This document provides an introduction to modeling and simulation. It discusses the goals of modeling, different types of models, and an overview of the simulation process. The key steps in simulation include defining an achievable goal, ensuring appropriate skills and involvement from end users, choosing simulation tools, validating the model, and analyzing statistical output. Pitfalls to avoid include lack of clear objectives, inappropriate model detail, and failure to validate models or account for randomness.
Introduction to simulation and modeling will describe what is simulation, what is system and what is model. It will give a brief overview of simulation and modeling in computer science.
Computer models and simulations are used to predict how systems will behave without having to create physical systems. They use mathematical formulas and past data to mimic real-life situations. While not perfectly accurate, models allow testing of systems like cars, weather patterns, bridges and businesses in a safe, cost-effective manner. Examples given include using models to design safer cars, forecast weather, test bridge designs, predict business profits, and train pilots via realistic flight simulators.
System modeling and simulation full notes by sushma shetty (www.vtulife.com)Vivek Maurya
This document provides an overview of system simulation and modelling. It discusses when simulation is an appropriate tool and areas it can be applied, including manufacturing, semiconductors, construction, the military, logistics and healthcare. Simulation allows experimenting with systems to study their behavior over time without disrupting operations. It enables testing new designs and policies. The document also covers system components like entities, attributes, and activities, and defines a system and its environment.
introduction to modeling, Types of Models, Classification of mathematical mod...Waqas Afzal
Types of Systems
Ways to study system
Model
Types of Models
Why Mathematical Model
Classification of mathematical models
Black box, white box, Gray box
Lumped systems
Dynamic Systems
Simulation
Simulation is used to create models that represent real world systems and allow experimenting with different strategies without impacting the actual system. Models simplify real systems for analysis while maintaining key behaviors and results. Successful simulation models are easy to understand, represent the system accurately, produce fast results, and allow control and updating. Simulators are used when real experimentation is unsafe, too expensive, or when systems are still in development. Common uses of simulation include modeling systems in fields like military, education, healthcare, and engineering.
The information in this slide is very useful for me to do the assignment regarding the simulation in which we have to report together with the presentation...
The document discusses simulation as a technique for modeling real-world systems with uncertain inputs. It defines simulation as using models to represent systems over time to understand their behavior. The key aspects covered include:
- Components of a simulation model including inputs, calculations, and outputs
- Types of simulation like time-dependent vs time-independent and corporate/financial simulations
- Major applications in queuing systems and analyzing waiting times
- Steps of the simulation process from identifying the problem to evaluating results
- Components and structures of queuing systems like arrivals, queues, service, and departure.
This document provides an introduction to modeling and simulation. It discusses the goals of modeling, different types of models, and an overview of the simulation process. The key steps in simulation include defining an achievable goal, ensuring appropriate skills and involvement from end users, choosing simulation tools, validating the model, and analyzing statistical output. Pitfalls to avoid include lack of clear objectives, inappropriate model detail, and failure to validate models or account for randomness.
Introduction to simulation and modeling will describe what is simulation, what is system and what is model. It will give a brief overview of simulation and modeling in computer science.
Computer models and simulations are used to predict how systems will behave without having to create physical systems. They use mathematical formulas and past data to mimic real-life situations. While not perfectly accurate, models allow testing of systems like cars, weather patterns, bridges and businesses in a safe, cost-effective manner. Examples given include using models to design safer cars, forecast weather, test bridge designs, predict business profits, and train pilots via realistic flight simulators.
System modeling and simulation full notes by sushma shetty (www.vtulife.com)Vivek Maurya
This document provides an overview of system simulation and modelling. It discusses when simulation is an appropriate tool and areas it can be applied, including manufacturing, semiconductors, construction, the military, logistics and healthcare. Simulation allows experimenting with systems to study their behavior over time without disrupting operations. It enables testing new designs and policies. The document also covers system components like entities, attributes, and activities, and defines a system and its environment.
introduction to modeling, Types of Models, Classification of mathematical mod...Waqas Afzal
Types of Systems
Ways to study system
Model
Types of Models
Why Mathematical Model
Classification of mathematical models
Black box, white box, Gray box
Lumped systems
Dynamic Systems
Simulation
Simulation is used to create models that represent real world systems and allow experimenting with different strategies without impacting the actual system. Models simplify real systems for analysis while maintaining key behaviors and results. Successful simulation models are easy to understand, represent the system accurately, produce fast results, and allow control and updating. Simulators are used when real experimentation is unsafe, too expensive, or when systems are still in development. Common uses of simulation include modeling systems in fields like military, education, healthcare, and engineering.
The information in this slide is very useful for me to do the assignment regarding the simulation in which we have to report together with the presentation...
The document discusses simulation as a technique for modeling real-world systems with uncertain inputs. It defines simulation as using models to represent systems over time to understand their behavior. The key aspects covered include:
- Components of a simulation model including inputs, calculations, and outputs
- Types of simulation like time-dependent vs time-independent and corporate/financial simulations
- Major applications in queuing systems and analyzing waiting times
- Steps of the simulation process from identifying the problem to evaluating results
- Components and structures of queuing systems like arrivals, queues, service, and departure.
This document discusses different types of simulation models. It describes:
1) Static vs dynamic models, with dynamic models changing over time and static models as snapshots.
2) Deterministic vs stochastic vs chaotic models, depending on how predictable the behavior is.
3) Discrete vs continuous models, with discrete changing at countable points and continuous changing continuously.
4) Aggregate vs individual models, with aggregate models taking a more distant view and individual models a closer view of decisions.
Simulation involves imitating the operation of a real-world process over time, usually on a computer. It is widely used for decision making and analyzing complex systems that cannot be solved mathematically. A simulation study involves problem formulation, model conceptualization, validation, experimentation, and implementation. Key aspects of a model include entities, attributes, resources, variables, events, and activities.
This document provides an overview of simulation and discrete event simulation. It discusses different types of models including static/dynamic, deterministic/stochastic, and discrete/continuous. It also describes three approaches to discrete event simulation: activity-oriented, event-oriented, and process-oriented. The document outlines several popular simulators including CSIM, GloMoSim, NS-2, and NCTU-NS. It concludes with references for further reading on simulation and these simulators. Mini-projects and projects are proposed for using GloMoSim and developing a MAC simulator using PARSEC, respectively.
System modeling and simulation involves creating simplified representations of real-world systems to understand and evaluate their behavior over time. A system is composed of interconnected parts designed to achieve specific objectives. A model abstracts and simplifies a system for analysis. Simulation executes a model over time to observe how a system operates. It allows experimenting with systems that may be too expensive, dangerous or complex to study directly. Simulation has many uses including analyzing systems before implementation, optimizing designs, training, and evaluating "what-if" scenarios. Key areas where simulation is applied include manufacturing, business, healthcare, transportation and the military.
Simulation involves developing a model of a real-world system over time to analyze its behavior and performance. The key aspects covered in this document include defining simulation as modeling the operation of a system over time through artificial history generation and observation. Simulation models can be used as analysis and design tools to predict the effects of changes to a system before actual implementation. Discrete event simulation is discussed as a common technique that models systems with state changes occurring at discrete points in time. The document also outlines the steps in a typical simulation study including problem formulation, model conceptualization, experimentation and analysis.
This presentation introduces discrete-event simulation software. It discusses what discrete-event simulation is, how it models systems as sequences of events over time. It covers the basic constructs like entities, resources, control elements and operations. It explains how simulation execution advances by processing the next event. It discusses entity states like active, ready, time-delayed and conditional-delayed. It also summarizes different implementations in discrete-event modeling languages and tools like Arena and AutoMod.
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Simulation involves modeling real-life situations and processes to analyze them or visualize outcomes. There are several types of simulations including static vs dynamic and deterministic vs stochastic. The simulation process involves defining the problem, collecting data, building a model, experimenting, and interpreting results. Simulation is widely used in fields like engineering, manufacturing, business, and government to study complex systems without having to create physical models. While simulation provides benefits like controlling variables and identifying issues, limitations include not guaranteeing optimal solutions and difficulty interpreting random outputs.
This document provides an introduction to discrete event simulation. It discusses key concepts like systems, processes, states, activities, continuous vs discrete vs hybrid systems, deterministic vs stochastic systems, and when simulation is an appropriate tool. It also outlines the steps in a simulation study from problem formulation to implementation. An example queueing simulation is presented to illustrate tracking customers over time. Random number generation techniques like the linear congruential method are introduced. Desired properties of pseudorandom numbers for simulation are discussed.
System simulation & modeling notes[sjbit]qwerty626
The document discusses simulation modeling and provides an introduction to the topic. It defines simulation as imitating the operation of a real-world process over time. Simulation models take the form of assumptions expressed mathematically or logically about the relationships between system entities. Simulation is appropriate when a system is too complex to understand through other means or to experiment with system changes safely. The document outlines the components of a system and types of models. It also describes the basic steps involved in a simulation study from problem formulation to implementation.
This document discusses different approaches for learning with complete data, including:
1) Parameter learning aims to find numerical parameters for a fixed probability model given complete data for all variables.
2) Maximum likelihood parameter learning derives parameter expressions as log terms and finds values by equating logs to 0.
3) Naive Bayes models assume attributes are conditionally independent, and truth is not representable as a decision tree.
4) Continuous models represent real-world applications using linear Gaussian models that minimize sum of squared errors via standard linear recursion.
Simulation involves modeling real-world processes or systems on a computer in order to evaluate scenarios and outcomes that would otherwise be difficult to comprehend without computational analysis. It allows testing of new policies, designs, and processes without disrupting existing systems by compressing or expanding timescales. Insights from simulation include understanding variable interactions, bottlenecks, and system operations under different conditions.
This document discusses computer simulation and modeling. It defines computer simulation as creating an imitation of a real-world system on a computer in order to experiment with and observe its behavior. The key steps in simulation are defining the system, formulating a model, collecting input data, translating the model, verifying results, and experimenting. Applications include weather forecasting, design of vehicles, architecture, and aeronautics. Computer simulation provides advantages like testing systems without building them physically and training for risky tasks virtually. Limitations are reliance on the model maker's skills and the time and costs involved.
This document discusses desirable features for simulation software. It identifies general capabilities like modeling flexibility, ease of use, and debugging aids as important. Hardware/software considerations and statistical capabilities are also important factors. Good animation, documentation, customer support, and output reports are desirable as well. Flexibility, ease of use, statistical tools, and visualization are key aspects to consider in choosing simulation software.
The document discusses various techniques for analysis modeling in software engineering. It describes the goals of analysis modeling as providing the first technical representation of a system that is easy to understand and maintain. It then covers different types of analysis models, including flow-oriented modeling, scenario-based modeling using use cases and activity diagrams, and class-based modeling involving identifying classes, attributes, and operations. The document provides examples and guidelines for effectively utilizing these modeling approaches in requirements analysis.
GPSS is one of the earliest discrete event simulation languages developed in the 1960s. It uses a network of blocks to model systems, with each block performing a specific function. Transactions representing entities move through the blocks. Common blocks include Generate to create transactions, Queue to queue transactions, and Advance to impose delays. GPSS is not programmed like other languages but rather models the system as a network of interconnected blocks through which transactions flow.
This document provides an overview of object-oriented analysis and design. It defines key terms and concepts in object-oriented modeling like use cases, class diagrams, states, sequences. It describes developing requirements models using use cases and class diagrams. It also explains modeling object behavior through state and sequence diagrams and transitioning analysis models to design.
This document provides an introduction to queuing models and simulation. It discusses key characteristics of queuing systems such as arrival processes, service times, queue discipline, and performance measures. Common queuing notations are also introduced, including the widely used Kendall notation. Examples of queuing systems from various applications are provided to illustrate real-world scenarios that can be modeled using queuing theory.
This document discusses simulation modeling and its applications. It begins with definitions of simulation as operating a model of a system over time to study its behavior. Simulation is used to evaluate system performance under different configurations before implementation. The key advantages are exploring "what if" scenarios without disrupting real systems and testing new designs. Common applications include manufacturing, construction, military, logistics and transportation. The document outlines the steps in a simulation study and discusses when simulation is appropriate versus not. It concludes with references on modeling and simulation.
The document discusses using simulation to model queuing problems with random numbers. It describes queuing systems as having arrivals, a waiting line, service, and departure components. A single queue-single service point queuing structure is examined, with first-come, first-served queue discipline and random inter-arrival and service times. An example problem simulates 10 customer arrivals at a retail store using random numbers to estimate average waiting time and server idle time percentage. The solution shows calculating arrival and service time probabilities, simulating customer service, and finding total 4 minutes of waiting time and 12 minutes of idle time over 53 minutes.
This document discusses different types of simulation models. It describes:
1) Static vs dynamic models, with dynamic models changing over time and static models as snapshots.
2) Deterministic vs stochastic vs chaotic models, depending on how predictable the behavior is.
3) Discrete vs continuous models, with discrete changing at countable points and continuous changing continuously.
4) Aggregate vs individual models, with aggregate models taking a more distant view and individual models a closer view of decisions.
Simulation involves imitating the operation of a real-world process over time, usually on a computer. It is widely used for decision making and analyzing complex systems that cannot be solved mathematically. A simulation study involves problem formulation, model conceptualization, validation, experimentation, and implementation. Key aspects of a model include entities, attributes, resources, variables, events, and activities.
This document provides an overview of simulation and discrete event simulation. It discusses different types of models including static/dynamic, deterministic/stochastic, and discrete/continuous. It also describes three approaches to discrete event simulation: activity-oriented, event-oriented, and process-oriented. The document outlines several popular simulators including CSIM, GloMoSim, NS-2, and NCTU-NS. It concludes with references for further reading on simulation and these simulators. Mini-projects and projects are proposed for using GloMoSim and developing a MAC simulator using PARSEC, respectively.
System modeling and simulation involves creating simplified representations of real-world systems to understand and evaluate their behavior over time. A system is composed of interconnected parts designed to achieve specific objectives. A model abstracts and simplifies a system for analysis. Simulation executes a model over time to observe how a system operates. It allows experimenting with systems that may be too expensive, dangerous or complex to study directly. Simulation has many uses including analyzing systems before implementation, optimizing designs, training, and evaluating "what-if" scenarios. Key areas where simulation is applied include manufacturing, business, healthcare, transportation and the military.
Simulation involves developing a model of a real-world system over time to analyze its behavior and performance. The key aspects covered in this document include defining simulation as modeling the operation of a system over time through artificial history generation and observation. Simulation models can be used as analysis and design tools to predict the effects of changes to a system before actual implementation. Discrete event simulation is discussed as a common technique that models systems with state changes occurring at discrete points in time. The document also outlines the steps in a typical simulation study including problem formulation, model conceptualization, experimentation and analysis.
This presentation introduces discrete-event simulation software. It discusses what discrete-event simulation is, how it models systems as sequences of events over time. It covers the basic constructs like entities, resources, control elements and operations. It explains how simulation execution advances by processing the next event. It discusses entity states like active, ready, time-delayed and conditional-delayed. It also summarizes different implementations in discrete-event modeling languages and tools like Arena and AutoMod.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Simulation involves modeling real-life situations and processes to analyze them or visualize outcomes. There are several types of simulations including static vs dynamic and deterministic vs stochastic. The simulation process involves defining the problem, collecting data, building a model, experimenting, and interpreting results. Simulation is widely used in fields like engineering, manufacturing, business, and government to study complex systems without having to create physical models. While simulation provides benefits like controlling variables and identifying issues, limitations include not guaranteeing optimal solutions and difficulty interpreting random outputs.
This document provides an introduction to discrete event simulation. It discusses key concepts like systems, processes, states, activities, continuous vs discrete vs hybrid systems, deterministic vs stochastic systems, and when simulation is an appropriate tool. It also outlines the steps in a simulation study from problem formulation to implementation. An example queueing simulation is presented to illustrate tracking customers over time. Random number generation techniques like the linear congruential method are introduced. Desired properties of pseudorandom numbers for simulation are discussed.
System simulation & modeling notes[sjbit]qwerty626
The document discusses simulation modeling and provides an introduction to the topic. It defines simulation as imitating the operation of a real-world process over time. Simulation models take the form of assumptions expressed mathematically or logically about the relationships between system entities. Simulation is appropriate when a system is too complex to understand through other means or to experiment with system changes safely. The document outlines the components of a system and types of models. It also describes the basic steps involved in a simulation study from problem formulation to implementation.
This document discusses different approaches for learning with complete data, including:
1) Parameter learning aims to find numerical parameters for a fixed probability model given complete data for all variables.
2) Maximum likelihood parameter learning derives parameter expressions as log terms and finds values by equating logs to 0.
3) Naive Bayes models assume attributes are conditionally independent, and truth is not representable as a decision tree.
4) Continuous models represent real-world applications using linear Gaussian models that minimize sum of squared errors via standard linear recursion.
Simulation involves modeling real-world processes or systems on a computer in order to evaluate scenarios and outcomes that would otherwise be difficult to comprehend without computational analysis. It allows testing of new policies, designs, and processes without disrupting existing systems by compressing or expanding timescales. Insights from simulation include understanding variable interactions, bottlenecks, and system operations under different conditions.
This document discusses computer simulation and modeling. It defines computer simulation as creating an imitation of a real-world system on a computer in order to experiment with and observe its behavior. The key steps in simulation are defining the system, formulating a model, collecting input data, translating the model, verifying results, and experimenting. Applications include weather forecasting, design of vehicles, architecture, and aeronautics. Computer simulation provides advantages like testing systems without building them physically and training for risky tasks virtually. Limitations are reliance on the model maker's skills and the time and costs involved.
This document discusses desirable features for simulation software. It identifies general capabilities like modeling flexibility, ease of use, and debugging aids as important. Hardware/software considerations and statistical capabilities are also important factors. Good animation, documentation, customer support, and output reports are desirable as well. Flexibility, ease of use, statistical tools, and visualization are key aspects to consider in choosing simulation software.
The document discusses various techniques for analysis modeling in software engineering. It describes the goals of analysis modeling as providing the first technical representation of a system that is easy to understand and maintain. It then covers different types of analysis models, including flow-oriented modeling, scenario-based modeling using use cases and activity diagrams, and class-based modeling involving identifying classes, attributes, and operations. The document provides examples and guidelines for effectively utilizing these modeling approaches in requirements analysis.
GPSS is one of the earliest discrete event simulation languages developed in the 1960s. It uses a network of blocks to model systems, with each block performing a specific function. Transactions representing entities move through the blocks. Common blocks include Generate to create transactions, Queue to queue transactions, and Advance to impose delays. GPSS is not programmed like other languages but rather models the system as a network of interconnected blocks through which transactions flow.
This document provides an overview of object-oriented analysis and design. It defines key terms and concepts in object-oriented modeling like use cases, class diagrams, states, sequences. It describes developing requirements models using use cases and class diagrams. It also explains modeling object behavior through state and sequence diagrams and transitioning analysis models to design.
This document provides an introduction to queuing models and simulation. It discusses key characteristics of queuing systems such as arrival processes, service times, queue discipline, and performance measures. Common queuing notations are also introduced, including the widely used Kendall notation. Examples of queuing systems from various applications are provided to illustrate real-world scenarios that can be modeled using queuing theory.
This document discusses simulation modeling and its applications. It begins with definitions of simulation as operating a model of a system over time to study its behavior. Simulation is used to evaluate system performance under different configurations before implementation. The key advantages are exploring "what if" scenarios without disrupting real systems and testing new designs. Common applications include manufacturing, construction, military, logistics and transportation. The document outlines the steps in a simulation study and discusses when simulation is appropriate versus not. It concludes with references on modeling and simulation.
The document discusses using simulation to model queuing problems with random numbers. It describes queuing systems as having arrivals, a waiting line, service, and departure components. A single queue-single service point queuing structure is examined, with first-come, first-served queue discipline and random inter-arrival and service times. An example problem simulates 10 customer arrivals at a retail store using random numbers to estimate average waiting time and server idle time percentage. The solution shows calculating arrival and service time probabilities, simulating customer service, and finding total 4 minutes of waiting time and 12 minutes of idle time over 53 minutes.
This document summarizes a seminar report on virtual manufacturing. It includes an outline covering topics like what virtual manufacturing is, its characteristics and classification, the virtual reality and other technologies used, as well as benefits, drawbacks and applications. Virtual manufacturing uses simulation, virtual reality and information technologies to generate digital information about the structure and behavior of manufacturing systems. It allows for interactive simulation of processes like virtual prototyping and assembly.
1. The document describes predictor-corrector methods for numerically solving ordinary and partial differential equations.
2. Predictor-corrector methods use an explicit method to predict the next value, then apply an implicit correction method in an iterative process to improve the accuracy of the predicted value.
3. The Euler predictor-corrector method is presented as a basic example, using Euler's explicit method for the predictor and the backward Euler implicit method for the corrector.
This document describes an experiment to determine the specific latent heat of vaporization of water using an electrical method. The experiment involves heating water in a beaker electrically using a resistor submerged in the water. The voltage and current are measured as the water boils and vaporizes. The mass of water before and after boiling is measured to determine the mass vaporized. The heat supplied and mass vaporized are then used to calculate the specific latent heat of vaporization. Potential sources of error and improvements to the experiment are also discussed.
The document outlines several technology challenges for the Program Executive Office for Simulation, Training and Instrumentation (PEO STRI) including:
1) Improving platform virtualization and delivery of simulation services from centralized hubs to better utilize computing resources.
2) Enhancing modeling and simulation of human intelligence collection operations and sources.
3) Developing biometric authentication to replace usernames and passwords for secure access to simulation systems.
4) Automating feature extraction from imagery and merging with existing data.
This document provides an introduction and overview of simulation modeling. It discusses when simulation is an appropriate tool, the advantages and disadvantages, common applications, and the basic components and types of systems that can be modeled. It also outlines the typical steps involved in a simulation study, including problem formulation, model building, experimentation and analysis, and documentation. Model building involves conceptualizing the model, collecting data, translating the model into a computer program, verifying that the program is working correctly, and validating the model outputs against real system behavior.
Ana Clara Mourão Moura on "Geoprocessing, Multi-criteria Analysis, conflict of interest and simulation of landscape intervention: learning topics in urban planning, at UFMG – Brazil"
The goal of analysis should provide leadership with insight into risk and uncertainty and guidance on actions that can be taken. However, common analysis methods of using point estimates to generate forward-looking business plans disregard uncertainty and ignore risk.
In this presentation, you will learn how to incorporate uncertainty directly into a decision support application. The results is a range estimate with likelihoods of exceeding thresholds based on assumption values, providing leadership with the insight into uncertainty and actions that can be taken to reduce risk.
This document provides an introduction to simulation. It defines simulation as modeling a real system and experimenting with that model to understand the system's behavior or evaluate different operational strategies. The document discusses how simulation allows modeling complex systems in detail and making predictions. It provides examples of simulation applications in computer systems, manufacturing, business, and government for purposes like hardware/software testing, production planning, financial analysis, and military planning. Advantages of simulation are that it can study existing systems without disruption and test proposed systems beforehand. Drawbacks include difficulty interpreting results and high time/costs compared to analytical methods.
This document discusses systems analysis and simulation. It defines a system as a collection of elements that work together to achieve a goal. There are two main types of systems: discrete systems where state variables change at separate points in time, and continuous systems where state variables change continuously over time. A model represents a system in order to study it, as experimenting directly with the real system may not be possible or wise. Simulation models can be static or dynamic, deterministic or stochastic, discrete or continuous. Discrete-event simulation specifically models systems as they progress through time as a series of instantaneous events.
The use of 3D simulation technology to improve health and safety performance ...Stephen Au
As building construction projects become more complex with shorter time to market, 3D design becomes a key driver for success. By adopting the leading 3D, BIM, ITEM, Mobile and Cloud computing technology, an integrated collaboration platform allows owners, architects, engineers, constructors and sales & marketing working together at any place and any time to get the instant correct information with controlled business process. This can greatly improve design innovation, productivity, safety and cost effectiveness under the GREEN design-build-sell-maintain lifecycle. This seminar will be more focus on how to use BIM information to create the 3D construction method statement and 3D on line safety training manual and courses. Some examples of applications of 3D designs in mitigating safety hazards in the construction and manufacturing industries will be highlighted in this seminar.
An Introduction to Simulation in the Social Sciencesfsmart01
This document provides an introduction to simulation design in the social sciences. It discusses why simulations are used, including to confirm theoretical results, explore unknown theoretical environments, and generate statistical estimates. It outlines the key stages of simulations, including specifying the model, assigning parameters, generating data, calculating and storing results, and repeating the process. Finally, it provides examples of simulations and discusses necessary programming tools and considerations for simulation design.
The document discusses the future of simulation in healthcare education and how it will be shaped by emerging technologies, learner demand, enhanced pedagogical approaches, and cultural factors. It describes how younger generations expect interactivity and multimedia in their learning and how simulation allows for realistic virtual experiences. The future of healthcare simulation is predicted to include increased use of reusable learning objects, virtual worlds, and lifelong learning to support a more collaborative, technology-enabled educational model.
Esri CityEngine software allows users to quickly build 3D city models by aggregating geospatial data and procedurally generating buildings and other structures. It provides tools for importing GIS data, designing street networks, modeling buildings and vegetation, and visualizing and analyzing urban planning projects in 3D. CityEngine outputs can be shared online or exported to common 3D formats for use in simulation, gaming, and film production.
The most awaiting technology in the communication field....We have so many tools for sharing our feelings and views by our gesters, voice, image , video...but yet we are ignoring our nose, which is also a very smart senser of our body. So This technology is completely dedicated to our naughty nose :0
Innovation becomes more complex and multidisciplinary, and consequently more challenging and expensive. One way to remedy this, is by using simulation technology, facilitating design iterations and reducing the number of failed experiments.
1) The document provides an overview of APSinc's HR dashboard and business simulation technology services.
2) APSinc offers custom dashboard solutions that integrate client data to provide performance metrics and analytics through interactive interfaces.
3) They also provide business simulation seminars and training programs using software like Foundation to teach strategic decision making through a simulated business environment.
This document provides an introduction and overview of simulation. It discusses what a simulation is and the key components of modeling a system, including entities, attributes, activities, states, and events. It also describes discrete event simulation and the main strategies of activity-oriented, event-oriented, and process-oriented simulation. Finally, it briefly introduces some common network simulators used for wireless networks, including NS-2, GloMoSim, and QualNet.
Simulation and Modelling Reading Notes.pptxDanMuendo1
This document discusses simulation and modeling. It defines simulation as imitating real-life situations using computer models. Models represent systems using mathematical relationships. Simulation allows experimenting with models to understand system behavior under different conditions without changing the real system. The document outlines the modeling and simulation process and provides examples of applications in areas like business planning, drug development, and traffic analysis.
This document provides an introduction to computer simulation. It discusses how simulation can be used to model real systems on a computer in order to understand system behavior and evaluate alternatives. It describes different types of models including iconic, symbolic, deterministic, stochastic, static, dynamic, continuous and discrete models. Monte Carlo simulation is introduced as a technique that uses random numbers. The document outlines the steps in a simulation study and provides examples of systems and their components that can be modeled using simulation.
This document provides notes for an introduction to simulation course. It defines key terms like system, entities, events, and different types of models. It explains that simulation is useful for evaluating systems that would be too complex, expensive or dangerous to experiment on directly. The document outlines the goals of the course as understanding simulation concepts, mathematics, programming and implementing simulation projects. It also discusses different approaches to representing time in a simulation, like next-event time advance and fixed-increment time advance.
Modeling and simulation is the use of models as a basis for simulations to develop data utilized for managerial or technical decision making. In the computer application of modeling and simulation a computer is used to build a mathematical model which contains key parameters of the physical model.
This document discusses modeling and simulation. It defines a model as a representation of an object, system, or idea that is different from the actual entity. Models are used to test systems without creating real versions, predict future behavior, train users safely, and investigate systems in detail. The document outlines different types of modeling including physics-based, finite element, data-based, multi-scale, mathematical, and hybrid modeling. It also discusses conceptual modeling and creating block diagrams to represent systems as subsystems and connections. Criteria for separating systems into subsystems include anatomy, function, and measurability of inputs and outputs.
Approaches to gather business requirements, defining problem statements, business requirements for
use case development, Assets for development of IoT solutions
This chapter introduces simulation as a technique for modeling real-world systems using computers. Simulation allows testing of complex models that may be too difficult to solve analytically. It involves creating a model of a system that runs on a computer and imitates the system's operations over time. The chapter discusses different types of simulation models and their applications in areas like manufacturing, healthcare, transportation and more. It also covers advantages like flexibility to model complex systems and ability to experiment without impacting the real system.
This chapter introduces discrete-event simulation and outlines the key steps in a simulation study. It defines simulation as imitating the operation of a real-world process over time through a conceptual model. Simulation allows experimenting with "what if" scenarios to analyze the potential effects of changes. The chapter describes when simulation is an appropriate tool, its advantages and disadvantages, common application areas, and components of systems and models. It distinguishes between discrete and continuous systems and events, and outlines the development process for discrete-event simulation models.
This chapter introduces discrete-event simulation and outlines the key steps in a simulation study. It defines simulation as imitating the operation of a real-world process over time through a conceptual model. Simulation allows experimenting with "what if" scenarios to analyze the potential effects of changes. The chapter describes when simulation is an appropriate tool, its advantages and disadvantages, common application areas, and components of systems and models. It distinguishes between discrete and continuous systems and events. The final sections outline the development of a discrete-event simulation model and the steps of verifying and validating the model.
Simulation and modeling introduction.pptxShamasRehman4
This document discusses simulation and modeling. It begins by introducing systems, modeling, and simulation. Modeling creates a representation of a system, while simulation operates a model to study the behavior of the actual system. There are different types of simulation models including deterministic/stochastic and static/dynamic models. The document outlines steps for building simulation models, including defining goals, involving end users, choosing tools, and validating results. General purpose languages, simulation languages, and special purpose packages are options for developing simulation models.
Shaun Douglas Smith's graduate studies focused on operations research, industrial engineering, statistics, and quality engineering. His coursework covered topics like optimization theory, simulation, stochastic processes, experiment design, statistical modeling, production and inventory control, and quality engineering. His final project applied an algorithm to solve a bin packing problem. Overall, his program equipped him with technical and analytical skills for improving production systems and decision-making.
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Introduction to simulation modeling
1. Introduction to Simulation
modeling
Submitted To:-
Prof. D.K. Chaturvedi,
Electrical Department,
Faculty of Engineering,
Dayalbagh Educational Institute,
Dayalbagh, Agra.
Submitted By:-
Bhupendra Kumar
M.Tech(Int.) – 094008
2. Introduction to model
Shannon Defines a model as-
A Representation of an object, a system, or
an idea in some form other than that of the
entity itself.
3. Definition - Simulation
“Simulation is the process of designing
a model of a real system and conducting
experiments with this model for the
purpose of either understanding the
behavior of the system and/or
evaluating various strategies for the
operation of the system.”
- Introduction to Simulation Using SIMAN
(2nd Edition)
4. Some other definitions
• The technique of imitating the behavior of
some situation or system by means of an
analogous model, situation, or apparatus,
either to gain information more conveniently
or to train personnel.
• Simulation:
– “… as a strategy – not a technology – to mirror,
anticipate, or amplify real situations with guided
experiences in a fully interactive way.”
6. 6
• Ways to study a system
Systems, Models, and Simulation
7. 7
Elements of Simulation Analysis
Problem Formulation
Data Collection and Analysis
Model development
Model Verification and Validation
Model Experimentation and Optimization
Implementation of Simulation Results
Major Iterative Loops in a Simulation Study
8. Brief history
• World War II
• “Monte Carlo” simulation: originated with the work on
the atomic bomb. Used to simulate bombing raids. Given
the security code name “Monte-Carlo”.
• Late ‘50s, early ‘60s
• First languages introduced: SIMSCRIPT, GPSS (IBM)
• Late ‘60s, early ‘70s
• GASP IV introduced by Pritsker. Triggered a wave of
diverse applications. Significant in the evolution of
simulation.
9. • Late ‘70s, early ’80
• SLAM introduced in 1979 by Pritsker and Pegden.
• Models more credible because of sophisticated tools
• SIMAN introduced in 1982 by Pegden. First language to
run on both a mainframe as well as a microcomputer.
• Late ‘80s through present
• Powerful PCs
• Languages are very sophisticated (market almost saturated)
• Major advancement: graphics. Models can now be
animated!
10. Simulation modeling perspectives
• Can be used to study simple systems
• Good for comparing alternative designs
– More complex techniques allow “optimization” using a
simulation model
• can be used to understand the behavior of the system or evaluate
strategies for the operation of the system
• Model complex systems in a detailed way
• Construct theories or hypotheses that account for the observed
behavior
• Use the model to predict future behavior, that is, the effects that
will be produced by changes in the system
• Analyze proposed systems
12. 12
Examples Of Both Type Models
Continuous Time and Discrete Time
Models:
CPU scheduling model vs. number of
students attending the class.
13. Advantages to Simulation:
• Can be used to study existing systems without disrupting the
ongoing operations.
• Proposed systems can be “tested” before committing resources.
• Allows us to control time.
• Allows us to identify bottlenecks.
• Allows us to gain insight into which variables are most
important to system performance.
• Flexibility to model things as they are (even if messy and complicated)
Allows uncertainty, nonstationarity in modeling
14. Some Primary Uses of Simulation
Models in Operations
• Find the bottlenecks
• How are resources utilized
• Capacity planning
• Impact of configuration changes
• Understand the system dynamics
15. Disadvantages to Simulation
• Model building is an art as well as a science. The quality
of the analysis depends on the quality of the model and the
skill of the modeler.
• Simulation results are sometimes hard to interpret.
• Simulation analysis can be time consuming and expensive.
Should not be used when an analytical method would
provide for quicker results.
• Not guarantee to provide optimal solution
16. Limitations & pitfalls
• Failure to identify objectives clearly up front
• In appropriate level of detail (both ways)
• Inadequate design and analysis of simulation
• experiments
• Inadequate education, training
• Failure to account correctly for sources of
randomness in the system under consideration
• Failure to collect good system data, e.g. not enough
data to create a good model
17. 17
Applications:
Designing and analyzing manufacturing
systems
Evaluating H/W and S/W requirements for a
computer system
Evaluating a new military weapons system or
tactics
Determining ordering policies for an
inventory system
Designing communications systems and
message protocols for them
18. 18
Applications:(continued)
Designing and operating transportation
facilities such as freeways, airports, subways,
or ports
Evaluating designs for service organizations
such as hospitals, post offices, or fast-food
restaurants
Analyzing financial or economic systems
material handling systems, assembly lines,
automated production facilities.
19. Hand and manual simulation concepts
• The numerical methods for manual simulation
can be classified into the following two
classes:
• 1. One-step or single-step method
Euler’s method, Runge–Kutta method.
• 2. Multistep method
Milne, Adams–Bashforth methods, predictor
corrector method.
21. 21
Euler Method
• Modified Euler method is derived by applying the trapezoidal
rule to integrating ; So, we have
• If f is linear in y, we can solved for similar as backward
Euler method
• If f is nonlinear in y, we necessary to used the method for
solving nonlinear equations i.e. successive substitution
method (fixed point)
),(' tyfyn
),('),(
2
''
11 nnnnnnn tyfyyy
h
yy
1ny
22. 22
Example: solve
Solution:
f is linear in y. So, solving the problem using modified Euler
method for yields
25.0,10,1)0(,1' 0 htyytyy
hy
t
h
t
h
y
ht
h
yt
h
y
ytyt
h
y
yy
h
yy
n
n
n
n
nnnn
nnnnn
nnnn
1
1
11
111
11
)
2
1(
)
2
1(
)
2
1()
2
1(
)11(
2
)''(
2
ny
24. Predictor-Corrector Methods
• The Predictor-Corrector technique uses an explicit
scheme (like the Adams-Bashforth Method) to
estimate the initial guess for xi+1 and then uses an
implicit technique (like the Adams-Moulton Method)
to correct xi+1.
25. Predictor-Corrector Example
• Adams third order Predictor-Corrector scheme:
• Use the Adams-Bashforth three point explicit scheme
for the initial value.
• Use the Adams-Moulton three-point implicit method
to correct.
2i1iii1i 51623
12
* fff
h
xx
),(),(8),(5
12
11
*
11i1i iiiiii xtfxtfxtf
h
xx
26. Predictor-Corrector Example
• Consider Exact Solution
• Initial condition: x(0) = 1
• Step size: h = 0.1
• We will use the 3 Point Adams-Bashforth and 3 point
Adams-Moulton. Both require 3 points to get
started!
2
tx
dt
dx
t2
22 ettx
27. Predictor-Corrector Example
• From the 4th order Runge Kutta
• 3-point Adams-Bashforth Predictor Value:
340184.1121587.0218597.1
)1(5)094829.1(16)178597.1(23
12
1.0
2
*
3
xx
218597.1
178597.1218597.1,2.0
094829.1104829.1,1.0
0000.11,0
2
2.0
1.0
0
x
ff
ff
ff
28. Predictor-Corrector Example
• To correct, we need f(t3 , x3
*)
• 3-point Adams-Moulton Corrector Value:
250184.1340184.1,3.0 f
340138.1
121541.0218597.1
094829.11178597.18250184.15
12
1.0
23
xx
29. The values for the Predictor-Corrector Scheme
Three Point Predictor-Corrector Scheme
t x f A-B sum x* f* A-M sum
0 1 1
0.1 1.104829 1.094829
0.2 1.218597 1.178597 0.121587 1.340184 1.250184 0.121541
0.3 1.340138 1.250138 0.128081 1.468219 1.308219 0.12803
0.4 1.468168 1.308168 0.133155 1.601323 1.351323 0.133098
0.5 1.601266 1.351266 0.136659 1.737925 1.377925 0.136597
0.6 1.737863 1.377863 0.138429 1.876291 1.386291 0.138359
0.7 1.876222 1.386222 0.13828 2.014502 1.374502 0.138204
0.8 2.014425 1.374425 0.136013 2.150438 1.340438 0.135928
0.9 2.150353 1.340353 0.131404 2.281757 1.281757 0.13131
1 2.281663 1.281663 0.124206 2.405869 1.195869 0.124102
Predictor-Corrector Example
30. The predictor-corrector method
produces a solution with nearly the
same accuracy as the RK order 4
method.
Generally, the n-step method will
have truncation error of order at
least n.
-10
-8
-6
-4
-2
0
2
4
0 1 2 3 4
xValue
t Value
3 Point Predictor-Corrector Method
4th order Runge-Kutta
Exact
Adam Moulton
Adam Bashforth
Predictor-Corrector Example