Simulation and Modeling Intro

• Simulation & Modeling presented by Dr. Reham Amin Ahmed.

What to Expect from the Course

• The course will cover simulation and modeling concepts.

Book

• The book is "Discrete-Event System Simulation," 5th edition, by Jerry Banks, David Nicol, John Carson, and Barry Nelson.
• It was published in 2013.
• The print ISBN is 978-1-292-02437-0 and the E-ISBN is 978-1-292-03726-4.
• The book has 568 pages and is written in English.

Agenda

• The topics that will be covered are an introduction to simulation and modeling.
• Determining when simulation is an appropriate tool.
• Discussing when not to use simulation.
• Advantages and application areas of simulation will be reviewed.
• Systems and system environments.
• System components.
• Examples of systems and components.

What are Modeling and Simulation?

• Modeling involves constructing a conceptual framework that describes a system.
• Simulation involves Imitating real-world processes or systems, whether by hand or by computer.
• It generates an artificial history to understand the characteristics of the real system.
• It answers a wide range of "what if" questions about a real-world system.

When is Simulation Appropriate?

• Simulation is appropriate when studying internal interactions of a subsystem with a complex system.
• It helps in gaining knowledge about the improvement of a system.
• New designs and policies can be tested before implementation.
• Can help determine the requirements by studying different capabilities for a machine.

When Not to Use Simulation?

• Don't use the simulation if the problem can be solved by common sense.
• If the problem can be solved analytically, it does not need to be simulated.
• If a direct cost experiment is more effective, simulation is not necessary.
• Also, simulation should not be used if the cost exceeds the savings.
• Don't simulate when resources or time are unavailable.
• Avoid simulation if system behavior is relatively complex.

Advantages of Simulation

• Enables the study of new policies and operating procedures without disrupting ongoing operations.
• New hardware or system designs can be tested without committing resources for their acquisition.
• The Transportation systems and layouts can be tested using simulation.
• Helps understand the impact of different variables on the overall system performance.

Application Areas

• Simulation has applications in manufacturing, semiconductor manufacturing, construction engineering and project management, and military applications.
• Also has applications in logistics, supply chain and distribution, transportation modes and traffic, and business application.
• There are applications in automated material handling.
• Also in testing and function control, computer related simulations, health care, risk analysis, and insurance.

Systems and System Environment

• A "system" is defined as a group of objects that are joined together in some regular interaction to accomplish a purpose.
• A car factory with machines, components, parts, welders, and assembly lines is an example.
• A system is often affected by changes occurring outside the "system environment".
• Examples include Factory: Arrival orders and Banks: arrival of customers

System Components

• An Entity represents an object of interest within the system.
• Attributes: represent the system property.
• The elements that often make up the system e.g. :Machine.
• Activity: Duration of specific length such as processing.
• State: Collection of variables necessary to describe the system in any time relative to the objective of the study such as status when busy, idle, down.
• Event: Instantaneous breakdown that might change the state of the system.

Example System Entities, Activities and Attributes

• Entity: Departments and Orders.
• Activity: Manufacturing.
• Attribute: quantity for each order, number of machines.

Examples of Systems and Components

• Discusses several examples of systems, components, attributes, activities and events.

Discrete System

• Discrete system: a system in which the state variable(s) change only at a discrete set of points in time.
• A Bank is an example of a discrete system.
• Changes only happen when customers get served.

Continuous System

• Continuous system: a system in which the state variable(s) change continuously over time.
• An example is the head of water behind a dam during a rain storm.

Model of a System

• A model is a representation of a system used for the purpose of studying the system.
• The behavior of a system that evolves over time is studied by developing a simulation model.
• The model considers only those aspects of a system that affect the problem under investigation.
• These aspects are represented in a model of the system, which by definition is a simplification of the system.
• A model takes a set of expressed assumptions.
• Utilizes mathematical or logical components and symbolic relationships between the entities.

Goal of Modeling and Simulation

• A model can be used to investigate a wide variety of "what if" questions about real-world systems.
• Simulation can be used as an analysis tool for predicting the effect of changes.
• Used as a design tool to predicate the performance of new systems.
• It is better to do simulation before implementation.

Why Modeling Is Important

• Modeling helps in understanding the behavior of a real system before it is built.
• It reduces the cost of building and experimenting with a model.
• The models can be used to decrease the number of plane crashes.
• Models help pilots can be taught how to cope with wind sheer while landing.
• The models have the capability of scale time or space in a favorable manner.

Types of Models - Ways to Model a System

• Modeling options: experiments with the actual system.
• Experiment with a model, apply physical models.
• Other options are mathematical models, analytical solutions, and symbolic notations with equations.

Types of Simulation Model

• System models may be deterministic or stochastic.
• They may be static or dynamic.
• They may be continuous or discrete.

Types of Simulation Model Summarized

• Models can be Deterministic or Stochastic, Static or Dynamic and/or Continuous or Discrete.

Characterizing a Simulation Model

• Is the model deterministic or stochastic?
• It is important to know if it contains stochastic components?
• Is time a significant variable?
• To know if it is static or dynamic.
• Does the system state evolve continuously or only at discrete points in time?
• Helps to know if continuous or discrete.
• Continuous utilizes classical mechanics.
• Discrete employs queuing, inventory, and machine shop models.

Types of Simulation Model - Deterministic Model

• Deterministic or Stochastic: Simulation models that contain no random variables are classified as deterministic.
• Deterministic models have a known set of inputs that will result in a unique set of outputs.
• Deterministic arrivals would occur at a dentist's office if all patients arrived at the scheduled appointment time.

Types of Simulation Model - Stochastic Model

• Deterministic or Stochastic: A stochastic simulation model has one or more random variables as inputs.
• Random inputs lead to random outputs.
• Since the outputs are random, they can be considered only as estimates of a model's true characteristics.
• The simulation of a bank would usually involve random interarrival times and random service times.
• In a stochastic simulation, the output measures (average number of people waiting average waiting time, of a customer) should be treated as statistical estimates of a model's true characteristics.

Types of Simulation Model - Static Model

• Static or Dynamic: A static simulation model, sometimes called a Monte Carlo simulation, represents a system at a particular point in time.
• The dynamic model will present systems as they change over time.
• The simulation of a bank from 9:00 A.M. to 4:00 P.M. is an example of a dynamic simulation.

Types of Simulation Model - Continuous or Discrete System

• A discrete system is one in which the state variable(s) change only at a discrete set of points in time.
• A continuous system is one in which the state variable(s) change continuously over time.

Discrete Event Simulation Model

• DES models system where the state variable changes only at a discrete set of points in time.
• Simulation models are analyzed by numerical rather than analytical methods.
• The modelling is stochastic and state variables are somewhat random.
• Dynamic, time evolution is important.
• Discrete-Event means that significant changes occur at discrete time instances.

Steps in Simulation Study

• How to simulate? By hand, using programming languages or simulation software.

Steps in Simulation Study - Diagram Details

• Problem formulation.
• Setting objectives and overall project plan.
• Model conceptualization.
• Data collection.
• Model translation.
• Verified or not.
• Validated or not.
• Experimental design.
• Production runs.
• Analysis.
• Documentation.
• Reporting.
• Implementation.

Steps in Simulation Study - Problem Formulation

• Describing the process of problem formulation.
• The problem is clearly understood by the simulation analyst.
• The formulation is clearly understood by the client.

Steps in Simulation Study - Setting Objectives and Overall Plan

• Project proposal should determine questions and identify scenarios.
• Should provide decision criteria and determine the data requirements.
• The user the hardware and the software required should be determined.
• Should prepare a time plan with costs and billing.

Steps in Simulation Study - Model Conceptualization and Building

• Abstract essential features from events activities attributes.
• Attributes and resources and variables, provide performance measures and provide data requirements.
• Select data relative to level of details and ensure they are assumptions.

Steps in Simulation Study - Data Collection

• The process of collecting data for input analysis and validation is discussed.
• In a bank setting: learn about the length of waiting lines as the number of tellers change.
• Get the data needed that will be the distributions of interarrival times, service time and distribution of lengths of lines

Steps in Simulation Study - Model Translation

• The objective of the model translation phase is to translate the system into a computer model.
• Uses Coding.
• Examples: General Purpose Language or Special Purpose Stimulation Languages.

Steps in Simulation Study - Verified?

• Verification: The process to determine if the operational logic is correct.
• Debugging needs to take place if it is not correct.

Steps in Simulation Study - Validated?

• Validation: the process of determining if the model accurately represents the system.
• Comparison of model results with collected data from the real system should be completed

Steps in Simulation Study - Experimental Design

• Alternative scenarios to be simulated including number of simulations and length of time for each simulation experiment.

Steps in Simulation Study - Production Runs and Anaysis

• Discussing Statistical tests for significance and ranking, point/estimation and confidence estimation when utilizing result.

Steps in Simulation Study - Documentation & Reporting

• Program documentation allows for future modifications and creates confidence.
• Progress Reports include Frequent reports.
• Alternative scenarios, performance measures.
• Results and recommendations are included in documentation.

Implementation

• Ensuring you have successful project.

Review Questions

• Name several entities, attributes, activities, events, and state variables for the following systems.
• Examples of entities are included in question.

More Review Questions

• The review question asks to to conduct simulation of cooking spaghetti, asking what are events and variables associated with process.