Simulation Data Analysis

Paulus TangkereIndustrial Model Simulation

Session 5

ptSimulation Data

• Data Types– Input Data – that affect the system’s performance– Output Data – are generally used to measure

performance levels• Data Sources

– Collection– Generated

ptInput Data Analysis

• Input data provide the driving force for a simulation model.

• A fundamental concept to define the input data is to break down the types of data into as many different independent types as possible.

The analyst must be prepared to identify types of input data particular to the specific project.

ptInput Data Categories

• System Entities – elements that are processed by the system.– customers, passengers, job orders, time between

arrivals to the system, the number of entities in an arrival, and entity processing times.

• System Resources – parts of the system that process the system entities.– personnel, manufacturing equipment, vehicles, break

times, breakdown or failure rates, operating capacities, and movement speeds.

ptInput Data Development

1. Collect data from the real system – requires a substantial time and resource commitment.

2. Identify a probability distribution to represent the input process – frequency distribution or histogram of the data.

3. Choose parameters that determine a specific instance of the distribution family

4. Evaluate the chosen distribution and the associated parameters for good-of-fit.

ptSources for Input Data

• Historical records• Manufacturer specifications• Vendor claims• Operator estimates• Management estimates• Automatic data capture• Direct observation

ptCollecting Input Data

• Data collection devices – either manually or with the assistance of electronic devices.

• Time collection mode and units – time intervals and types of units to use.

• Other data collection considerations – unbiased data and avoid process disruption.

pt

Common• Uniform• Bernoulli • Normal• Lognormal• Exponential• Poisson

Special • Gamma• Beta• Erlang• Weibull• Triangular

Distribution Families

ptDistribution Tests

1. Graphic approach2. Chi-square test3. Kolmogorov–Smirnov test4. Square error

ptOutput Data Analysis

• Output analysis deals with drawing inferences about the actual system based on the simulation output – be careful in interpreting the simulation results.

• The most valuable benefit from simulation is to gain insight, not necessarily to find absolute answers.

ptOutput Data Analysis

• To provide decision recommendations with respect to the objectives of the simulation project – one or more performance measures for the system.

• Depends on objectives of the simulation study and nature of the system, types simulation can be classified into – Terminating simulation– Non-terminating simulation

ptTerminating Simulation

• Runs over the simulation time, T(E), E is a specified event that stop the simulation. Ex. Bank office hours.

• The analysis process of terminating simulation consist of following steps1. Replication analysis2. Production simulation runs3. Statistical analysis of the simulation run results4. Economic analysis of statistical analysis results

ptNon-terminating Simulation

• Run continuously over a long period of time.• The analysis process of non-terminating

simulation consist of following steps1. Starting conditions2. Determining steady state3. Addressing autocorrelation4. Length of replication5. Batching method

ptCase – Service Station

ptCase – Service Station

Min : 1Max : 8Mean: 3,125Stdev: 2,295

ptCase – Service Station

Min : 1Max : 8Mean: 3,125Stdev: 2,295

Distribution FunctionUNIFORM

p(x) = 12,5%; x=1,....,8

ptCase 2– Assembly lineA robot is used to install the doors on automobiles along an assembly line. It was thought that the installation times followed a normal distribution. The robot is capable of measuring installation times accurately.A sample of 20 installation times was automatically taken by the robot, with the following results, where the values are in seconds:

ptCase 2– Assembly line

ptCase 3– Assembly line