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IE 594 : Research Methodology – Discrete Event Simulation

IE 594 : Research Methodology – Discrete Event Simulation. David S. Kim Spring 2009. Research Methodology - Simulation. Simulation as a research tool Research in simulation Focus here is on simulation of discrete event dynamic systems. Simulation as a Research Tool.

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IE 594 : Research Methodology – Discrete Event Simulation

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  1. IE 594 : Research Methodology – Discrete Event Simulation David S. Kim Spring 2009

  2. Research Methodology - Simulation • Simulation as a research tool • Research in simulation • Focus here is on simulation of discrete event dynamic systems

  3. Simulation as a Research Tool • What is the role of simulation in the research? • Used as the tool to understand general system dynamics and generate insights? • Comparison or validation of an approximation or heuristic? • What is the role of simulation in the buffer allocation paper (Conway paper)?

  4. Simulation as a Research Tool – Case 1 • Why simulation? • The system of interest has a performance function f that transforms controllable system parameters into system performance. • The function f is unknown or computationally intractable. • Markov chain that approximate the throughput function have too many states • Analytical models do not exist. • What is f in the buffer allocation paper (Conway et al.)? • What are the input parameters?

  5. Simulation as a Research Tool – Case 2 • Why simulation? • A heuristic or analytical approximation has been developed to model some system performance measure. • The development of the approximation requires simplifying assumptions/approximations. • The conjecture is that the analytical model is still a reasonable representation of the real system. • Simulation is being used to support or refute this conjecture. • What are the simplifying assumptions/ approximations used in the Nagarajan paper?

  6. Simulation as a Research Tool • What is the real system being simulated? • Is there evidence or research precedent indicating that the simplified model being simulated characterizes real systems? • Is this generally known or do references need to be cited? • What’s being simulated in the buffer paper?

  7. Simulation as a Research Tool • Are the assumptions applied in the simulation clearly stated? • Distributions used. • Operational protocols, e.g., blocking, etc. • Correlation? • Can you simulate the same system? • Steady State vs. Terminating • Number of runs • Length of runs • Some models take a long time to “settle down” • Warm-up

  8. Simulation as a Research Tool • Verification & validation • Mainly applies to studying a real system or a detailed representation • How was this conducted? • Results compared to an existing system? • Comparisons made to existing analytical results? • Extreme cases tested?

  9. Simulation as a Research Tool • Experimental design • How was the “parameter space” explored? • Experimental design? • Random systems? • Worse case systems? • Standard problem library? • The importance of this depends on the way the simulation was used • If simulating to understand a system and gain insight, these issues become more important.

  10. Simulation as a Research Tool • Output analysis • Were proper statistical procedures applied to the output? • e.g., confidence intervals • What is the variance around the average results?

  11. Research in Simulation • Research into the various aspects of simulation mechanisms or conducting simulation studies • Generating random numbers • Generating random variates • Use of distributions in simulations • Dealing with extremely large simulation models • Optimization of systems using simulation • etc., … • See general topics for the WSC

  12. Research in Simulation • Simulation optimization • The system of interest has a performance function f that transforms controllable system parameters into system performance. • The function f is unknown or computationally intractable. • Simulation is used to approximate f • How can simulation be used to optimize f subject to constraints and costs?

  13. Research in Simulation • Examples of simulation optimization research • Perturbation analysis • Use of global optimization heuristics (e.g., genetic algorithms) • Vergara paper.

  14. Research in Simulation • Simulation modeling methodology • How can different systems be modeled? • Distributed/parallel simulation • Methods for verification/validation • Short cuts/approximations • Where is the Sharma paper ?

  15. Conclusions • Many of the items discussed today are things to look for when evaluating research and when conducting research. • Often the importance of each item will depend on the circumstances, the existing body of research, and the objectives of the research.

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