System Modeling and Simulation: Understanding Complexity and Making Informed Decisions

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. 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. 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. 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. 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