1. 1 System Is a section of reality Composed of components that interact with one another Can be a subsystem Has hypothetical boundaries Can include or input the external influence (based on the purpose of study) Performs a function Source: Khoshnevis

2. 2 Models Abstraction/simplification of the system used as a proxy for the system itself Can try wide-ranging ideas in the model –Make your mistakes on the computer where they don’t count, rather for real where they do count Issue of model validity Two types of models –Physical (iconic) –Logical/Mathematical — quantitative and logical assumptions, approximations Source: Systems Modeling Co.

3. 3 Advantages of Simulation Flexibility to model things as they are (even if messy and complicated) –Avoid “looking where the light is” (a morality play): Allows uncertainty, nonstationarity in modeling –The only thing that’s for sure: nothing is for sure –Danger of ignoring system variability –Model validity Source: Systems Modeling Co.

4. 4 The Bad News Don’t get exact answers, only approximations, estimates –Also true of many other modern methods –Can bound errors by machine roundoff Get random output (RIRO) from stochastic simulations –Statistical design, analysis of simulation experiments –Exploit: noise control, replicability, sequential sampling, variance-reduction techniques –Catch: “standard” statistical methods seldom work Source: Systems Modeling Co.

5. 5 Remarks on pitfalls Inappropriate levels of complexity Lengthy development time Inherent inexactness of results Misinterpretation of simulation results Other suitable techniques Simulation is an art rather than science Source: Khoshnevis

6. Example 2: Packing Station with break and carts Refer to handout on web page. Objectives: Relationship of different goals to their simulation model Preparation of input information for model creation Input to and output from simulation software (Arena) Creation of summary tables based on statistical output for final analysis IE 429, Parisay, January 2010

7. Example 2 Logical Model IE 429, Parisay, January 2010

8. You should have some idea by now about the answer of these questions. * What is a “queuing system”? * Why is that important to study queuing system? * Why do we have waiting lines? * What are performance measures of a queuing system? * How do we decide if a queuing system needs improvement? * How do we decide on acceptable values for performance measures? * When/why do we perform simulation study? * What are the “input” to a simulation study? * What are the “output” from a simulation study? * How do we use output from a simulation study for practical applications? * How should simulation model match the goal (problem statement) of study? IE 429, Parisay, January 2010

9. Simulation Terms Entities: “Players” that move around, change status, affect and are affected by other entities Resources: What entities compete for: People, Equipment, Space. Entity seizes a resource, uses it, then releases it. Queues: Place for entities to wait when they can’t move on Attributes: Characteristic of all entities to describe and or differentiate Process: The task being performed with some duration, usually with random length of time