• 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
• 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
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
28.1 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,
• This data may be found using data provided by the manufacturer, sampled in-house, etc.
• 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.
• Simulation software will provide information such as,
- production rates
- machine usage
- buffer size
- work in process
28.3 Design of Experiments
• WHAT? combinations of individual parameters for process control are varied, and their effect on the output quality are measured. From this we determine the sensitivity of the process to each parameter.
• WHY? Because randomly varying individual parameters takes too long.
• e.g. A One-Factor-At-A-Time-Experiment
• The example shows how the number of samples grows quickly.
• A better approach is designed experiments
• e.g. DESIGNED EXPERIMENT for springs in last section
28.4 Running the Simulation
• When a simulation is first run it will be empty. If it is allowed to run for a while it will settle down to a steady state. We will typically want to,
- run the simulation for a long time
- or, delay the start of data collection
- or, preload the system will parts
28.5 Decision Making Strategy
• The general sequence of thought when making decisions is,
• General properties of strategy include,
- time horizon
- concentration of effort
- patterns of decisions
• The levels of strategies include,
• Decisions can be categorized,
- vertical integration
- production planning/material control
• Typical criteria for making decisions might include,