Simulation

6 Key excerpts on "Simulation"

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  eBook - PDF

  Products and Services

  from R and D to Final Solutions

  • Igor Fuerstner(Author)
  • 2010(Publication Date)
  • IntechOpen

    (Publisher)

  Simulation Modelling of Manufacturing Business Systems 81 The major reference value in production management is the product quantity and the time by which the product ought to have been finalized and made available. 4. Simulation modelling of manufacturing business systems Using Simulations in a business environment is determined by the goals of a business system. If we assume that the general goal of each business system is its survival in the market and business growth, as well as the development of the system, then Simulations will be directed at improving the execution of business processes, attempting to forecast future situations and identify critical success factors for achieving the desired business goals (Visawan&Tannock, 2004). In literature on Simulation various authors propose different definitions of the concept of Simulation, which depends on the area covered in a particular book or paper or a specific field of interest of the author. While some of them interpret Simulation as a representation of systems dynamics, a mathematically grounded numerical technique, experimentation or computer software operations, others define it as a process or a process modelling technique. In general, it can be said that Simulation modelling is mimicking of a real system by means of scientific methods (probability theory, statistics and operations research) and contemporary information technologies. It is in this way that the Simulation process and its capabilities are most precisely defined. Simulation modelling methods are a powerful means of achieving qualitative changes. Working with models makes it possible to view business systems dynamics, which means that individual action scenarios can be generated and the system fine-tuned in short time segments (Manzini et all, 2005). Each system state described by a conceptual model contains formally determined parameters and conditions which describe it.

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  Assessment of Problem Solving Using Simulations

  • Eva Baker, Jan Dickieson, Wallace Wulfeck, Harold F. O'Neil(Authors)
  • 2017(Publication Date)
  • Routledge

    (Publisher)

  Simulations and games have been used in the context of computer-based learning since the 1960s. They have depicted a specific phenomenon within a model that is executable on a computer. This chapter refers to phenomena in economic contexts, particularly enterprises competing with each other in a simulated market. The model of the enterprises and the market represents the complexity and dynamics of business structures and concepts the participants have to cope with within the Simulation. The use of business Simulations can target different objectives (Breuer, Molkenthin, & Tennyson, 2006; Lainema & Nurmi, 2006):

  • Acquisition of structural knowledge

  • Development of domain-specific problem-solving competencies

  • Elaboration of holistic views toward complex phenomena

  • Fostering meta-cognitive competencies (self-regulation and self-monitoring; O’Neil, 2002)

  • Support for the ability of role-taking

  • Buildup of the ability for coping with dynamics

  The traditional concept of using business Simulations can be divided into three phases: briefing, Simulation or gaming, and debriefing (Capaul, 2001). The three phases are usually moderated and supported by one or more teachers (= moderator). Within the first phase (briefing), the participants receive an introduction to the structure and the rules within the model. This is necessary to activate prior knowledge, which can be referred to by the participants. Within the briefing, the participants are confronted with a problem they ought to solve within the follow-up Simulation, for example, to gain a higher market share within a growing market. Because learning with business Simulation refers to the paradigm of problem-based learning, the selection of an appropriate problem is an important aspect (Tennyson & Breuer, 2002). The participants should elaborate their mental model about the concepts represented within the Simulation model by solving a given problem; that is, the appropriate knowledge to solve the problem is not available at this point in time. Otherwise, from the perspective of the participants, the presented “problem” would be a task only. In this situation, learning takes place in the sense of automating existing knowledge.

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  Risk, Crisis and Security Management

  • Edward P. Borodzicz(Author)
  • 2015(Publication Date)
  • Wiley

    (Publisher)

  Representation then is effected by means of abstraction from a source reality. This is achieved by modelling that system on the basis of a selection of conceived features: the essential characteristics, rules and strategies (processes). In this context, the modelled Simulation has the qualities of a low-cost error model, in comparison to a real system where errors can be expensive or even disastrous. Participants in a simulated environment are hence able to make errors in relative safety and learn by them. In reality this process may be more of an art than a science. Deciding upon the correct set of features to model may be a somewhat ambiguous and problematic task, particularly with subjective social phenomena. A second common feature to Simulations is that they simulate certain aspects of reality at the expense of ignoring others (Abt, 1970). This is due to purely pragmatic reasons. If a Simulation were to offer all the aspects of the real situation from which it had been abstracted, then it would no longer be a Simulation, but the source reality itself. In this respect, Simulation scenarios can be seen to offer an ethical and pragmatic alternative to an unacceptable reality. This reality may be unacceptable for moral and/or ethical reasons (as will be discussed later in this chapter), or on economic or safety grounds. For example, it would be quite unethical to train a pilot for emergency flight procedures in a real jet aircraft full of passengers, or to really commit murder in a classical Greek tragedy for effect. It may also be physically impossible to create the real situation that the simulator wishes to experience, such as a thermonuclear explosion in outer space or travelling at the speed of light. A third common feature of Simulations is that they create dynamic realities by their own production.

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  Discrete and Continuous Simulation

  Theory and Practice

  • Susmita Bandyopadhyay, Ranjan Bhattacharya(Authors)
  • 2014(Publication Date)
  • CRC Press

    (Publisher)

  159 9 Computer Simulation 9.1 INTRODUCTION Computer Simulation is a computer process as well as a Simulation tool. In Chapters 2 and 10 through 20, we introduce several aspects of Simulation study, and in all cases, a number of software programs are shown in the respective fields of study. But before developing a computerized Simulation model, there is always a stage of develop-ing an idealized model. This idealized model helps in building the real Simulation model. In case of computer Simulation, the visualization of the model by using a graphi-cal user interface (GUI) is an important aspect. Moreover, visual Simulation is a vast area of study. The elements of visual Simulation are rendering, display mod-eling, model creation, and animation modeling. There are several basic languages that are used in this regard. In addition, there are general-purpose languages for Simulations, such as MATLAB ® , and languages under Visual Studio .NET. The other sets of languages, also known as object-oriented languages, include C ++ , Java, Python, Smalltalk, and so on. The main characteristics of object-oriented languages are inheritance, encapsulation, and polymorphism. Simulation may be physical Simulation, in which a small prototype of the actual larger system is built, or logic Simulation, which generally uses a computer to imple-ment the Simulation model. Thus, logic Simulations in all fields of study consist of built-in software systems used to simulate real-world phenomena. Simulation stud-ies may be useful in concept building, providing open-ended experiences to the prac-titioners and tools for scientific methods, and helping in solving various real-world problems. Computer Simulation may even be helpful in distance education. Computer Simulation simulates several concepts in various fields of study, and accordingly, there are Simulations in economics, chemistry, physics, biology, and so on. Thus, computer Simulation is also used for various purposes.

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  Foundations of Nanotechnology, Volume Two

  Nanoelements Formation and Interaction

  • Sabu Thomas, Saeedeh Rafiei, Shima Maghsoodlou, Arezo Afzali(Authors)
  • 2014(Publication Date)
  • Apple Academic Press

    (Publisher)

  As in chess, the winner is the one who can visualize more moves and scenarios before the opponent does. In business the same idea holds. Making decisions on impulse can be very dangerous, yet if the idea is envisaged on a computer monitor then no harm is really done, and the problem is diagnosed before it even happens. Diagnosing problems is the fifth reason why people need to simulate. Likewise, the sixth reason tackles the same aspect of identifying con-straints and predicting obstacles that may arise, and is considered as one major factor why businesses buy Simulation software. The seventh reason addresses the fact that many times decisions are made based on “someone’s thought” rather than what is really happening. 110 Foundations of Nanotechnology: Nanoelements Formation and Interaction When studying some Simulation packages, the model can be viewed in 3-D. This animation allows the user “to detect design flaws within systems that appear credible when seen on paper or in a 2-D CAD drawing.” The ninth incentive for Simulation is to “visualize the plan.” It is much easier and more cost effective to make a decision based on predictable and distinguished facts. Yet, it is a known fact that such luxury is scarce in the business world [6]. Nevertheless, before trying out the “what if” scenario many would rather have the safety net beneath them. Therefore, Simulation is used for “preparing for change” [7]. In addition, the 13th reason is evidently trying different scenarios on a simulated environment; proving to be less expensive, as well as less disturb-ing, than trying the idea in real life. Therefore, Simulation software does save money and effort, which denotes a wise investment. In any field listing the requirements can be of tremendous effort, for the simple reason that there are so many of them.

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  UKSC 84

  Proceedings of the 1984 UKSC Conference on Computer Simulation

  • D.J. Murray-Smith(Author)
  • 2014(Publication Date)
  • Butterworth-Heinemann

    (Publisher)

  326. 1. INTRODUCTION Simulation models are used to enhance the state of our knowledge or, at least, beliefs about some process. By all generally accepted definitions, a Simulation is a symbolic or numerical abstraction of the process under study and is not the process itself. Thus learning from a Simulation requires two stages. The first is to understand the behaviour of the model itself in terms of the relations that exist between inputs and outputs. The second, and often more difficult task, is to translate learning from the Simulation to learning about the actual process. The second task, the translation of learning from a model to actual process is generally viewed as the focus of the validation process /16/. 2. REVIEW OF VALIDATION AND VERIFICATION THEORIES AND PROCEDURES The validation of computer Simulation models has received considerable attention in the literature in the last decade, but of the various stages of modelling it remains the last developed in terms of agreed procedures /5/. Despite the extensive use of Simulation in systems analysis, many sceptics have well-founded reservations concerning its merit. There is an impressive array of things that can go wrong in a Simulation study /3/. The problem of validating a model is to ensure that the model is a valid model of the real world, so that conclusions and inferences obtained from experiments on the model can be applied to the real world HI . J Browne /l/ points out that validation is important because of the nature of computer models, and that simulators conceal their assumptions, because of the tendency to regard the actual model as a 'black box 1 . Naylor, Balintfy, Burdick and Chu /10/ outline the two major methodological positions concerning the problem of validation. These are the Synthetic a priorism and the Ultra empiricism.

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