System Simulation Modeling chapter 1

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System Simulation & Modeling chapter-1


Simulation A Simulation is the imitation of the operation of a real-world process or system over
time.Brief Explanation
* The behavior of a system as it evolves over time is studied by developing a simulation
* This model takes the form of a set of assumptions concerning the operation of the system.
The assumptions are expressed in:
Mathematical relationships
Logical relationships
Symbolic relationships
Between the entities of the system.
Measures of performance
The model solved by mathematical methods such as differential calculus, probability
theory, algebraic methods has the solution usually consists of one or more numerical parameters
which are called measures of performance.
1.1 When Simulation is the Appropriate Tool
* Simulation enables the study of and experimentation with the internal interactions of a
complex system, or of a subsystem within a complex system.
* Informational, organizational and environmental changes can be simulated and the effect
of those alternations on the model’s behavior can be observer.
* The knowledge gained in designing a simulation model can be of great value toward
suggesting improvement in the system under investigation.
* By changing simulation inputs and observing the resulting outputs, valuable insight may
be obtained into which variables are most important and how variables interact.
* Simulation can be used as a pedagogical device to reinforce analytic solution

* Simulation can be used to experiment with new designs or policies prior to
implementation, so as to prepare for what may happen.
* Simulation can be used to verify analytic solutions.
* By simulating different capabilities for a machine, requirements can be determined.
* Simulation models designed for training, allow learning without the cost and disruption
of on-the-job learning.
* Animation shows a system in simulated operation so that the plan can be visualized.
* The modern system(factory, water fabrication plant, service organization, etc) is so
complex that the interactions can be treated only through simulation.
1.2 When Simulation is Not Appropriate
* Simulation should be used when the problem cannot be solved using common sense.
* Simulation should not be used if the problem can be solved analytically.
* Simulation should not be used, if it is easier to perform direct experiments.
* Simulation should not be used, if the costs exceeds savings.
* Simulation should not be performed, if the resources or time are not available.
* If no data is available, not even estimate simulation is not advised.
* If there is not enough time or the person are not available, simulation is not appropriate.
* If managers have unreasonable expectation say, too much soon * or the power of
simulation is over estimated, simulation may not be appropriate.
* If system behavior is too complex or cannot be defined, simulation is not appropriate.
1.3 Advantages of Simulation
* Simulation can also be used to study systems in the design stage.
* Simulation models are run rather than solver.
* New policies, operating procedures, decision rules, information flow, etc can be explored
without disrupting the ongoing operations of the real system.
* New hardware designs, physical layouts, transportation systems can be tested without
committing resources for their acquisition.
* Hypotheses about how or why certain phenomena occur can be tested for feasibility.
* Time can be compressed or expanded allowing for a speedup or slowdown of the
phenomena under investigation.
* Insight can be obtained about the interaction of variables.
* Insight can be obtained about the importance of variables to the performance of the
* Bottleneck analysis can be performed indication where work-inprocess, information
materials and so on are being excessively delayed.\
* A simulation study can help in understanding how the system operates rather than how
individuals think the system operates.
* “what-if” questions can be answered. Useful in the design of new systems.
1.4 Disadvantages of simulation
* Model building requires special training.
* Simulation results may 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
1.5 Applications of Simulation
Manufacturing Applications
1. Analysis of electronics assembly operations
2. Design and evaluation of a selective assembly station for highprecision scroll
compressor shells.
3. Comparison of dispatching rules for semiconductor manufacturing using large facility
4. Evaluation of cluster tool throughput for thin-film head production.
5. Determining optimal lot size for a semiconductor backend factory.
6. Optimization of cycle time and utilization in semiconductor test manufacturing.
7. Analysis of storage and retrieval strategies in a warehouse.
8. Investigation of dynamics in a service oriented supply chain.
9. Model for an Army chemical munitions disposal facility.
Semiconductor Manufacturing
1. Comparison of dispatching rules using large-facility models.
2. The corrupting influence of variability.
3. A new lot-release rule for wafer fabs.
4. Assessment of potential gains in productivity due to proactive retied management.
5. Comparison of a 200 mm and 300 mm X-ray lithography cell.
6. Capacity planning with time constraints between operations.
7. 300 mm logistic system risk reduction.
Construction Engineering
1. Construction of a dam embankment.
2. Trench less renewal of underground urban infrastructures.
3. Activity scheduling in a dynamic, multiproject setting.
4. Investigation of the structural steel erection process.
5. Special purpose template for utility tunnel construction.

Military Applications
1. Modeling leadership effects and recruit type in a Army recruiting station.
2. Design and test of an intelligent controller for autonomous underwater vehicles.
3. Modeling military requirements for nonwarfighting operations.
4. Multitrajectory performance for varying scenario sizes.
5. Using adaptive agents in U.S. Air Force retention.
Logistics, Transportation and Distribution Applications
1. Evaluating the potential benefits of a rail-traffic planning algorithm.
2. Evaluating strategies to improve railroad performance.
3. Parametric Modeling in rail-capacity planning.
4. Analysis of passenger flows in an airport terminal.
5. Proactive flight-schedule evaluation.
6. Logistic issues in autonomous food production systems for extended duration space
7. Sizing industrial rail-car fleets.
8. Production distribution in newspaper industry.
9. Design of a toll plaza
10. Choosing between rental-car locations.
11. Quick response replenishment.
Business Process Simulation
1. Impact of connection bank redesign on airport gate assignment.
2. Product development program planning.
3. Reconciliation of business and system modeling.
4. Personal forecasting and strategic workforce planning.
Human Systems
1. Modeling human performance in complex systems.
2. Studying the human element in out traffic control.
1.6 Systems
A system is defined as an aggregation or assemblage of objects joined in some regular
interaction or interdependence toward the accomplishment of some purpose.
Example : Production System
In the above system there are certain distinct objects, each of which possesses properties
of interest. There are also certain interactions occurring in the system that cause changes in the
1.7 Components of a System
An entity is an object of interest in a system.
Ex: In the factory system, departments, orders, parts and products are The entities.
An attribute denotes the property of an entity.
Ex: Quantities for each order, type of part, or number of machines in a Department are attributes
of factory system.
Any process causing changes in a system is called as an activity.
Ex: Manufacturing process of the department.
State of the System
The state of a system is defined as the collection of variables necessary to describe a
system at any time, relative to the objective of study. In other words, state of the system mean a
description of all the entities, attributes and activities as they exist at one point in time.

An event is define as an instaneous occurrence that may change the
state of the system.
1.8 System Environment
The external components which interact with the system and produce necessary changes
are said to constitute the system environment. In modeling systems, it is necessary to decide on
the boundary between the system and its environment. This decision may depend on the purpose
of the study.
Ex: In a factory system, the factors controlling arrival of orders may be considered to be outside
the factory but yet a part of the system environment. When, we consider the demand and supply
of goods, there is certainly a relationship between the factory output and arrival of orders. This
relationship is considered as an activity of the system.
Endogenous System
The term endogenous is used to describe activities and events occurring within a system.
Ex: Drawing cash in a bank.
Exogenous System
The term exogenous is used to describe activities and events in the environment that
affect the system. Ex: Arrival of customers.
Closed System
A system for which there is no exogenous activity and event is said to be a closed. Ex:
Water in an insulated flask.
Open system
A system for which there is exogenous activity and event is said to be a open. Ex: Bank
Discrete and Continuous Systems
Continuous Systems
Systems in which the changes are predominantly smooth are called continuous system.
Ex: Head of a water behind a dam.

Discrete Systems
Systems in which the changes are predominantly discontinuous are called discrete
systems. Ex: Bank * the number of customers changes only when a customer arrives or when the
service provided a customer is completed.
1.10 Model of a system
A model is defined as a representation of a system for the purpose of studying the system.
It is necessary to consider only those aspects of the system that affect the problem under
investigation. These aspects are represented in a model, and by definition it is a simplification of
the system.

1.11 Types of Models
The various types models are
* Mathematical or Physical Model
* Static Model
* Dynamic Model
* Deterministic Model
* Stochastic Model
* Discrete Model
* Continuous Model
Mathematical Model
Uses symbolic notation and the mathematical equations to represent a
Static Model
Represents a system at a particular point of time and also known as Monte-
Carlo simulation.
Dynamic Model
Represents systems as they change over time. Ex: Simulation of a bank
Deterministic Model
Contains no random variables. They have a known set of inputs which will
result in a unique set of outputs. Ex: Arrival of patients to the Dentist at the
scheduled appointment time.
Stochastic Model
Has one or more random variable as inputs. Random inputs leads to random
outputs. Ex: Simulation of a bank involves random interarrival and service times.

Discrete and Continuous Model
Used in an analogous manner. Simulation models may be mixed both with
discrete and continuous. The choice is based on the characteristics of the system
and the objective of the study.
1.12 Discrete-Event System Simulation
Modeling of systems in which the state variable changes only at a discrete
set of points in time. The simulation models are analyzed by numerical rather than
by analytical methods. Analytical methods employ the deductive reasoning of
mathematics to solve the model. Eg: Differential calculus can be used to determine
the minimum cost policy for some inventory models.
Numerical methods use computational procedures and are ‘runs’, which is
generated based on the model assumptions and observations are collected to be
analyzed and to estimate the true system performance measures. Real-world
simulation is so vast, whose runs are conducted with the help of computer. Much
insight can be obtained by simulation manually which is applicable for small
1.13 Steps in a Simulation study
1. Problem formulation
Every study begins with a statement of the problem, provided by policy
makers. Analyst ensures its clearly understood. If it is developed by analyst policy
makers should understand and agree with it.
2. Setting of 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. Assuming it is appropriate, the overall project plan
should include
* A statement of the alternative systems
* A method for evaluating the effectiveness of these alternatives

* Plans for the study in terms of the number of people involved
* Cost of the study
* The number of days required to accomplish each phase of the work with the
anticipated results.
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
* 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. Model conceptualization 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 model and the
collection of needed input data. Done in the early stages.
Objective kind of data are to be collected.
5. Model translation
Real-world systems result in models that require a great deal of information
storage and computation. It can be programmed by using simulation languages or
special purpose simulation software.
Simulation languages are powerful and flexible. Simulation software models
development time can be reduced.
6. Verified
It pertains to he computer program and checking the performance. If the
input parameters and logical structure and correctly represented, verification is
7. Validated
It is the determination that a model is an accurate representation of the real
system. Achieved through calibration of the model, an iterative process of
comparing the model to actual system behavior and the discrepancies between the
8. Experimental Design
The alternatives that are to be simulated must be determined. Which
alternatives to simulate may be a function of runs. For each system design,
decisions need to be made concerning
* Length of the initialization period
* Length of simulation runs
* Number of replication to be made of each run
9. Production runs and analysis
They are used to estimate measures of performance for the system designs
that are being simulated.
10. More runs
Based on the analysis of runs that have been completed. The analyst
determines if additional runs are needed and what design those additional
experiments should follow.
11. Documentation and reporting
Two types of documentation.
* Program documentation
* Process documentation
Program documentation
Can be used again by the same or different analysts to understand how the
program operates. Further modification will be easier. Model users can change the
input parameters for better performance.
Process documentation
Gives the history of a simulation project. The result of all analysis should be
reported clearly and concisely in a final report. This enable to review the final
formulation and alternatives, results of the experiments and the recommended
solution to the problem. The final report provides a vehicle of certification.
12. Implementation
Success depends on the previous steps. If the model user has been
thoroughly involved and understands the nature of the model and its outputs,
likelihood of a vigorous implementation is enhanced.
The simulation model building can be broken into 4 phases.
I Phase
* Consists of steps 1 and 2
* It is period of discovery/orientation
* The analyst may have to restart the process if it is not fine-tuned
* Recalibrations and clarifications may occur in this phase or another
II Phase
* Consists of steps 3,4,5,6 and 7
* A continuing interplay is required among the steps
* Exclusion of model user results in implications during implementation
III Phase
* Consists of steps 8,9 and 10
* Conceives a thorough plan for experimenting
* Discrete-event stochastic is a statistical experiment
* The output variables are estimates that contain random error and
therefore proper statistical analysis is required.
IV Phase
* Consists of steps 11 and 12
* Successful implementation depends on the involvement of user and
every steps successful completion.


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