Simulation-Module 1
1. high level representations of the operation of a real world process or sys-tem:
Models
2. Changes every once in a while at discrete points in time (e.g. customer
arrivals): Discrete Models
3. Constantly changes (e.g. the weather): Continuous Models
4. Uses probability and statistics: Stochastic Models
5. Changes: Dynamic Models
6. Is the same repeatedly: Static Models
7. Discrete vs. Continuous, Stochastic vs. Deterministic, Dynamic vs. Static: -
Model Opposites
8. Analytic Methods (1st preferred)
Numerical Methods (if analytic methods don't work)
Simulation Methods (If other methods don't work): Solving a Model
9. Like solving an equation; gives an answer; fairly easy approach - e.g. stoneoff of a
cliff: Analytic Methods
10. Solves a problem without a closed form solution; e.g. modeling weather: -
Numerical Methods
11. General approach; for problems too complicated for other methods; mod-els with
randomness: Simulation Methods
12. Imitation of a real-world process or system over time; generation of arti-ficial
history to draw inferences about operating characteristics of the real system
represented: Simulation
13. Top 3 IE/Operation research/management science technologies
Used by academics and practitioners on a wide array of theoretical/applied
problems
indispensable problem-solving methodology: Simulation is
14. Describe/analyze real/conceptual system behaviorAsk
what-if questions
Aid in system design, optimization
simulate almost anything: Simulation is good for
15. Customer-based systems
Manufacturing processes Supply
Chains Health Systems
Systems with no "customers" e.g. stock prices: What can be simulated
16. Will system accomplish its goals?
Current system won't accomplish goals. Now what?Need
incremental improvement
Create specification or action plan
Solve a problem, like a bottleneck
1/4
1. high level representations of the operation of a real world process or sys-tem:
Models
2. Changes every once in a while at discrete points in time (e.g. customer
arrivals): Discrete Models
3. Constantly changes (e.g. the weather): Continuous Models
4. Uses probability and statistics: Stochastic Models
5. Changes: Dynamic Models
6. Is the same repeatedly: Static Models
7. Discrete vs. Continuous, Stochastic vs. Deterministic, Dynamic vs. Static: -
Model Opposites
8. Analytic Methods (1st preferred)
Numerical Methods (if analytic methods don't work)
Simulation Methods (If other methods don't work): Solving a Model
9. Like solving an equation; gives an answer; fairly easy approach - e.g. stoneoff of a
cliff: Analytic Methods
10. Solves a problem without a closed form solution; e.g. modeling weather: -
Numerical Methods
11. General approach; for problems too complicated for other methods; mod-els with
randomness: Simulation Methods
12. Imitation of a real-world process or system over time; generation of arti-ficial
history to draw inferences about operating characteristics of the real system
represented: Simulation
13. Top 3 IE/Operation research/management science technologies
Used by academics and practitioners on a wide array of theoretical/applied
problems
indispensable problem-solving methodology: Simulation is
14. Describe/analyze real/conceptual system behaviorAsk
what-if questions
Aid in system design, optimization
simulate almost anything: Simulation is good for
15. Customer-based systems
Manufacturing processes Supply
Chains Health Systems
Systems with no "customers" e.g. stock prices: What can be simulated
16. Will system accomplish its goals?
Current system won't accomplish goals. Now what?Need
incremental improvement
Create specification or action plan
Solve a problem, like a bottleneck
1/4
