1. Chapter 1 What is Simulation?

2. Fall 2001 IMSE643 Industrial Simulation What’s Simulation? Simulation – A broad collection of methods and applications to mimic the behavior of real systems, usually on a computer with proper software.

3. Fall 2001 IMSE643 Industrial Simulation Modeling Like other analysis methods taught in IE, simulation involves two major parts: Systems and, Models of them

4. Fall 2001 IMSE643 Industrial Simulation Examples: A manufacturing plant: system with machines, people, transport devices, storage spaces. A distribution network of plants, warehouses, an transportation links. A bank or other service operations, with different kinds of customers, servers, teller windows, ATMs, safe deposit boxes. A computer network with servers, clients, storage space (HDs), printers, networking capabilities, and operators. A supermarket or chain-store with inventory control, check-outs, and customer services. A chemical products plants with storage tanks, pipeline, reactor vessels, and railway tanker cars in which ship the finished goods.

5. Fall 2001 IMSE643 Industrial Simulation Why simulation? Why not just use the system? When you cannot play with the system. Physical models – flight simulators, tabletop models, matchbox models. Logical (or mathematical) models Numerical vs. Mathematical models

6. Fall 2001 IMSE643 Industrial Simulation Computer Simulation Methods for studying a wide variety of models of real-life systems by numerical evaluation using software designed to imitate the system’s operations or characteristics, often over time. Specially useful on complex system, where math. Model are not easy or not possible to build.

7. Fall 2001 IMSE643 Industrial Simulation Pros and Cons Easy to build for large & complex systems Efficiently evaluate what-if solution Model building process and results are straight- forward and easy to understand Time consuming for computer resources, rely on good computing power Results are based on random input and stochastic processes

8. Fall 2001 IMSE643 Industrial Simulation Types of Simulations Static vs. Dynamic Continuous vs. Discrete Deterministic vs. Stochastic

9. Fall 2001 IMSE643 Industrial Simulation How to do simulation? By hand – dart board game.. Programming in General-purpose languages Simulation languages High-level simulators Physical simulator Computer games

10. Fall 2001 IMSE643 Industrial Simulation Simulation fundamental concepts An Example system Drilling Machine Center Drill table Part in process Finished parts Waiting line Arrival parts

11. Fall 2001 IMSE643 Industrial Simulation The objectives: The total production (# of parts that completed) during20 minutes period Average waiting time in queue The maximum time waiting in queue The time-average number of parts in queue Average and maximum total time in the system Utilization of the drilling machine

12. Fall 2001 IMSE643 Industrial Simulation Analysis Methods (1) With the model, its inputs, and its outputs defined We need to figure out how to get (or calculate) the outputs by transforming the inputs according to the model’s logic.

13. Fall 2001 IMSE643 Industrial Simulation Analysis Methods (2) Educated Guessing Queueing Theory Linear programming Simulation

14. Fall 2001 IMSE643 Industrial Simulation Elements of simulation model Entities Attributes Global Variables Resources (Servers) Queues Statistical Accumulators Events Simulation clocks Starting and stopping

15. Fall 2001 IMSE643 Industrial Simulation Entities The “player” that moves around, change status, affect the state of the system, and affect the output performance measures Dynamic objects – created, moves around for a while and then disposed e.g. the parts, the customers, the breakdowns…

16. Fall 2001 IMSE643 Industrial Simulation Attributes To individualize entities Is a common characteristic of the entities Each entity has its own “private” (local) copy of the attribute The name refers to the attribute for each entity Change the value of an attribute on an entity will not affect other entities’ attribute values. Arena keeps track of attribute automatically. Like the local variables in C (C++).

17. Fall 2001 IMSE643 Industrial Simulation Global Variables A piece of global information that reflects some characteristic of your system All entities refer to the same copy of the variable If any entity changes the value on a variable, that changes affect all the entities. They can be access by all the entities Like the global variables in C (C++)

18. Fall 2001 IMSE643 Industrial Simulation Resources Entities often compete with each other for services from the “Resources” the represent things like personnel, equipment, machines, processors, space, or computers. Entities “seize” the resource when available, and then releases it when finished. A resource can represent a group of several “parallel” individual servers, each of which is called a unit of that resources. The number of available units of a resource can be changed during the simulation run to represent agents going on break or opening up new station when things get busy.

19. Fall 2001 IMSE643 Industrial Simulation Queue When an entity cannot get hold of a certain resource for service, it needs a place (Queue) to wait. Queue could have a capacity Queue could have different priorities Sometimes it could represent a imaginary “waiting list” not a physical queue.

20. Fall 2001 IMSE643 Industrial Simulation Statistical Accumulators The number of parts produced so far The total of waiting times in queue so far The number of parts that have passed through the queue so far The longest time spent in queue we have seen so far The total time spent in the system by all parts that have finished so far The longest time in system we have seen so far The area so far under the queue length curve Q ( t ) The highest level that Q(t) have so far attained The area so far under the server busy function

21. Fall 2001 IMSE643 Industrial Simulation Events(1) Arrivals- -- A new part shows up Schedule next new part to arrive later at next arrival time—planning next event Update the time persistent statistics Store the arriving part’s time of arrival If the drill press is idle, the arriving part goes right into service Else (if the drill press is busy) put the arriving part in queue

22. Fall 2001 IMSE643 Industrial Simulation Events(2) Departures – the part has been served and leaving Increment the number of produced statistics accumulator Compute the total time the part spent in the system Update the time-persistent statistics If there are any parts in the queue, take the first one out of the queue, compute the total queue time for the next part. Schedule the departure time of the part If the queue is empty, set the drill press to idle.

23. Fall 2001 IMSE643 Industrial Simulation Events(3) The end– the simulation is over Update the time-persistent statistics to the end of simulation Compute the final summary output performance measures.

24. Fall 2001 IMSE643 Industrial Simulation Simulation Clock The current time of simulation is simply held in a variable call the simulation clock “TNOW” Unlike the real time, the simulation clock does NOT flow continuously It lurches from time to time when event happened. The simulation clock interacts closely with the event calendar. Arena keeps tracks of simulation clock automatically.

25. Fall 2001 IMSE643 Industrial Simulation Randomness in Simulation Random inputs, Random Outputs Different results for different replication Thinking the inputs are randomly happened just like the real-world – customer arrives to the system in random times but follows certain pattern (distribution) The output is also random, and we are trying to simulation to estimate the ranges, sensitivity, levels, or reliability of the output data. This is the main reason that we should run a simulation model for several different “replications” to see how the model behave under different random inputs.