Modeling and Simulation Chapter 1 Summary PDF

Modeling and Simulation Chapter 1: Introduction Important Definitions System The term “system” is derived from the Greek word “systema”, which means: an organized relationship among functioning units or components. Us...

Modeling and Simulation Chapter 1: Introduction Important Definitions System The term “system” is derived from the Greek word “systema”, which means: an organized relationship among functioning units or components. Used to describe almost any ordered arrangement of ideas or constructs. Any set of interrelated components acting together to achieve a common objective. A group of objects that are joined in some regular interaction or interdependence to accomplish some purpose. Something in the real world we are interested in. Often, it is something complicated, so we must decide which details can be simplified or abstracted away. The collection of entities that compose a system for one study might be only a subset of another larger system. For example: If one wants to study a banking system to determine the number of tellers needed to provide adequate service for customers who want just to encash or deposit → the system can be defined to be that portion of the bank comprising of the tellers and the customers. If the loan officer and the safety deposit counters are to be included, then the definition of the system must be more inclusive accordingly. Model Modeling and Simulation 1  The result of abstraction includes the features we think are essential. It can be represented in the form of diagrams and equations, which can be: 1. Used for mathematical analysis. 2. Implemented in the form of a computer program, which can run simulations. A simplification of a real system. It can be: 1. Analytic: When a mathematical approach is feasible. 2. Simulation: When a model will be used for complex systems. 3. Experimental: When the real system already exists. Modeling The process of representing a system with a specific tool to study its behavior. Simulation The imitation of the operation of a real-world process or system over time. Enables the study of internal interaction of a subsystem with complex system. prediction The result of analysis and simulation about: 1. What the system will do? 2. Why does it behave in that way? 3. A design intended to achieve a purpose. We can validate predictions and test designs by: 1. Taking measurements from the real world. 2. Comparing the data we get with the results from analysis and simulation. The Goal of Modeling and Simulation Modeling A model can be used to: Investigate a wide variety of “what if” questions about real-world systems. Potential changes to the system can be simulated and predicate their impact on the system. Find adequate parameters before implementation. Simulation Simulation can be used as: An analysis tool for predicting the effect of changes. A design tool to predicate the performance of the new system. Modeling and Simulation 2  It is better to do a simulation before implementation. Simulation Languages 1. MATLAB. 2. Python. 3. Java. 4. C/C++. Others. Simulation Software 1. Anylogic. 2. Arena. 3. AutoMod. 4. ExtendSim. 5. Flexsim. 6. ProModel. 7. SIMUL8. Others. Simulation is the Appropriate Tool For: 1. Simulating environmental changes and finding their effects. 2. Gaining knowledge about the improvement of a system using a simulation model. 3. Changing simulation inputs to find important input parameters. 4. Testing new designs before implementation. 5. Determining the requirements by simulating different capabilities for a machine. 6. When there is a risk of damaging the system, or a risk of life. 7. Visualizing a plan with animated simulation, because: The modern system is too complex that its internal interaction can be treated only by simulation. Experimenting with a real system is an extremely costly affair. For example, the physical experimentation of a complex system like the satellite system is quite expensive and time consuming. 8. When an analytical solution is possible. Simulation is Not Appropriate Tool When The problem can be solved: Modeling and Simulation 3  By common sense. Analytically. If it is easier to perform direct experiments. If cost exceeds savings. If resources or time are not available. Advantages of Simulation 1. Testing hypotheses about how or why certain phenomena occur for feasibility. 2. Compressing or expanding time to allow for a speed-up or slow-down of the phenomena. 3. Obtaining insights about: a. The interaction of variables. b. The importance of variables to the performance of the system. 4. Testing new hardware designs, physical layouts, transportation systems easily. 5. Understanding how the system operates using a simulation study. 6. Answering important “What if” questions for designing new systems. Disadvantages of Simulation 1. Model building requires special training. 2. Simulation results can be difficult to interpret. 3. Simulation modeling and analysis can be time-consuming and expensive. Areas of Application 1. Networks. 2. Military applications. 3. Call centers. 4. Manufacturing applications. 5. Health care. 6. Road traffic. 7. Crowd flow. 8. Airport. 9. Car garage. 10. Banking. 11. Trains stations. 12. Gas stations. 13. Highway junction. Modeling and Simulation 4  14. Automated guided vehicles. System Environment Every system consists of: 1. Supersystems at higher levels. 2. Subsystems or components at lower levels. A system is characterized by the following attributes: System boundary In modeling systems, it is necessary to decide on the boundary between the system (i.e., part of system) and its environment. This decision may depend on the purpose of the study. System components and their interactions A system component is: a fundamental building block. Simple and easy to understand. Quite easy to find the input-output relations for the system components with the help of some fundamental laws of physics, which is called the mathematical model for components. May be written in the form of difference or differential equations. An interaction has two components: Input: What enters the system from outside the boundary. Output: What leaves the system boundary to the environment. Direct or indirect result to a given input. System Environment A system is often affected by changes occurring outside the system. Such changes are said to occur in the system environment, like in: Factory: Arrival orders. Effect of supply on demand: relationship between factory output and arrival (activity of system). Modeling and Simulation 5  Banks: Arrival of customers. Example → living organisms: They are open systems. They cannot survive without continuously exchanging matter and energy with their environment. When we separate a living organism from its surroundings, it will die shortly due to a lack of oxygen, water, and food. Components of a System Entity An object of interest in the system. Attribute A property of an entity. Activity A time period of specified length. State Variables Collection of variables necessary to describe the system at any time, relative to the objectives of the study. Event An immediate occurrence that might change the state of the system. Important Terms Endogenous: Used to describe activities and events occurring within a system, like: The completion of service of a customer. Exogenous: Used to describe activities and events in the environment that affect the system, like: The arrival of a customer. Examples of several systems Modeling and Simulation 6  System Entities Attributes Activities Events State Variables Number of Checking- busy tellers; Making Arrival; Banking Customers account number of deposits departure balance customers waiting Status of Speed; Welding; machines Production Machines capacity; Breakdown stamping (busy, idle, or breakdown rate down) Number waiting Length; Arrival at Communications Messages Transmitting to be destination destination transmitted Levels of inventory; Inventory Warehouse Capacity Withdrawing Demand backlogged demands Classification of Systems Systems can be classified based on: Time frame 1. Discrete System The state variable(s) change only at discrete points in time, like queuing system (bank, telephone network, traffic lights, machine). 2. Continuous System The state variable(s) change continuously over time, like the head of water behind a dam, solar system, charging a battery. Modeling and Simulation 7  3. Hybrid System A combination of continuous and discrete dynamic system behavior. It has the benefit of encompassing a larger class of systems within its structure, allowing more flexibility in modeling continuous and discrete dynamic phenomena, like, traffic along a road with traffic lights. Complexity 1. Physical systems Systems whose variables can be measured with physical devices that are quantitative such as: Electrical systems. Mechanical systems. Computer systems. Hydraulic systems. Thermal systems. Combination of these systems. It is a collection of components, in which each component has its own behavior, used for some purpose. These systems are relatively less complex. 2. Conceptual systems Systems in which all the measurements are conceptual or imaginary and in qualitative form as in: Modeling and Simulation 8  Psychological systems. Social systems. Health care systems. Economic systems. These are complex systems. 3. Esoteric systems Systems in which the measurements are not possible with physical measuring devices. The complexity of these systems is of highest order. The degree of interconnection of events Independent system If the events have no effect upon one another. Cascaded System If the effects of the events are unilateral (that is, part A affects part B, B affects C, and not vice versa). Coupled system If the events mutually affect each other The nature and type of components 1. Static or dynamic components A static simulation model (sometimes called a Monte Carlo simulation): A system at a particular point in time. Dynamic simulation models: Systems as they change over time, like the simulation of a bank from 9:00 A.M. to 4:00 P.M. 2. Deterministic or stochastic components Deterministic: Simulation models that contain no random variables (no probabilistic component in the system). Stochastic: Simulation model that has one or more random variables as inputs (some components of the system have a probabilistic behavior, as random variable or event probability), like queuing systems. 3. Continuous-time and discrete-time systems 4. Linear or nonlinear components Type of measurements taken. Type of interactions. Steps in a Simulation Study Modeling and Simulation 9  1 → Problem formulation Every study should begin with a statement of the problem. If the statement is provided by the policymakers or those who have the problem, the analyst must ensure that the problem being described is clearly understood. If the analyst is developing a problem statement, the policymakers must understand and agree with the formulation. 2 → Setting objectives and overall project plan The objectives indicate the questions to be answered by simulation. At this point, a determination should be made concerning whether simulation is the appropriate methodology for the problem as formulated and the objectives as stated or not. 3 → Model conceptualization It is best to start with a simple model and build toward greater complexity. However, the model complexity need not exceed that required to accomplish the purposes for which the model is intended. It is advisable to involve the model user in model conceptualization, which will enhance the quality of the resulting model and increase the confidence of the model user in the application of the model. 4 → Data collection There is a constant interplay between the construction of the model and the collection of the needed input data. As the complexity of the model changes, the required data elements can also change. Also, since data collection takes such a large portion of the total time required to perform a simulation, it is necessary to begin as early as possible, usually together with the early stages of model building. 5 → Model translation Most real-world systems result in models that require a great deal of information storage and computation, so the model must be entered into a computer-recognizable format. We use the term program even though it is possible, in many instances, to accomplish the desired result with little or no actual coding. The modeler must decide whether to program the model in a simulation language or to use special-purpose simulation software. 6 → Verified? Did we build the model right? Verification pertains to the computer program that has been prepared for the simulation model. Is the computer program performing properly? With complex models, it is difficult, if not impossible, to translate a model successfully in its entirety without a good deal of debugging; if the input parameters and logical structure of the model are correctly represented in the computer, verification has been completed. 7 → Validated? Modeling and Simulation 10  Did we build the right model? Validation usually is achieved through the calibration of the model, an iterative process of comparing the model against actual system behavior and using the conflict between the two, and the insights gained, to improve the model. This process is repeated until model accuracy is judged acceptable. 8 → Experimental design The alternatives that are to be simulated must be determined. Often, the decision concerning which alternatives to simulate will be a function of runs that have been completed and analyzed. For each system design that is simulated, decisions need to be made concerning the length of the initialization period, the length of simulation runs, and the number of replications to be made of each run. 9 → Production runs and analysis Production runs and their subsequent analysis, are used to estimate measures of performance for the system designs that are being simulated. 10 → More runs? Given the analysis of runs that have been completed, the analyst determines whether additional runs are needed and what design those additional experiments should follow. 11 → Documentation and reporting There are two types of documentation: 1. Program documentation: Necessary if: The program is going to be used again by the same or different analysts, it could be necessary to understand how the program operates. The program is to be modified by the same or a different analyst. 2. Progress reports. Provide the important, written history of a simulation project. Give a chronology of work done and decisions made. “It is better to work with many intermediate milestones than with one absolute deadline.” Possibilities prior to the final report include: Model specification. Prototype demonstrations. Animations. Training results. Intermediate analyses. Program documentation. Progress reports. Modeling and Simulation 11  Presentations. 12 → Implementation The success of this phase depends on how well the previous eleven steps have been performed. Modeling and Simulation 12