Software for Queueing Analysis

Software for Queueing Analysis

Copyright: © 2018 |Pages: 37
DOI: 10.4018/978-1-5225-5264-2.ch008

Abstract

Chapter 8 gives a brief discussion of computer simulation for discrete events. The chapter lists software programs in the technical literature that outline programs for the simulation of discrete events, both of commercial origin and free programs. In addition to the lists submitted, the authors present specialized packages for analysis and simulation of waiting lines in the R language. Statistical considerations are presented, which must be taken into account when obtaining data from simulations in situations of waiting lines. Chapter 8 presents three packages of the statistical program R: the “queueing” analysis package provides versatile tools for analysis of birth- and death-based Markovian queueing models and single and multiclass product-form queueing networks; “simmer” package is a process-oriented and trajectory-based discrete-event simulation (DES) package for R; and, the purpose of the “queuecomputer” package is to calculate, deterministically, the outputs of a queueing network, given the arrival and service times of all the customers. It also uses simulation for the implementation of a method for the calculation of queues with arbitrary arrival and service times. For each theme, the authors show the use of the packages in R.
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Computer Simulation

Computer simulation is the production of data about the behavior of a system or process, using an algorithm or model that reproduces the characteristics that are of interest. This algorithm or model is programmed in computer where the data of the behavior under study is also collected.

Computer simulation is used to model systems or processes with practical difficulties to be studied in a direct analytical way. To perform a computer simulation, a model or algorithm is programmed that reproduces the characteristics that are of interest in the study of the system or process. When executing the simulation program, we obtain data that allows us to understand the behavior that is reproduced in the system or process. The data obtained serve to carry out the necessary analyzes.

Computer simulation uses a programmed algorithm that represents the behavior of a system over time. The programmed algorithm uses a model with logical and mathematical relations of the elements that are identified as important to explain the observed behavior of the system under study.

The model or algorithm constructed to reproduce the behavior under study is validated. This implies, among other tests, that a previously observed behavior is reproduced with known conditions. The model must also allow us to obtain useful data for the analyzes that are planned to be carried out.

In queueing systems, the evolution of the system in time depends on the interactions of the timing of various discrete events. The state of such dynamic systems changes only at these discrete instants of time instead of continuously. We shall call such systems discrete event dynamic systems.

We can visualize such a discrete event dynamic system consisting of jobs and resources. Jobs travel from resource to resource demanding and competing for service. The dynamics of the system is determined by the interactions of the timings of various discrete events associated with the jobs and resources.

Discrete event simulation utilizes a mathematical and logical model of a system that portrays state changes at precise points in simulated time. Both the nature of the state changes and the time at which the changes occur mandate precise description.

Discrete event simulation languages provide the constructs of jobs, resources, timing of events. and logical tests while the coding in such languages produces the description of the specific systems.

These specialized programs allow the person who wants to perform the simulation of a waiting line system to be able to concentrate on the details that define the features that make the system under study of special interest.

Most of the efforts in simulation have to do with the following:

  • 1.

    Good language design and friendly software to make simulation modeling easy;

  • 2.

    Statistical analysis of outputs,

Software Lists

Every two years the magazine “OR/MS Today” has published reviews of software for discrete event simulation, (Swain, 2013) (Swain, 2015), and keeps a site on the network for consulting the most up-to-date results of the review. The document warns that the review should not be taken as complete but is representative of the available simulation packages. The original article presents 55 products from 31 vendors.

Using the results of Swain, Dias et al. as a basis, in 2016 they published an article that presents 19 commercial simulation programs based on popularity. This popularity is a measure made up by a combination of several measurements that include instances on the world-wide web and in scientific publications. The authors state that popularity does not ensure better quality or that any one program is a better simulation tool. However, there could be a positive correlation between popularity, quality and a better use of simulation. The authors say that the list was created using a subjective evaluation of a set of parameters. If, alternatively, we ere to use different parameters with different weightings we could get different results. Furthermore, the values of the parameters change over time and the evaluation needs to be updated to reflect these changes.

Furthermore, the University of Windsor professor, Myron Hlynka, maintains a list of queueing software, containing 73 products. (Hylinka, 2014).

A review of free discrete event simulation software is reported by Dagkakis and Heavey in 2016. However, the discussion of free software omits the discrete event simulation packages in the R statistical programming language: “simmer” and “queuecomputer”.

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