Chapter 1

CS 487 Simulation and Modeling What is Simulation? A simulation is the imitation of the operation of a real-world process or system over time. Could be done by hand or on a computer. Involves generation of data & artificial history of a system, observation of the data and history, and inferences concerning the system’s characteristics. To study a system, we often have to make assumptions about the operation of the system. Assumptions constitute a model, used to understand the behavior of a system. Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling What is Simulation? Analytical solutions: if the model relationships are simple enough to use mathematical methods to obtain exact information on system Simulation: Develop a simulation model and evaluate the model usually with a computer to estimate the desired characteristics of the model. Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling Simulation Model A simplified representation of a sys. (or process or theory), not sys. Itself. Models can’t have all attributes; they are simplified, controlled, generalized, or idealized. For a model to be useful, all its relevant behaviors must be determined in a practical way, given a reasonably limited set of descriptions. A model must be validated. After validation, a model can be used to investigate and predicate system behaviors, or answer “what-if” questions to enhance understanding, training, prediction, and evaluation of alternatives. Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

When Simulation is Appropriate? Study the internal interactions of a complex (sub)-system. Observe model’s behavior and resulting outputs due to changes on external environment or internal variables. Improve system through model building. Experiment new designs and policies prior to implementation. Understand & verify analytic solutions. Identify & determine requirements. Allow training & learning at a lower cost. Visualize operations through animation. It’s difficult, time-consuming, expensive, hazardous, or impossible to solve the problem by conventional analytic or numeric methods. Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

When Simulation is Not Appropriate? When the problem can be solved using common sense. When the problem can be solved analytically. When it is easier to perform direct experiments. When the costs exceed the savings. When resources or time are not available. When no data is available. When verification & validation cannot be performed. When the power is overestimated. When the system is too complex or can’t be defined. Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Advantages of simulation New policies, operating procedures, information flows and son on can be explored without disrupting ongoing operation of the real system. New hardware designs, physical layouts, transportation systems and … can be tested without committing resources for their acquisition. Time can be compressed or expanded to allow for a speed-up or slow-down of the phenomenon( clock is self-control). Insight can be obtained about interaction of variables and important variables to the performance. Bottleneck analysis can be performed to discover where work in process, the system is delayed. A simulation study can help in understanding how the system operates. “What if” questions can be answered. Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Disadvantages of simulation Model building requires special training. Vendors of simulation software have been actively developing packages that contain models that only need input (templates). Simulation results can be difficult to interpret. Simulation modeling and analysis can be time consuming and expensive. Many simulation software have output-analysis. Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling Areas of Applications Computer systems, e.g. scheduling, memory management Computer networks and communications Manufacturing Semiconductor manufacturing Construction engineering Military applications Transportation Business process ….. Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Systems and system environment To model a system, we first need to know what a system is. A system: objects and their relationships and interactions. System environment: changes happen outside the system, but affect the system Boundary Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling Components of a System Entity: system object of interest (e.g. Customers in a bank, machines in a factory) Attribute: property of an entity (e.g. Customers account, machines speed/capacity) Activity: operation in a specific time period (e.g. customers withdraw, machines welding) State: collection of variables needed to describe the system at any time (e.g. #customers waiting, busy/idle machine) Event: an instantaneous occurrence that may change the state of the system (e.g. customer arrivals, machine breakdown). Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling Components of a System Partial components shown in the table Full components are based on the simulation objectives. Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling Components of a System Specify the system components for A cafeteria A grocery store An automobile assembly line Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Discrete and Continuous Systems Discrete system: state variable(s) change only at a discrete set of points in time. Example: number of customers waiting in line Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Discrete and Continuous Systems Continuous system: state variable(s) change continuously or smoothly over time. Example: chemical level in a tank, electric current Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling Model of a System To study the system it is sometimes possible to experiments with system This is not always possible (bank, factory,…) A new system may not yet exist Model: construct a conceptual framework that describes a system It is necessary to consider those accepts of systems that affect the problem under investigation (unnecessary details must remove) Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling Types of Models Models: Physical: model home, model of a bridge, wax model of a person Mathematical (symbolic): a(j!skf)(nvoe) = me3! Simulation model Static (at some point in time) vs. Dynamic (change over time) Deterministic (known inputs) vs. Stochastic (random variables, inputs/outputs) Discrete vs. Continuous Focus of this course: Discrete, dynamic, and stochastic Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling Types of Models Known inputs Random inputs At given time Change over time Chemical levels in a tank with known input rate Arrival to doctor office with appointments Arrival to a cafeteria Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Discrete-Event System Simulation Modeling of systems in which the state variable changes at a discrete set of points in time. Methods: numerical instead of analytical Analytical: deductive reasoning of math; accurate Numerical: computational procedures; approximate Simulation models are “run” rather than solved. Observation of the real system, entities, interactions Assumptions of the model Collection of data Analysis and estimation of system performance Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling Three Model Levels Conceptual Very high level How comprehensive should the model be? What are the state variables, which are dynamic, and which are important? Specification On paper May involve equations, pseudocode, etc. How will the model receive input? Computational A computer program General-purpose PL or simulation language? Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Steps in a Simulation Study Figure 1.3: Problem formulation Setting of objectives and project plan Model conceptualization Data collection Model translation Verification Validation Experimental design Production runs and analysis More runs Documentation and reporting Implementation Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Steps in a Simulation Study The previous steps can be classified in phases as follows: Phase 1: Discovery and orientation Fine-tune, recalibration, clarifications Problem formulation Setting of objectives and project plan Phase 2: Model building and data collection Continuing interplay, Model conceptualization Data collection Model translation Verification Validation Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Steps in a Simulation Study Phase 3: Running the model carefully organized plane to experiment the simulation model, statistical experiment involves estimated output with errors Experimental design Production runs and analysis More runs Phase 4: Implementation Documentation and reporting Implementation Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Steps in a Simulation Study Problem formulation Analyst and policy maker should agree and understand the formulation Setting of objectives and project plan Questions to be answered. Appropriate to do simulation: as formulated or as objectives stated? Alternative systems to be used and how to be evaluated? Peoples involved? Time required? Cost? Anticipated results? Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Steps in a Simulation Study Model conceptualization Map the essence of the real system to the model Data collection Influenced by the objectives Data to be able to do simulation and obtain results Data to be used for validation of the simulation Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Steps in a Simulation Study Model translation Written as a code or in a form that can be assessed, calculated or evaluated Verification Is the model built right? is the computer program performing properly? Need debug? Validation Compare the simulation model to the actual system? Iterative until acceptable accuracy level achieved. Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Steps in a Simulation Study Experimental design Expected runs, alternatives to be tested, parameters to be used? Production runs and analysis Estimate the measures of performance. More runs Documentation and reporting Program and progress Implementation Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Verification and Validation The most important step in the process: validation Validation should not be an isolated task that follows model development, but rather an integral part of model development. Verification: “Are we building the model right?” Is the model programmed correctly (input parameters and logical structure)? Validation: “Are we building the right model?” Is the model an accurate representation of the real system? Iterative process of comparing the model to actual system behavior and refine the model Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

CS 487 Simulation and Modeling Model Building Iterative process consisting of three main steps: Observe the real system & the interactions of components and collect data Domain specific knowledge Stakeholders: operators, technicians, engineers, … Construct a conceptual model Assumptions or hypotheses on components and parameter values Structure of the system Translate the operational model to computer recognizable form Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008

Uses of PCs in simulation steps: Data Collection (step 4) – Storage of raw data in a file would allow rapid accessibility and a large memory at a very low cost. The data could be easily augmented as it is being collected. Analysis of the data could also be performed using currently available software. Model Translation (step 5) – Many simulation languages are now available (see Chapter 4). Validation (step 7) – Validation is partially a statistical exercise. Statistical packages are available for this purpose. Experimental Design (step 3) - Same response as for step 7. Production Runs (step 9) - See discussion of step 5 above. Documentation and Reporting (step 11) – Software is available for documentation assistance and for report preparation. Jordan University of Science and Technology – Computer Science Department CS 487 Simulation and Modeling First semester 2007/2008