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Jack H.Integration and automation of manufacturing systems.2001.pdf
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page 572

22. SIMULATION

Some complex systems can’t be modeled because of,

-random events

-changing operating conditions

-too many interactions

-exact solutions don’t exist

Simulation is used to determine how these systems will behave

Simulation typically involves developing a model that includes discrete stations and events that occur with some probable distribution.

We can then examine the simulation results to evaluate the modeled system. Examples include,

-machine utilization

-lead time

-down time

-etc.

This is a very effective tool when considering the effect of a change, comparing decision options, or refining a design.

Some simulation terms include,

System - the real collection of components

Model - a reasonable mathematically (simpler) representation of the system

State - the model undergoes discrete changes. A state is a ‘snapshot’ of the system Entity - a part of the system (eg machine tool)

Attributes - the behavior of an entity

Event - something that changes the state of a machine

Activity - when an entity is going through some activity. (eg, press cycling) Delay - a period of time with no activity

Good approach to simulation,

1.Determine what the problem is

2.Set objectives for the simulation

page 573

3.Build a model and collect data

4.Enter the model into a simulation package

5.Verify the model then check for validity

6.Design experiments to achieve goals

7.Run simulations and collect results

8.Analyze and make decisions

22.1 MODEL BUILDING

If we are building a model for a plant floor layout, we will tend to have certain elements,

-material handling paths (transfer)

-buffers/waiting areas (delays)

-stock rooms (source)

-shipping rooms (destination)

-machine tools (activities)

Some of these actions can be stated as exact. For example, a transfer time can be approximated and random (manual labor), or exact (synchronous line), or proportional to a distance.

Some events will occur based on availability. For example, if there are parts in a buffer, a machine tool can be loaded and activity occurs.

Some activities and events will be subject to probabilities. Consider that the operation time in a press may vary, and there is probability of scrapping a part.

The random variations can be modeled as,

-discrete - for individual units

-continuous for variations

Well known distributions include,

page 574

Normal/Gaussian

mean

Probability Density

Poisson/Exponential

Probability Density

1

0.5

0

mean

Cumulative Probability

1

0

Cumulative Probability

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Uniform

 

 

 

 

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mean

 

 

 

 

mean

 

 

 

 

 

 

Probability Density

 

Cumulative Probability

 

Normal/Gaussian

 

1

 

0

mean

mean

Probability Density

Cumulative Probability

• This data may be found using data provided by the manufacturer, sampled in-house, etc.

22.2 ANALYSIS

• To meet goals, simple tests may be devised. These tests should be formulated as hypotheses. We can then relate these to the simulation results using correlation.