Simulation

A simulation is the imitation of the operation of a real-world process or system over time.

 Simulation can be done by hand or on a computer.

 Simulation involves the generation of an artificial history of a system.
 Simulation observes that artificial history to draw inferences concerning the operating
characteristics of the real system.
 The behavior of a system as it evolves over time is studied by developing a simulation
model.
 This model usually takes the form of a set of assumptions concerning the operation of the
system
 These assumptions are expressed in mathematical, logical, and symbolic relationships
between the entities, or objects of interest, of the system.

Advantages of simulation

 New policies, operating procedures, information flows, organizational procedures, and so

on can be explored without disrupting ongoing operations of the real system.
 New hardware .designs, physical layouts, transportation systems, and so on can be tested
without committing resources for their acquisition.
 Hypotheses about how or why certain phenomena occur can be tested for feasibility.
 A simulation study can help in understanding how the system operates rather than how
individuals think the system operates.
 Able to test a product or system works before building it.
 Can speed things up or slow them down to see changes over long or short periods of time.
 Simulation allows you to explore ‘what if’ questions and scenarios without having to
experiment on the system itself.
 It helps you to gain insight into which variables are most important to system
performance.
 Simulation models can be modified to accommodate the changing environments of the
real situation.

Disadvantages of simulation

 Model building requires special training and experiences.

 Sometimes Simulation results can be difficult to interpret
 The cost of running several different simulations may be high.

When simulation is the appropriate tool

 Simulation enable the study of internal interaction of a subsystem with complex system
  Informational, organizational and environmental changes can be simulated and find their
effects
 A simulation model help us to gain knowledge about improvement of system
 Finding important input parameters with changing simulation inputs
 Simulation can be used with new design and policies before implementation

When simulation is not appropriate

 When the problem can be solved by common sense.

 When the problem can be solved analytically.
 If it is easier to perform direct experiments.
 If cost exceed savings.
 If resource or time are not available.
 If system behavior is too complex.

Areas of application

1) Manufacturing Applications
2) Semiconductor Manufacturing
3) Construction Engineering and project management
4) Military application
5) Logistics, Supply chain and distribution application
6) Transportation modes and Traffic
7) Business Process Simulation
8) Health Care
9) Automated Material Handling System (AMHS)
10) Risk analysis
11) Computer Simulation
12) Network simulation

System

A system is defined as a group of objects that are joined together in some regular interaction
toward the accomplishment of some purpose.

An example is a production system of manufacturing automobiles. The machines, component

parts and workers operate jointly along an assembly line to produce a high quality vehicle.

System environment

A system is often affected by changes occurring outside the system. Such changes are said to
occur in the system environment.
For factory system, arrival order is the part of the environment because it has effect on supply
and demand.

For banks: arrival of customers

Components of system
Entity
An object of interest in the system: Machines in factory, customers in bank.

Attribute
The property of an entity: speed, capacity (factory), balance in their checking account (bank)

Activity
A time period of specified length: welding, stamping (factory), making deposit (bank)

State
A collection of variables that describe the system in any time: status of machine (busy, idle,
down,…)the number of busy tellers, the number of customers waiting in line or being served,
and the arrival time of the next customer.

Event
An instantaneous occurrence that might change the state of the system: breakdown Endogenous
The term endogenous is used to describe Activities and events occurring within a system the
completion of service of a customer are an endogenous event.

Exogenous
The term endogenous is used to describe Activities and events in the environment that affect the
system. In the bank study, the arrival of a customer is an exogenous event

Examples of systems and components

Systems Entities Attributes Activities Events State Variables
Banking Customers Checking Making Arrival, Number of busy
account deposit departure tellers, number of
balance customers waiting
Production Machines Speed, Welding, Breakdown Status of machines
capacity stamping (busy, idle or
down)
Communications Messages Length, Transmittin Arrival at Number of waiting
destination g destination to be transmitted
Inventory Warehouses Capacity Withdrawin Demand Level of inventory,
 g backlogged
demand

Discrete and continuous system

A discrete system is one in which the state variables change only at a discrete set of points in
time: bank is an example of a discrete system. The state variable, the number of customers in the
bank, changes only when a customer arrives or when the service provided a customer is
completed

Figure: Discrete system

Continuous system
A continues system is one in which the state variables change continuously over time: Head of
water behind the dam
 Figure: Continuous system
Model of a system
A model is defined as a representation of a system for the purpose of studying the system.

Types of model
Models can be classified as being mathematical or physical.

A mathematical model uses symbolic notation and mathematical equations to represent a system.
A simulation model is a particular type of mathematical model of a system.

Simulation models may be further classified as being static or dynamic, deterministic or

stochastic, and discrete or continuous.

A static simulation model, sometimes called a Monte Carlo simulation, represents a, system at
a particular point in time.

Dynamic simulation models represent 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.

Simulation models that contain no random variables are classified as deterministic.

Deterministic models have a known set of inputs, which will result in a unique set of outputs. For
example, Deterministic arrivals would occur at a dentist's office if all patients arrived at the
scheduled appointment time.

A stochastic simulation model has one or more random variables as inputs. Random inputs lead
to random outputs. The simulation of a bank would usually involve random inter-arrival times
and random service times are an example of a stochastic simulation model.
Continuous simulation model: System state variables evolve continuously in time. For
example: classical mechanics

Discrete simulation model: System state variables evolve only at discrete points in time. For
example: queuing, inventory etc.

However, a discrete simulation model is not always used to model a discrete system, nor is a
continuous simulation model always used to model a continuous system. Tanks and
pipes are modeled discretely by some software vendors, even though we know that fluid flow is
continuous.

In addition, simulation models may be mixed, both discrete and continuous.

Discrete-event system simulation
Discrete-event systems simulation is the modeling of systems in which the state variable changes
only at a discrete set of points in time. In discrete event simulation, the models of interest are
analyzed numerically, usually with the aid of a computer.

Figure: Types of model

Steps in simulation study

 Figure: Steps in simulation study

1. Problem formulation: Every study should begin with a statement of the problem. If the
statement is provided by the policymakers or those that have the problem, the analyst
must ensure that the problem being described is clearly understood. If a problem
statement is being developed by the analyst, it is important that the policymakers
understand and agree with the formulation.
2. Setting of objectives and overall project plan: The objectives indicate the questions to
be answered by simulation. The overall project plan should include a statement of the
alternative systems to be considered and of a method for evaluating the effectiveness of
these alternatives. It should also include the plans for the study in terms of the number of
people involved, the cost of the study, and the number of days required to accomplish
each phase of the work, along with the results expected at the end of each stage.
3. Model conceptualization: The construction of a model of a system is probably as much
art as science. The art of modeling is enhanced by an ability to abstract the essential
features of a problem, to select and modify basic assumptions that characterize the
system, and then to enrich and elaborate the model until a useful approximation results.
Thus, it is best to start with a simple model and build toward greater complexity. It is
advisable to involve the model user in model conceptualization.
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. The objectives of
the study dictate, in a large way, the kind of data to be collected.
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. The modeler must decide whether to program the model in a
simulation language or to use special-purpose simulation software. For manufacturing
and material handling, there exists such software as Simio, AnyLogic, Arena, AutoMod ,
Enterprise Dynamics Extend, Flexsim, ProMode, SIMUL8 etc. Simulation languages are
powerful and flexible.
6. Verification: Verification pertains to the computer program that has been prepared for
the simulation model. Is the computer program performing properly? if the input
parameters and logical structure of the model are correctly represented in the computer,
verification has been completed.
7. Validation: Validation usually is achieved through the calibration of the model, an
iterative process of comparing the model against actual system behavior and using the
discrepancies 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.
Mainly software aids this process.
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: program and
progress. Program documentation is necessary for numerous reasons. 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. This will create confidence in the program, so that
model users and policymakers can make decisions based on the analysis.

Another reason for documenting a program is so that model users can change parameters
at will in an effort to learn the relationships between input parameters and output
measures of performance or to discover the input parameters that “optimize” some output
measure of performance.

Progress reports provide the important, written history of a simulation project. Project
reports give a chronology of work done and decisions made. Frequent reports (monthly,
at least) create awareness, correct misunderstanding early and help to solve the problem
easily. For progress reporting a project log which includes comprehensive record of
 accomplishments, change requests, key decisions, and other items of importance is
helpful.
The results of all the analysis should be reported clearly and concisely in a final report.
This will allow the model users (now the decision makers) to review the final
formulation, the alternative systems that were addressed, the criteria by which the
alternatives were compared, the results of the experiments, and the recommended
solution(s) to the problem. Furthermore, if decisions have to be justified at a higher level,
the final report should provide a vehicle of certification for the model user/decision
maker and add to the credibility of the model and of the model-building process.

12. Implementation: If the model user has been involved during the entire model-building
process and if the model user understands the nature of the model and its outputs, the
likelihood of a vigorous implementation is enhanced