1.  1  GSLM 53300 System Simulation Yat-wah Wan Room: B317; Email: ywan; Ext: 3166

2.  2  Agenda  house keeping issues  applications of simulation  useful information from Arena  systems, models, and simulation

3.  3  House-Keeping Issues  prerequisite/background: undergraduate probability and statistics  aims and objectives: covering  simulation of discrete-event systems  Arena as the main tool, plus others (spreadsheet)  a little bit on modeling and statistical issues of simulation

4.  4  Contents  first 13 lectures: first 8 Ch of the textbook  Ch 1What is simulation?  Ch 2Fundamental simulation concepts  Ch 3A guided tour through Arena  Ch 4Modeling basic operations and inputs  Ch 5Modeling detailed operations  Ch 6Statistical analysis of output from terminating simulations  Ch 7Intermediate modeling and steady-state statistical analysis  Ch 8Entity transfter

5.  5  Contents  last 4 lectures  special features & unconventional models of Arena  generation of random variates  examples of simulation applications

6.  6  Textbook and References  textbook  Kelton, W. David, Randall P. Sadowski, and David T. Sturrock (2010) Simulation with Arena  references  Hoover, Stewart V. and Ronald F. Perry (1989) Simulation: A Problem-Solving Approach  Law, Averill M. and W. David Kelton (2000) Simulation Modeling and Analysis  Ripley, Brian D. (2006) Stochastic Simulation  Ross, Sheldon M. (2006) Simulation

7.  7  Assessments  Assignments30%  Project40%  Final Examination30%

8.  8  Applications of Simulation

9.  9  Examples of Simulation  searching for “ simulation ” on web  Games Games  Solar System Simulator Solar System Simulator  Simulating Fire Patterns in Heterogeneous Landscapes Simulating Fire Patterns in Heterogeneous Landscapes  Arena Software Arena Software  Example 1, Example 2 Example 1Example 2  ….. etc.

10.  10  Reasons to Use Simulation  mimic reality when the real system is  not available  costly to build  dangerous to operate  difficult to visualize  slow in evolution  difficult to predict  both deterministic and stochastic systems how about analytical methods?

11.  11  From System to Simulation From System to Simulation

12.  12  Applications of Simulation Applications of Simulation  Interfaces Interfaces  simulation papers in 2007 & 2008 simulation papers in 2007 & 2008  15 (out of 172) titles in two years 15 (out of 172) titles in two years market- ing SCMNLPStatistic fore- casting Invent- ory DPLP logisticsIPnetwork15simulat- ion 102233 16 212 61714

13.  13  Useful Information from Arena

14.  14  Download Arena 12.0  McGraw-Hill Web site McGraw-Hill Web site  student center: download software,  installation: STUDENT

15.  15  Useful Information from Arena  Start/All Programs/Rockwell Software/Arena/Online Books  C:\Program Files\Rockwell Software\Arena  Book Examples  Examples  Online Books  Smarts

16.  16  Systems, Models, and Simulation

17.  17  System, Model, & Solution systemmodel solution Role of models: describe, explain, predict, control, optimize Simulation: a special way to find the solution of a model

18.  18  Our Simulation computer simulation, after all most for stochastic systems, though …  required knowledge  modeling (state, dynamics, etc.)  analysis (input, output, verification and validation, variance reduction, optimization)  a computer language and a simulation package

19.  19  Pillars of Simulation simulation  Which is the most important? modeling computer: languages & software analysis: probability & statistics

20.  20  System, Model, & Solution systemmodel solution  how to represent a real system?  stochastic inputs  what information to carry  model correctly (setting the model right)?  correct model (setting the right model)? art  how to analyze?  how to optimize?

21.  21  Issues to Simulate a System  first: the amount of information to carry to represent the system (i.e., the state of the system)  second: the evolution of the state of the system (i.e., the dynamics of the system)  third: the medium to realize the (evolution of the) system  fourth: the method to represent the system dynamics in the selected medium  fifth: the analysis, control, and optimization of the simulation model

22.  22  Examples  simulation projects of increasing complexity  well-defined dynamics  chessboard  differential equations  well-defined problems well-defined problems  simulation of real-life systems