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Introduction to simulationUNIT 1 (CHAPTER 01)

2contents

When simulation is the appropriate tool and when it is not appropriate Advantages and disadvantages of Simulation Areas of application Systems and system environment Components of a system Discrete and continuous systems Model of a system Types of Models Discrete-Event System Simulation Steps in a Simulation Study

3What is simulation?

Definition: It is the imitation of the operation of a real world process or system over time.

It involves the generation of artificial history of the system and the observation of that artificial history to draw the inferences concerning to the characteristics of the real system.

The behavior of a system as it evolves over time is studied by developing simulation model.

Simulation modeling can be used both as an analysis tool and a design tool. Analysis Tool: To predict the effect of changes to the existing systems Design Tool: To predict the performance of new systems under varying sets

of circumstances.

4When simulation is an appropriate tool?

To study the internal interactions of a computer system or a subsystem within a complex system.

To study the informational, organizational and environmental changes which affects the model’s behavior.

To gain the knowledge which may help to investigate the improvement of a model

5When simulation is an appropriate tool? Cont’d

Changing the simulation i/p’s and studying the o/p’s can produce a valuable insight

Can be used as pedagogical device to reinforce analytical solution methodologies

Can be used to experiment with new designs or policies before implementation to prepare what might happen.

To verify analytic solutions.

6When simulation is an appropriate tool? Cont’d

Simulating different capabilities can determine the requirements on it.

Animation shows a system in simulated operation can be visualized.

To study the modern systems.

7When Simulation is not appropriate?

Should not be used when the problem can be solved with common sense

Should not be used when the problem can be solved analytically.

Should not be used if it is easier to perform the direct experiments.

Not to use simulation if costs exceeds the savings.

8When Simulation is not appropriate? Cont’d

Not to be performed if the resources or time are not available

Not advised when no data available.

If managers have unreasonable expectations or if the power of simulation is over estimated , simulation might not be appropriate.

If the system behavior is too complex or can’t be defined , simulation is not appropriate.

9Advantages of simulation

New policies and all the different rules and regulation of real system can be explored.

Testing of new systems without committing resources is possible.

Hypothesis about how or why certain phenomena occur can be tested for feasibility.

Insight can be obtained about the importance of variables to the performance of the system.

10Advantages of simulation cont’d

Bottleneck analysis can be performed to discover where work in process, information, Materials and so on are being delayed excessively.

It can help in understanding how the system operates rather than how individuals think the system operates.

“what if” questions can be answered to design the new systems.

11Disadvantages of simulation

Model building requires special training.

Simulation results can be difficult to interpret.

Simulation modeling and analysis can be time consuming and expensive.

Can be used only in some cases when an analytical solution is possible or even preferable.

12Areas of Application

Manufacturing applications Wafer fabrication Business Process Simulation Construction Engineering and Project management Logistics, Supply chain and Distribution Applications Military applications Health Care Additional applications

13System & Environment

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

E.g..: production system manufacturing automobiles A system is often affected by changes occurring outside the system, such changes

are said to occur in the system environment. In modelling systems, it is necessary to determine the boundary between the

system and environment

14Components of system

Entity: Object of interest in the system. Attribute: Property of an entity. Activity: Time period of specified length State: Collection of variables necessary to describe a system at any time Event: An instantaneous occurrence that might change the state of the system. Terms such as Endogenous: describes the activities and event occur within a system Exogenous: describes the activities and events in the environment that affects the system

15examples

16Types of systems

Can be classified as discrete and continuous system Discrete system is one whose state variables change only at discrete set of points

in time E. g. : Bank and customers No. of customers change only when they arrive or service to be provided has

completed. Following figure depicts a discrete system

17Discrete system state variable

18Types of systems

A continuous system is one in which the state variables change continuously over the time

E.g. : head of water behind the time During excess water, they do flood control, for electricity they draw water Following figure depicts continuous system

19Continuous system state variable

20Model of a system

A model is defined as a representation of a system for the purpose of studying the system.

Model is nothing but simplification of the system Should be sufficiently detailed to permit valid conclusions to be drawn about the

real system Different models of the same system could be required as the purpose of

investigation changes.

21Types of models

Models can be mathematical or physical A mathematical model uses symbolic notation and mathematical equations to

represent a system A physical model is larger or smaller version of an object such as the enlargement of

atom or scaled down version of solar system Simulation models can be classified as

Static or dynamic Deterministic or stochastic Discrete or continuous

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Static model represents a system at a particular point in time Dynamic model represents the system as they change over time Eg: bank simulator from 9 am to 4 pm Deterministic model model that contains no random variables Stochastic model model which has one or more random variables as inputs. Random inputs leads to random output

23Discrete event system simulation

State variable changes only at a discrete set of point in time The simulation models are analysed by numerical rather than analytical methods Analytical methods employ the deductive reasoning of mathematics to solve the

model. Numerical methods employ computational procedures to solve mathematical

models.

24Steps in Simulation Study

Initialization phase (First phase)1. Problem Formulation2. Setting objectives and overall project plan

Model building (Second Phase)3. Model Conceptualization4. Data Collection5. Model Translation6. Verification7. Validation

Third phase8. Experimental Design9. Production runs and Analysis10. More Runs?

Documentation (Fourth phase)11. Documentation and Reporting12. Implementation

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26Problem formulation

Every study should begin with the statement of the problem Problem must be clearly understood by the analyst from those who have the

problem If the problem statement is still being developed by the analyst, it is important

that the policy makers understand and agree with the formulation.

27Setting objectives and overall project plan

The objectives indicate the questions to be answered by the simulation At this point, determination should be made concerning whether simulation is the

appropriate methodology for the problem as formulated and the objectives as stated.

Should include the plans for the study in terms of the number of people involve, the cost of study, number of days required to accomplish each phase of the work, along with the results expected in each stage.

28Model conceptualization

It is not possible to provide a set if instructions that will lead to building successful and appropriate models in every instance

Hence it is good to build simple model and build towards greater complexityy It is not necessary to have one to one mapping between the model and real

system, only essence of real system is needed. Involving the model user will both enhance the quality of the resulting model and

increase the confidence of the model user in the application of the model.

29Data collection

There is direct relation between the construction of model and collection of the needed input data

As the model changes the required data elements can also change. Data collection takes large portion of time, hence it is necessary to begin as early

as possible

30Model translation

Model must be entered into a computer recognizable format Model is converted into program to accomplish the desired result with little or no

actual coding If the problem is amenable to solution with simulation software, the model

development is greatly reduced.

31Verified?

After converting the model into program, to check whether it performs properly With complex models, it is difficult, if not impossible to translate the model

successfully in its entirely without a good deal of debugging If the input parameters and logical structure of the model are correctly

represented in the computer, verification is completed.

32Validated?

Achieved through calibration of the model An iterative process of comparing the model against the actual system behaviour

and using discrepancies between the two, the insights gained , to improve the model.

The process is repeated until the accuracy is judged acceptable

33Experimental design

The alternatives that are to be simulated must be determined 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 numbers of replications to be made of each run.

34Production runs and analysis

Used to estimate measures of performance for the system designs that are being simulated.

35More runs?

After the run is completed, the analyst determines whether additional runs are needed and what design those additional experiments should follows.

36Documentation and reporting

There are two types of documentation Program Progress

Program documentation – here the program is documented well so that if same program when to be used by another analyst, it can be easily understood hence policymakers and model users can make decisions based on analysis very easily

Progress documentation- written history of a simulation project Tells about work done and decisions made “It is better to work with many intermediate milestones that with one absolute deadline”

37implementation

The success of implementation phase depends on the previous stages If the model user has been involved during the entire model building process and

if the model user understands the nature of the model, its outputs, the likelihood of implementation is enhanced.

If the model and its underlying assumptions have not been properly communicated, then implementation will probably suffer, regardless of simulation validity.

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exercises

39Example 1

Name the several entities , attributes, events and state variables for the following systemsa) A cafeteriab) A grocery storec) A Laundromatd) A fast food restaurante) A hospital emergency roomf) A taxicab company with 10 taxisg) An automobile assembly line

40solution

a) CafeteriaEntities Diners (customers)Attributes 1. Size of appetite (thurst for hunger)

2. Entree preference (choice of main course)Activities 1. Selecting food

2. Paying for foodEvents 1. Arrival at service line

2. Departure from service lineState variables 1. Number of diners in waiting line

2. Number of servers working

41solution

b) Grocery storeEntities ShoppersAttributes 1. Length of grocery listActivities 1. Checking outEvents 1. Arrival of checkout counters

2. Departure from checkout counterState variables 1. Number of shoppers in line

2. Numbers of checkout lanes in operation

42solution

c) Laundromat (coin based- public washing machine)Entities Washing machineAttributes 1. Breakdown rateActivities 1. Repairing the machineEvents 1. Occurrence of breakdown

2. Completion of serviceState variables 1. Number of machine running

2. Number of machine in repair3. Number of machine in waiting for repair

43solution

d) Fast food restaurantEntities CustomersAttributes 1. Size of order desiredActivities 1. Placing the order

2. Paying the orderEvents 1. Arrival at the counter

2. Completion of the purchaseState variables 1. Number of customers waiting

2. Number of position operating

44solution

e) A hospital emergency roomEntities PatientsAttributes 1. Attention level requiredActivities 1. Providing the service requiredEvents 1. Arrival of the patients

2. Departure of the patientsState variables 1. Number of patients waiting

2. Number of doctors waiting

45solution

f) A taxi cab company with 10 taxisEntities FaresAttributes 1. Origination (start location)

2. Destination (end location)Activities 1. travellingEvents 1. Pick up of fare

2. Drop off of fareState variables 1. Number of busy taxi cabs

2. Number of fares waiting to be picked up

46solution

g) Automobile assembly lineEntities Robot weldersAttributes 1. Speed

2. Breakdown rateActivities 1. Spot weldingEvents 1. Breaking downState variables 1. Availability of machines

47Example 2

What are the events and activities associated with the use of your checkbook?

48solution

Event Deposit Withdrawal

Activities Writing a check Cashing a check Making a deposit Verifying the account balance Reconciling the checkbook with the bank statement

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End of unit 1THANK YOU