simulation modeling in DSS

Nora ALHarbi Enaam ALOtaibi

CPIS620

Dr.AbdualAllah ALGhamdi

Outlines• Introduction• Simulation Definition • Classification of Simulation• Major Characteristic • Advantages and Disadvantages• Process of Simulation• Type of Simulation• Simulation in Practices• Conclusion • References

Introduction • Simulation has been used for analyzing systems and too

complex decision problems.• Russian army used to simulate wars by holding field

exercise.• The movie Apollo 13 illustrated the use of simulation to

train astronauts .• NASA also uses simulations to predict rocket and

satellite trajectories.• Simulation modeling enables organizations to make

better decisions by letting them see the impact of proposed changes before they are implemented.

Definition: • “Simulation is the process of designing a model of a real

system and conducting experiments with this model for the purpose of either understanding the behavior of the system and/or evaluating various strategies for the operation of the system.”

• It is often used to conduct what-if analysis on the model of the actual system

• It is a popular DSS technique .

Cont. • Allow us to do the following:

• Model complex systems in a detailed way

• Describe the behavior of systems

• Construct theories or hypotheses that account for the observed behavior

• Use the model to predict future behavior

• Analyze proposed systems

Simulation model

Decision and uncontrollable

variables

Measure of performance or

behavior

input output

Cont.

Classification of simulation models

1- Static analysis system : Represents the system at a particular point in time

2- Dynamic analysis system : Represents the system behaviour over time

Example:• Manufacturing facility• Bank operation• Airport operations • Transportation/logistics/distribution operation• Hospital facilities• Computer network• Business process (insurance office)• Chemical plant• Fast-food restaurant• Emergency-response system

Major Characteristics of Simulation

• Imitates reality and captures its richness both in shape and behavior– “Represent” versus “Imitate”

• Technique for conducting experiments• Descriptive, not normative tool• Often to “solve” [i.e., analyze] very complex

systems/problems• Simulation should be used only when a numerical

optimization is not possible• Simulation is often used to solve very complex, risky

problems

Advantages of Simulation• Flexibility• The theory is fairly straightforward• Great deal of time compression• Experiment with different alternatives• The model reflects manager’s perspective• Can handle large and complex systems• Can answer “what-if” questions• Produces important performance measures• Often it is the only DSS modeling tool for non-structured

problems

Disadvantages of Simulation• Can be expensive and time consuming• Cannot guarantee an optimal solution• Slow and costly construction process• Cannot transfer solutions and inferences to solve

other problems (each model is unique)• So easy to explain/sell to managers, may lead to

overlooking analytical solutions• Software may require special skills so it is not so

user friendly

The process of simulation

Problemformulation

Setting ofobjectivesand overallproject plan

Modelconceptualization

Datacollection

Modeltranslation Verified?

No

Validated?

No

No ExperimentalDesign

Production runsand analysis

More runs?

Documentationand reporting

No

Implementation

Yes

YesYes

Yes

Cont.

1-Problem formulation : (statement of the problem)•the problem is clearly understood by the simulation analyst•the formulation is clearly understood by the client.

2-Setting of objectives & project plan : (project proposal)•Determine : questions that are to be answered, scenarios to be investigated, decision criteria, end-user, hardware, software, & personnel requirements.

Cont.

3- Model conceptualization : (abstract essential features)•Contains of events, activities, entities, attributes, resources, variables, and their relationships.

Assumed system

Conceptual model

Real World System

Logical model

Entity: is an object of interest in the system

Example: Health Center

Patients

Visitors

Attribute: is a characteristic of all entities, but with a specific value “local” to the entity.

Example: Patient

Age,

Sex,

Temperature,

Blood Pressure

Resources: what entities compete for , it can be changed during the simulation

Example: Health Centre

Doctors, Nurses

X-Ray Equipment

Variable: A piece of information that reflects some characteristic of the whole system, not of specific entities


Number of patients in the system,

Number of idle doctors,

Current time

State: A collection of variables that contains all the information necessary to describe the system at any time


{Number of patients in the system,

Status of doctors (busy or idle),

Number of idle doctors,

Status of Lab equipment, etc}

Event: An instantaneous occurrence that changes the state of the system


Arrival of a new patient,

Completion of service (i.e., examination)

Failure of medical equipment, etc.

Activity: represents a time period of specified length.

Example: Health Center

Surgery,

Checking temperature,

X-Ray.

Logical model

Q(t)> 0 ?

3

YESNO

2 Departure event

Q(t)=Q(t)-1B(t)=0

Generate service & schedule new departure

Collect & update statistics TB, TQ, TL, N

L(t)=L(t)-1

L : # of entities in systemQ : # of entities in queueB : # of entities in server

Cont.

4- Data collection & analysis: •Collect and analysis data for input by: Determine the random variables , and Fit distribution functions.

5- Model translation:

Coding

General Purpose Language Special Purpose Simulation Language/Software

JAVA, C++, Visual BASIC

Examples:

SIMAN, ARENA, EXTEND

Examples:

ARENA example

public static void main(String argv[]){Initialization();

//Loop until first "TotalCustomers" have departedwhile (NumberofDepartures < TotalCustomers){Event evt = FutureEventList[0]; //get imminent eventremovefromFEL(); //be rid of itClock = evt.get_time(); //advance in timeif (evt.get_type() == arrival) ProcessArrival();else ProcessDeparture();}

ReportGeneration();}

JAVA example

Cont.

6- Verification: the process of determining if the operational logic is correct. (Debugging software)

7- Validation: the process of determining if the model accurately represents the system. (by comparison of model results with the real system)

Conceptual model

Logical model

Simulation model

Real World System

VERIFICATION

VALIDATION

Cont.

8- Experimental design : it the process of Alternative scenarios to be simulated ,Number of simulation runs , and Length of each run.

9- Analysis of the results: Statistical tests for significance and ranking and for Interpretation of results.

10- Documantion &reporting: Allows to future modifications ,Creates confidence , Frequent reports , Performance measures , and Recommendations.

11- Implemantion

Simulation Types1. Monte Carlo ( probabilistic simulation)

2. Activity-scanning simulation

3. Event-driven simulation

4. Process-driven simulation

5. Time dependent (discrete simulation)

6. Visual simulation.

1-Monte Carlo

• Specify one or more of the independent variables as a probability distribution of values.

• It helps take risk and uncertainty in a system into account in the results.

2-Activity-scanning simulation

• It involves to describing activities that occur during in fixed time

• Then simulating for multiple future periods the consequences of the activities

3-Event-driven simulation

• It identifies "events" that occur in a system

• Focusing on a time ordering of the events rather than a causal or logical ordering.

4-Process-driven simulation

• It focuses on modeling a logical sequence of events rather than activities.

5-Time dependent (discrete simulation)

• It refers to a situation where it is important to know exactly when an event occurs.

• For example, in waiting line or queuing problems, it is important to know the precise time of arrival to determine if a customer will have to wait or not.

6-Visual simulation.

• Use computer graphics to present the impact of different management decisions.

• Decision makers interact with the simulated model and watch the results over time

Simulation Software• A comprehensive list can be found at

– orms-today.org/surveys/Simulation/Simulation.html• Simio LLC, simio.com• SAS Simulation, sas.com• Lumina Decision Systems, lumina.com• Oracle Crystal Ball, oracle.com• Palisade Corp., palisadae.com• Rockwell Intl., arenasimulation.com …

Simulation in Practices• Example: Simulation analysis for Red Cross blood

drives

• Red Cross collects over 6 million units of blood per year in the US.

• The system relies heavily on repeat donors who give blood on a regular basis.

• In the early 1990s the Red Cross was very concerned about how to keep these donors satisfied.

• By minimize donor time especially waiting time , and the overall time of system

Red Cross cont.

• So, they examined blood collection process via simulation model to try to develop policies that would reduce the waiting time .

• The time was broken down into three parts: the time required to prepare the donor and insert the needle , blood gathering time , and the time required to disconnect the needle and bandage the donor.

• Then , the time fit to simulation model as inputs.

Red Cross cont.

• They identified three arrival patterns by direct observation and bar code scanning devices.

1. For business organization arrivals peaked around midmorning and midafternoon .

2. For schools in mid to late morning. 3. For open community drives around midday.• Several different policy alternatives were considered

( increasing the number of beds ) • Result was improved performance and greater

satisfaction

Simulation in Practices cont.

• Example: Simulation analysis at Desiny World

• Cruise Line Operation: Simulate the arrival and check-in process at the dock.

• Discovered the process they had in mind would cause hours in delays before getting on the ship.

• Private Island Arrival: How to transport passenger to the beach area?

• Drop-off point far from the beach. • Used simulation to determine whether to invest in trams, how

many trams to purchase, average transport and waiting times, etc..

Desiny World cont.

• Bus Maintenance Facility: Investigated “best” way of scheduling preventative maintenance trips.

• Alien Encounter Attraction: Visitors move through three areas. Encountered major variability when ride opened due to load and unload times (therefore, visitors waiting long periods before getting on the ride). Used simulation to determine the length of the individual shows so as to avoid bottlenecks.

Discussion:

• Some people believe that managers do not need to know the internal structure of the model and the technical aspects of modeling. “It is like the telephone or the elevator, you just use it.” Others claim that this is not the case and the opposite is true.

Conclusion

• Simulation is the common technique in DSS

• It’s has many characteristic such as Imitates reality and conducting experiments

• Often it is the only DSS modeling tool for non-structured problems

• It’s applying in many area to solve the complex problem .

References

• CPIS620 –Chapter 10• - Introduction to Simulation Using SIMAN (2nd Edition)• Evan, J. R. and D. L. Olson, Introduction to Simulation and Risk

Analysis (2nd Edition), Upper Saddle River, NJ: Prentice Hall, 2002