of minimum reasonable inventory (MRI) while retaining high customer service levels (CSL). The experimental facility available within the simulation model includes provision for assessing the impact of variable production lead times and information delays on system performance. Describes a specific application of the DSS and the specific improvements in a company's performance. Places the DSS in the context of a case‐based reasoning environment in which a knowledge base of system structures and their dynamic properties is achieved. Outlines the opportunity of utilizing the DSS in uncertain lead‐time environments in a range of industry sectors.

Electric transport is rapidly gaining popularity across the world. It is an example of technological advancement that has multiple consequences for regional economies, both in terms of the adaptation of production, transport and energy systems and their spatial optimization. The experience of leading economic regions, including countries of the Baltic Sea region, shows that electric transport can potentially substitute traditional transport technologies. Based on an authentic model of system dynamics, the authors propose a new approach to simulation modelling of the dissemination of electric vehicles in a given region. The proposed model allows the authors to take into account the key systemic feedback loops between the pool of electric vehicles and the charging infrastructure. In the absence of data required for the econometric methods of demand forecasting, the proposed model can be used for the identification of policies stimulating the consumer demand for electric vehicles in regions and facilitating the development of the electric transport infrastructure. The proposed model has been tested using real and simulated data for the Kaliningrad region, which due to its specific geographical location, is a convenient test-bed for developing simulation models of a regional scale. The proposed simulation model was built via the AnyLogic software. The authors explored the capacity of the model, its assumptions, further development and application. The proposed approach to demand forecasting can be further applied for building hybrid models that include elements of agent modelling and spatial optimization.

Previous research suggests that developing dynamic models of business processes prior to their radical change could increase the success of BPR projects. Identifies barriers encountered in existing business processes and presents an overview of business process modelling methods that can be used to identify ways of eliminating these barriers. A case study is used to demonstrate how simulation modelling can be used to effectively re‐engineer manufacturing processes. The developed model is then manipulated, with results being generated to discover the possibilities of increasing the through‐put of the system. The usability of simulation modelling for evaluating alternative business process strategies is then investigated. Guidelines for achieving more widespread use of business process simulation are then proposed.

AbstractThe aim of this paper is to provide a conceptual basis of modeling of discrete event systems, that is, a guideline along which making models and their programs and modification of them are carried out.For this purpose we formulate the three‐phase approach that was proposed as a methodology of discrete event simulation. Using this methodology, analysts model static interactions between entities in the target system and then make a program that shows dynamic aspects of the system. The formulation, based on the mathematical general systems theory, shows what determines the dynamics of a discrete event system resulting from the program, what information is used and how they are related in models in the methodology.More concretely, a simulation program used in the three‐phase approach is formulated as a state space representation whose state space consists of queue and a set of internal records. Those records hold internally‐set occurrence times of activities in corresponding entities. The role which activity interaction diagrams play as static skeleton models in the methodology is explicitly shown in terms of its simulation program.

This work describes the ontology OSMO, i.e., an ontologization and extension of MODA, a workflow metadata standard that constitutes a mandatory requirement within a number of European calls and projects in the context of materials modelling. OSMO was developed within the Horizon 2020 project VIMMP (Virtual Materials Marketplace) and is part of a larger effort in ontology engineering driven by the European Materials Modelling Council, with the European Materials and Modelling Ontology (EMMO) as its core. As such, OSMO provides connections and alignments with other related domain ontologies in computational engineering, including the EMMO itself. This work summarizes the domain, purpose, and design choices underlying OSMO, commenting on the implementation of OSMO and its applications. ; This work was funded from the European Union's Horizon 2020 research and innovation programme through grant agreement no. 760907, Virtual Materials Marketplace; it was facilitated by activities of the Innovation Centre for Process Data Technology (Inprodat e.V.). The co-author M.T.H. acknowledges funding from DFG project no. 441926934, NFDI4Cat, within the NFDI programme of the German Joint Science Conference (GWK).

While simulation has a vast area of application, this textbook focuses on the use of simulation to analyse business processes. It provides an up-to-date coverage of all stages of the discrete-event simulation (DES) process, covering important areas such as conceptual modelling, modelling input data, verification and validation and simulation output analysis. The book is comprehensive yet uncomplicated, covering the theoretical aspects of the subject and the practical elements of a typical simulation project, demonstrated by cases, examples and exercises. It also shows how simulation relates to new developments in machine learning, big data analytics and conceptual modelling techniques. Guidance is provided on how to build DES models using the Arena, Simio and Simul8 simulation software, and tutorials for using the software are incorporated throughout. Simulation Modelling offers a uniquely practical and end-to-end overview of the subject, which makes it perfect required or recommended reading for advanced undergraduate and postgraduate students studying business simulation and simulation modelling as part of operations research, business analytics, supply chain management and computer science courses.

This work describes the ontology OSMO, i.e., an ontologization and extension of MODA, a workflow metadata standard that constitutes a mandatory requirement within a number of European calls and projects in the context of materials modelling. OSMO was developed within the Horizon 2020 project VIMMP (Virtual Materials Marketplace) and is part of a larger effort in ontology engineering driven by the European Materials Modelling Council, with the Elementary Multiperspective Material Ontology (EMMO) as its core. As such, OSMO provides connections and alignments with other related domain ontologies in computational engineering, including the EMMO itself. This work summarizes the domain, purpose, and design choices underlying OSMO, commenting on the implementation of OSMO and its applications. ; This work was funded from the European Union's Horizon 2020 research and innovation programme through grant agreement no. 760907, Virtual Materials Marketplace; it was facilitated by activities of the Innovation Centre for Process Data Technology (Inprodat e.V.). The co-author M.T.H. acknowledges funding from DFG project no. 441926934, NFDI4Cat, within the NFDI programme of the German Joint Science Conference (GWK).

Abstract

Background and Purpose: The aim of this paper is to present the influence of Industry 4.0 on the development of the new simulation modelling paradigm, embodied by the Digital Twin concept, and examine the adoption of the new paradigm via a multiple case study involving real-life R&D cases involving academia and industry.

Design: We introduce the Industry 4.0 paradigm, presents its background, current state of development and its influence on the development of the simulation modelling paradigm. Further, we present the multiple case study methodology and examine several research and development projects involving automated industrial process modelling, presented in recent scientific publications and conclude with lessons learned.

Results: We present the research problems and main results from five individual cases of adoption of the new simulation modelling paradigm. Main lesson learned is that while the new simulation modelling paradigm is being adopted by big companies and SMEs, there are significant differences depending on company size in problems that they face, and the methodologies and technologies they use to overcome the issues.

Conclusion: While the examined cases indicate the acceptance of the new simulation modelling paradigm in the industrial and scientific communities, its adoption in academic environment requires close cooperation with industry partners and diversification of knowledge of researchers in order to build integrated, multi-level models of cyber-physical systems. As shown by the presented cases, lack of tools is not a problem, as the current generation of general purpose simulation modelling tools offers adequate integration options.

Simulationen stellen eine relevante Forschungsmethode in Controlling und Finanzwissen-schaft dar, diese Dissertation trägt an verschiedenen Stellen zu dieser Methodik bei. Erstens wird der Einfluss der Methodik auf das Forschungsfeld bibliometrisch quantifiziert und die Art der Nutzung sowie das Ausmaß der Diffusion in Forschungsclustern erfasst. Darüber hinaus trägt diese Dissertation eine auf Bayesscher Statistik beruhende Methodik zur Eingangsmodellierung von Simulationen bei. Zuletzt führt diese Dissertation eine neue Messgröße ein, die das Risiko in Simulationsmodellen von epistemologischer Unsicherheit in einer einzigen Zahl quantifiziert.