Ass. Wr. Wb. SIMULATION (Soemarno2014
Simulation A simulation is an imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviours of a selected physical or abstract system. Historically, the word had negative connotations: …for Distinction Sake, a Deceiving by Words, is commonly called a Lye, and a Deceiving by Actions, Gestures, or Behavior, is called Simulation… Robert South (1643–1716) However, the connection between simulation and dissembling later faded out and is now only of linguistic interest.
Simulation is used in many contexts, including the modeling of natural systems or human systems in order to gain insight into their functioning. Other contexts include simulation of technology for performance optimization, safety engineering, testing, training and education. Simulation can be used to show the eventual real effects of alternative conditions and courses of action. Key issues in simulation include acquisition of valid source information about the referent, selection of key characteristics and behaviours, the use of simplifying approximations and assumptions within the simulation, and fidelity and validity of the simulation outcomes.
Classification and terminology Historically, simulations used in different fields developed largely independently, but 20th century studies of Systems theory and Cybernetics combined with spreading use of computers across all those fields have led to some unification and a more systematic view of the concept. Physical and interactive simulation Physical simulation refers to simulation in which physical objects are substituted for the real thing. These physical objects are often chosen because they are smaller or cheaper than the actual object or system. Interactive simulation is a special kind of physical simulation, often referred to as a human in the loop simulation, in which physical simulations include human operators, such as in a flight simulator or a driving simulator.
Computer simulation A computer simulation is an attempt to model a real-life or hypothetical situation on a computer so that it can be studied to see how the system works. By changing variables, predictions may be made about the behaviour of the system. An interesting application of computer simulation is to simulate computers using computers. The related software is called computer architecture simulators, which can be further divided into instruction set simulators or full system simulators.
Computer simulation has become a useful part of modeling many natural systems in physics, chemistry and biology, and human systems in economics and social science (the computational sociology) as well as in engineering to gain insight into the operation of those systems. A good example of the usefulness of using computers to simulate can be found in the field of network traffic simulation. In such simulations the model behaviour will change each simulation according to the set of initial parameters assumed for the environment. Computer simulations are often considered to be human out of the loop simulations.
Traditionally, the formal modeling of systems has been via a mathematical model, which attempts to find analytical solutions enabling the prediction of the behaviour of the system from a set of parameters and initial conditions. Computer simulation is often used as an adjunct to, or substitution for, modeling systems for which simple closed form analytic solutions are not possible. There are many different types of computer simulation, the common feature they all share is the attempt to generate a sample of representative scenarios for a model in which a complete enumeration of all possible states would be prohibitive or impossible.
Various industries use discrete event simulation to model systems of interest in commerce, health, defence, manufacturing, logistics etc., for example the value-adding business processes. Imagine a business, where each person could do 30 tasks, where thousands of products or services involved dozens of tasks in a sequence, where customer demand varied seasonally and forecasting was inaccurate — this is the domain where such simulation helps with business decisions across all functions.
Related topics include Theory of Constraints, bottlenecks, and management consulting. Several software packages exist for running computer-based simulation modeling (e.g. Monte Carlo simulation and stochastic modeling) that makes the modeling almost effortless. It is increasingly common to hear simulations of many kinds referred to as "synthetic environments". This label has been adopted to broaden the definition of "simulation" to encompass virtually any computer-based representation.
Simulation in computer science In computer science, simulation has an even more specialized meaning: Alan Turing uses the term "simulation" to refer to what happens when a digital computer runs a state transition table (runs a program) that describes the state transitions, inputs and outputs of a subject discrete-state machine. The computer simulates the subject machine. In computer architecture, a simulator is often used to execute a program that has to run on some inconvenient type of computer, or in a tightly controlled testing environment. For example, simulators are usually used to debug a microprogram or sometimes commercial application programs. Since the operation of the computer is simulated, all of the information about the computer's operation is directly available to the programmer, and the speed and execution of the simulation can be varied at will.
Simulators may also be used to interpret fault trees, or test VLSI logic designs before they are constructed. Symbolic simulation that uses variables to stand for unknown values. In theoretical computer science the term simulation represents a relation between state transition systems. This is useful in the study of operational semantics. In the field of optimization, simulations of physical processes are often used in conjunction with evolutionary computation to optimize control strategies.
Simulation in training Simulation is often used in the training of civilian and military personnel. This usually occurs when it is prohibitively expensive or simply too dangerous to allow trainees to use the real equipment in the real world. In such situations they will spend time learning valuable lessons in a "safe" virtual environment. Often the convenience is to permit mistakes during training for a safety-critical system.
Training simulations typically come in one of three categories: "live" simulation (where real people use simulated (or "dummy") equipment in the real world); "virtual" simulation (where real people use simulated equipment in a simulated world (or "virtual environment")), or "constructive" simulation (where simulated people use simulated equipment in a simulated environment). Constructive simulation is often referred to as "wargaming" since it bears some resemblance to table-top war games in which players command armies of soldiers and equipment that move around a board.
Examples in different areas Truck Simulator A truck simulator provides an opportunity to reproduce the characteristics of real vehicles in a virtual environment. It replicates the external factors and conditions with which a vehicle interacts enabling a driver to feel as if they are sitting in the cab of their own vehicle. Scenarios and events are replicated with sufficient reality to ensure that drivers become fully immersed in the experience rather than simply viewing it as an educational programme.
The simulator provides a constructive experience for the novice driver and enables more complex exercises to be undertaken by the more mature driver. For novice drivers, truck simulators provide an opportunity to begin their career by applying best practice. For mature drivers, simulation provides the ability to enhance good driving or to detect poor practice and to suggest the necessary steps for remedial action. For companies, it provides an opportunity to educate staff in the driving skills that achieve reduced maintenance costs, improved productivity and, most importantly, to ensure the safety of their actions in all possible situations.
Healthcare (Clinical) Simulators Medical simulators are increasingly being developed and deployed to teach therapeutic and diagnostic procedures as well as medical concepts and decision making to personnel in the health professions. Simulators have been developed for training procedures ranging from the basics such as blood draw, to laparoscopic surgery and trauma care. They are also important to help on prototyping new devices for biomedical engineering problems. Currently, simulators are applied to research and development of tools for new therapies, treatments and early diagnosis in medicine.
Many medical simulators involve a computer connected to a plastic simulation of the relevant anatomy. Sophisticated simulators of this type employ a life size mannequin that responds to injected drugs and can be programmed to create simulations of life-threatening emergencies. In others simulations, visual components of the procedure are reproduced by computer graphics techniques, while touch-based components are reproduced by haptic feedback devices combined with physical simulation routines computed in response to the user's actions.
Medical simulations of this sort will often use 3D CT or MRI scans of patient data to enhance realism. Some medical simulations are developed to be widely distributed (such as web-enabled simulations that can be viewed via standard web browsers) and can be interacted with using standard computer interfaces, such as the keyboard and mouse. Another important medical application of a simulator — although, perhaps, denoting a slightly different meaning of simulator — is the use of a placebo drug, a formulation that simulates the active drug in trials of drug efficacy.
History of Simulation in Healthcare The first medical simulators were simple models of human patients. Since antiquity, these representations in clay and stone were used to demonstrate clinical features of disease states and their effects on humans. Models have been found from many cultures and continents. These models have been used in some cultures (e.g., Chinese culture) as a "diagnostic" instrument, allowing women to consult male physicians while maintaining social laws of modesty. Models are used today to help students learn the anatomy of the musculoskeletal system and organ systems.
Active models Active models that attempt to reproduce living anatomy or physiology are recent developments. The famous “Harvey” mannikin was developed at the University of Miami and is able to recreate many of the physical findings of the cardiology examination, including palpation, auscultation, and electrocardiography. Interactive models More recently, interactive models have been developed that respond to actions taken by a student or physician. Until recently, these simulations were two dimensional computer programs that acted more like a textbook than a patient. Computer simulations have the advantage of allowing a student to make judgements, and also to make errors. The process of iterative learning through assessment, evaluation, decision making, and error correction creates a much stronger learning environment than passive instruction.
Computer simulators Simulators have been proposed as an ideal tool for assessment of students for clinical skills. Programmed patients and simulated clinical situations, including mock disaster drills, have been used extensively for education and evaluation. These “lifelike” simulations are expensive, and lack reproducibility. A fully functional "3Pi" simulator would be the most specific tool available for teaching and measurement of clinical skills. Such a simulator meets the goals of an objective and standardized examination for clinical competence. This system is superior to examinations that use "standard patients" because it permits the quantitative measurement of competence, as well as reproducing the same objective findings.
The "classroom of the future" The "classroom of the future" will probably contain several kinds of simulators, in addition to textual and visual learning tools. This will allow students to enter the clinical years better prepared, and with a higher skill level. The advanced student or postgraduate will have a more concise and comprehensive method of retraining — or of incorporating new clinical procedures into their skill set — and regulatory bodies and medical institutions will find it easier to assess the proficiency and competency of individuals. The classroom of the future will also form the basis of a clinical skills unit for continuing education of medical personnel; and in the same way that the use of periodic flight training assists airline pilots, this technology will assist practitioners throughout their career. The simulator will be more than a "living" textbook, it will become an integral a part of the practice of medicine. The simulator environment will also provide a standard platform for curriculum development in institutions of medical education.
Finance In finance, computer simulations are often used for scenario planning. Risk-adjusted net present value, for example, is computed from well-defined but not always known (or fixed) inputs. By imitating the performance of the project under evaluation, simulation can provide a distribution of NPV over a range of discount rates and other inputs. City Simulators / Urban Simulation A City Simulator can be a game but can also be a tool used by urban planners to understand how cities are likely to evolve in response to various policy decisions. UrbanSim (developed at the University of Washington), ILUTE (developed at the University of Toronto) and Distrimobs (developed at the University of Bologna) are examples of modern, large-scale urban simulators designed for use by urban planners. City simulators are generally agent-based simulations with explicit representations for land use and transportation.
Flight simulators A flight simulator is used to train pilots on the ground. It permits a pilot to crash his simulated "aircraft" without being hurt. Flight simulators are often used to train pilots to operate aircraft in extremely hazardous situations, such as landings with no engines, or complete electrical or hydraulic failures. The most advanced simulators have high-fidelity visual systems and hydraulic motion systems. The simulator is normally cheaper to operate than a real trainer aircraft.
Home-built Flight Simulators Some people who use simulator software, especially flight simulator software, build their own simulator at home. Some people in order to further the realism of their homemade simulator, buy used cards and racks that still run the exact same software they did before they were disassembled from the actual machine itself. Though this brings along the problem of matching hardware and software, and the fact that hundreds of cards plug into many different racks, still, many find that is it well worth it. Some are very serious in building their simulator by buying real aircraft parts like complete nose sectionals of written off aircraft at aircraft boneyards. This permits people who are unable to perform their hobby in real life to simulate it.
Marine simulators Bearing resemblance to flight simulators, marine simulators train a ships' personnel. Simulators like these are mostly used to simulate large or complex vessels, such as cruiseships and dredging ships. They often consist of a replication of a ships' bridge, with operating desk(s), and a number of screens on which the virtual surroundings are projected.
Engineering (Technology) simulation or Process simulation Simulation is an important feature in engineering systems or any system that involves many processes. For example in electrical engineering, delay lines may be used to simulate propagation delay and phase shift caused by an actual transmission line. Similarly, dummy loads may be used to simulate impedance without simulating propagation, and is used in situations where propagation is unwanted. A simulator may imitate only a few of the operations and functions of the unit it simulates. Contrast with: emulate.
Most engineering simulations entail mathematical modeling and computer assisted investigation. There are many cases, however, where mathematical modeling is not reliable. Simulation of fluid dynamics problems often require both mathematical and physical simulations. In these cases the physical models require dynamic similitude. Physical and chemical simulations have also direct realistic uses, rather than research uses; in chemical engineering, for example, process simulations are used to give the process parameters immediately used for operating chemical plants, such as oil refineries.
Simulasi Dan Game Strategy games — both traditional and modern — may be viewed as simulations of abstracted decision-making for the purpose of training military and political leaders (see History of Go for an example of such a tradition). In a narrower sense, many video games are also simulators, implemented inexpensively. These are sometimes called "sim games". Such games can simulate various aspects of reality, from economics to piloting vehicles, such as flight simulators (described above). Another type of simulation is a government simulation, which can be used to help the player understand certain aspects of political science — specifically cause and effect.
Simulation in education Simulations in education are somewhat like training simulations. They focus on specific tasks. In the past, video has been used for teachers and education students to observe, problem solve and role play; however, a more recent use of simulations in education include animated narrative vignettes (ANV). ANVs are cartoon-like video narratives of hypothetical and reality-based stories involving classroom teaching and learning. ANVs have been used to assess knowledge, problem solving skills and dispositions of children, and pre-service and in-service teachers.
Another form of simulation has been finding favour in business education in recent years. Business simulations that incorporate a dynamic model enables experimentation with business strategies in a risk free environment and provide a useful extension to case study discussions.
Analytic Hierarchy Process Analytic Hierarchy Process (AHP) adalah teknik pengambilan keputusan yang dikembangkan oleh Thomas Saaty. Ia menyatakanbahwaAHP memungkinkan untuk evaluasi secararasional “pro” dan “kontra” mengenai berbagaialternatif solusiuntuk permasalahan yang bersifat multi-tujuan. AHP didasarkan pada serangkaian pembandingan-berpasangan dan kemudian pembandingan tersebutdiperiksa untuk mengujikonsistensi internalnya.
The procedure can be summarized as: Decision makers are asked their preferences of attributes of alternatives. For example, if the alternatives are comparing potential real-estate purchases, the investors might say they prefer location over price and price over timing. Then they would be asked if the location of alternative "A" is preferred to that of "B", which has the preferred timing, and so on.
This creates a matrix which is evaluated by using eigenvalues to check the consistency of the responses. This produces a "consistency coefficient" where a value of "1" means all preferences are internally consistent. This value would be lower, however, if decision makers said X is preferred to Y, Y to Z but Z is preferred to X (such a position is internally inconsistent). It is this last step that that causes many users to believe that AHP is theoretically well founded.
KRITIK AHP AHP memiliki banyak tantangan darisudutpandangteoritis dan praktisnya, dibandingkandenganmetodepengambilankeputusan lain yang lebihkuatlandasanteorinya.Beberapa ilmuwantelah menyatakan bahwa AHP menjadisarana “sewenang-wenang” atau “ordinal”untukpembandingan berpasangan. Para pendukung AHP mempertahankan bahwa meskipun AHP bersifat verbal, telah terbuktibahwadalamsituasidimana ada banyhakragamdanredundansi, sekalaprioritas yang cukupakurat dapat diturunkan dari “Judgment” verbal sepertiitu.
Proponents claim that it could be used by Aircraft engineers to evaluate alternative wing designs and actuaries can use it to evaluate risks. However, in those fields specific models already exist that make AHP unnecessary and innacurate. AHP, for example, cannot compute the value of a premium in the way that an actuary does. Such methods have to use specific mathematical theorems unique to that field.
AHP, like many systems based on pairwise comparisons, can produce "rank reversal" outcomes. That is a situation where the order of preference is, for example, A, B, C then D. But if C is eliminated for other reasons, the order of A and B could be reversed so that the resulting priority is then B, A, then D. It has been proven that any pairwise comparison system will still have rank-reversal solutions even when the pair preferences are consistent . Proponents argue that rank reversal may still be desirable but this is also controversial. Given the example, this would be the position that if C were elliminated, the preference of A over B should be switched.
Another strong theoretical problem of AHP was found by Perez, et. al. . This has to do with what they identify as an "indiferent criterion" flaw. Indiferent criterion requires that once A, B, C and D are ranked according to criteria, say, W, X, Y, adding another criterion for which A, B, C, and D are equal, should have no bearing on the ranks. Yet, Perez et al prove that such an outcome is possible. Note that this flaw, too, is a shortcoming of any pairwise comparison process, not just AHP. But AHP's consistency-checking methods offer no guarantee such flaws cannot occur, since there are solution sets with these flaws even when preferences are consistent.
Many alternatives to AHP are economically viable, especially for larger, riskier decision. Methods from decision theory and various economic modeling methods can be applied. A scoring method that has a superior track record of improving decisions was developed by EgonBrunswik in the 1950's. Other methods such as Applied Information Economics quantify risk, cost and value in economically meaningful terms even where AHP considers them to be "immeasurable".
References • Dyer, J. S. (1990): Remarks on the Analytic Hierarchy Process. In: Management Science, 36 (3), S. 249-258. • Simon French "Decision Theory: An Introduction to the Mathematics of Rationality", Ellis Horwood, Chichester, 1988. • J. Perez, J. Jimeno, E. Mokotoff, Another Potential Strong Shortcoming of AHP, Department of Economics, University of Alcala Spain