Chapter 5 Analyzing and Accessing Simulation Output

LEARNING OBJECTIVES

• To be able to recognize the different types of statistical quantities used within and produced by simulation models.

• To be able to analyze finite horizon simulations via the method of replications.

• To be able to analyze infinite horizon simulations via the method of batch means and the method of replication-deletion.

• To be able to compare simulation alternatives and make valid decisions based on the statistical output of a simulation.

• To be able to access output from KSL simulation models in many different forms.

Because the inputs to the simulation are random, the outputs from the simulation are also random. You can think of a simulation model as a function that maps inputs to outputs. This chapter presents the statistical analysis of the outputs from simulation models.

In addition, a number of issues that are related to the proper execution of simulation experiments are presented. For example, the simulation outputs are dependent upon the input random variables, input parameters, and the initial conditions of the model. Initial conditions refer to the starting conditions for the model, i.e. whether or not the system starts “empty and idle”. The effect of initial conditions on steady state simulations will be discussed in this chapter.

Input parameters are related to the controllable and uncontrollable factors associated with the system. For a simulation model, all input parameters are controllable; however, in the system being modeled we typically have control over only a limited set of parameters. Thus, in simulation you have the unique ability to control the random inputs into your model. This chapter will discuss how to take advantage of controlling the random inputs.

Input parameters can be further classified as decision variables. That is, those parameters of interest that you want to change in order to test model configurations for decision-making. The structure of the model itself may be considered a decision variable when you are trying to optimize the performance of the system. When you change the input parameters for the simulation model and then execute the simulation, you are simulating a different design alternative.

This chapter describes how to analyze the output from a single design alternative and how to analyze the results of multiple design alternatives. The focus of this chapter is on understanding the types of data produced by a discrete-event simulation and how to analyze that data. The KSL facilitates the capture and analysis of various statistical quantities. This chapter will present the most common and useful approaches to instrumenting a model and extracting the captured data. Although the focus will be on demonstrating this functionality on simple models, you will readily see how easily the approaches can be scaled up to larger models with substantial output requirements.

NOTE! This chapter provides a series of example Kotlin code that illustrates the use of KSL constructs for working with data generated from KSL models. The full source code of the examples can be found in the accompanying KSLExamples project associated with the KSL repository. The files for each example of this chapter can be found here.

To begin the discussion you need to build an understanding of the types of statistical quantities that may be produced by a simulation experiment.