01.Simulation Concepts.rev1

Chapt. 1 -- Handout 1

Prof. Luis E. Herrera 01.Simulation_concepts Slide 1

Chapter 1

What is Simulation?

Last revision August 06, 2012

Scenario analysis

Prof. Luis E. Herrera Topic 1 – What Is Simulation?


What is Simulation?

• A simulation: imitation of the operation of a real-world process or system over time: Involves generation of an artificial history of a system. Observes that history and draws inferences about system

characteristics.• Can be used as:

Analysis tool for predicting the effect of changes to existing systems. Design tool to predict performance of new systems.

• Many real-world systems are very complex that cannot be solved mathematically. Hence, numerical, computer-based simulation can be used to imitate the

system behavior.

When to use Simulation?

• Simulation can be used for the purposes of: Study and experiment with internal interactions of a complex system. Observe the effect of system alterations on model behavior. Gain knowledge about the system through design of simulation model. Use as a pedagogical device to reinforce analytic solution

methodologies, also to verify analytic solutions. Experiment with new designs or policies before implementation. Determine machine requirements through simulating different

capabilities. For training and learning. Show animation. Model complex system.


When to use Simulation? (Cont’d)

• Simulation can be used for the purposes of: Explore new policies or procedures without disrupting ongoing

operations of the real system. Test new hardware or physical systems without committing to

acquisition. Test hypotheses about how or why certain phenomena occur. Study speed-up or slow-down of the phenomena under investigation. Study interactions of variables, and their importance to system

performance. Perform bottleneck analysis. Understand how the system operates. Test “what if” questions.


When NOT to use Simulation?

• Simulation should not be used when: Problem can be solved by common sense. Problem can be solved analytically. If it is easier to perform direct experiments. If the costs exceed the savings. If the resources or time to perform simulation studies are not available. If no data, not even estimates, is available. If there is not enough time or personnel to verify/validate the model. If managers have unreasonable expectations: overestimate the power of

simulation. If system behavior is too complex or cannot be defined.



When NOT to use Simulation? (Cont’d)

• Disadvantages: Model building requires special training. Simulation results can be difficult to interpret. Simulation modeling and analysis can be time consuming and

expensive. Simulation is used in some cases when an analytical solution is possible

(or even preferable).


When NOT to use Simulation? (Cont’d)

• Disadvantages (cont’d): Don’t get exact answers, only approximations, estimates Also true of many other modern methods Can bound errors by machine roundoff Get random output (RIRO) from stochastic simulations Statistical design, analysis of simulation experiments Exploit: noise control, replicability, sequential sampling, variance-

reduction techniques Catch: “standard” statistical methods seldom work


Areas of application

• The applications of simulation are vast. The Winter Simulation Conference (www.wintersim.org): an excellent

way to learn more about the latest in simulation applications and theory.• Some areas of applications:

Manufacturing Logistics, supply chain, and distribution. Transportation modes and traffic. Healthcare. Construction engineering and project management. Military. Business process simulation.

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Popularity of Simulation

• Has been consistently ranked as the most useful, popular tool in broader area of operations research / management science 1978: M.S. graduates of CWRU O.R. Department … after graduation

1. Statistical analysis2. Forecasting3. Systems Analysis4. Information systems5. Simulation

1979: Survey 137 large firms, which methods used?1. Statistical analysis (93% used it)2. Simulation (84%)3. Followed by LP, PERT/CPM, inventory theory, NLP, …


Popularity of Simulation (cont’d.)

1980: (A)IIE O.R. division members– First in utility and interest — simulation– First in familiarity — LP (simulation was second)

1983, 1989, 1993: Longitudinal study of corporate practice1. Statistical analysis2. Simulation

1989: Survey of surveys– Heavy use of simulation consistently reported

• Since these surveys, hardware/software have improved, making simulation even more attractive Historical impediment to simulation – computer speed


Using Computers to Simulate

• General-purpose languages (C, C++, C#, Java, Matlab, FORTRAN, others) Tedious, low-level, error-prone But, almost complete flexibility

• Support packages for general-purpose languages Subroutines for list processing, bookkeeping, time advance Widely distributed, widely modified

• Spreadsheets Usually static models (only very simple dynamic models) Financial scenarios, distribution sampling, SQC Examples in Kelton, Chapter 2 (one static, one dynamic) Add-ins are available (@RISK, Crystal Ball)



Using Computers to Simulate (cont’d.)

• Simulation languages GPSS, SLX, SIMAN (on which Arena is based, included in Arena) Popular, some still in use Learning curve for features, effective use, syntax


Using Computers to Simulate (cont’d.)

• High-level simulators Very easy, graphical interface Domain-restricted (manufacturing, communications) Limited flexibility — model validity?

• Arena• Promodel• Simul8• Flexsim• Extend

Systems and System Environment

• A system is a group of objects joined together in some regular interaction or interdependence to accomplish some purpose. e.g., a production system: machines, component parts & workers

operate jointly along an assembly line to produce vehicle. Affected by changes occurring outside the system.

• System environment: “outside the system”, defining the boundary between system and it environment is important.

• Target systems in a simulation study: actual or planned

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Model of a System

• Studies of systems are often accomplished with a model of a system.

• A model: a representation of a system for the purpose of studying the system. A simplification of the system. Study model instead of real system … usually much easier, faster,

cheaper, safer Should be sufficiently detailed to permit valid conclusions to be drawn

about the real system. Model validity (any kind of model … not just simulation)

– Care in building to mimic reality faithfully– Level of detail– Get same conclusions from model as you would from system

Should contain only the components that are relevant to the study.


Types of models


Types of Models

• Physical (iconic) models Tabletop material-handling models Mock-ups of fast-food restaurants Flight simulators

• Logical (mathematical) models Approximations, assumptions about system’s operation Often represented via computer program in appropriate software Exercise program to try things, get results, learn about model behavior Mathematical model: uses symbolic notation and mathematical

equations to represent a system.• Simulation is a type of mathematical model.



Logical Models

• If model is simple enough, use traditional mathematical analysis … get exact results, lots of insight into model Queueing theory Differential equations Linear programming

• But complex systems can seldom be validly represented by simple analytic model Danger of over-simplifying assumptions … model validity? Type III error – working on the wrong problem

• Often, complex system requires complex model, analytical methods don’t apply … what to do?


Simulation Models

• Static vs. Dynamic Does time have a role in model?

• Continuous-change vs. Discrete-change Can “state” change continuously, or only at discrete points in time? Discrete example: the number of jobs in queue changes when a new job

arrives or when service is completed for another Continuous example: the head of water behind a dam

• Deterministic vs. Stochastic Is everything for sure or is there uncertainty?

• Most operational models: Dynamic, Discrete-change, Stochastic


Components of a Simulation System

• An entity: an object of interest in the system, e.g., computing jobs in queue.

• An attribute: a property of an entity, e.g., priority class, or vector of resource requirements.

• An activity: represents a time period of a specified length, e.g. job receiving service.

• The state of a system: collection of variables necessary to describe the system at any time, relative to the objectives of the study, e.g. the number of busy servers, the number of jobs in queue.

• An event: an instantaneous occurrence that may change the system state, can be endogenous or exogenous, e.g. a new job arrival, or service time completion


Components of a Simulation System

System Entities Attributes Activities Events State VariablesBanking - Customers

- Policemen- Checking account balance- Customer's VIP status

- Making Deposits- Withdrawal

- Arrival- Departure

- Number of busy tellers- Number of customers waiting

Rapid Rail - Riders- Salesmen

- Origination- Destination

- Traveling- Eating

- Arrival at a station- Arrival at destination

- Number of riders waiting at each station- Number of riders in transit

Production - Products- Sub-assys

- Arrival time- Priority of service- Routing

- Welding station- Machining- Inspection

- Arrival of product- Machine breakdown- Shift change

- Status of machines: busy, idle, or down- Number of parts in system

Communication - Messages - Length- Destination

- Transmitting- Verifying

- Transmissioncomplete- Machine breakdown- Shift change

- Status of machines: busy, idle, or down- Number of messages waiting for transmission

Steps in a Sim. StudyFour phases:• Problem formulation, and setting

objective and overall design (step 1 to 2).

• Modeling building and data collection (step 3 to 7)

• Running of the model (step 8 to 10).• Implementation (step 11 to 12).• An iterative process.


Example of a Simulation Study



Example of a Simulation Study (Cont’d)















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01.Simulation Concepts.rev1