Overall Structure of Java Simulation of a Single-Server Queue

Main() program
Start simulation.
call Initialization()
Initialize the model.
main progam
Remove imminent event from FutureEventList.
Advance simulation time to event time.
main program
Call event routine based on event type.
call ProcessArrival()
Execute arrival event.
call ProcessDeparture()
Execute departure event.
Simulation
over
?
call ReportGeneration()
Generate final report.
NO
YES

Title of Project: Single-Server Queue Simulation in Java

Abstract:

Discrete Event is the technique which is used to model the real world scenarios. A discrete-time single server queue with FIFO discipline and general independent service times is studied. Single channel queuing system can be seen in places such as banks, post offices etc, where one single queue will diverge into few counters. The moment a customer leaves the service station, the customer at the head of the queue will go to the server. Single-server queues are, perhaps, the most commonly encountered queuing situation in real life. One encounters a queue with a single server in many situations, including business (e.g. sales clerk), industry (e.g. a production line), and transport (e.g.: a queue that the customer can select from). Consequently, being able to model and analyze a single server queue's behavior is a particularly useful thing to do. The disadvantage of single channel queue is that the queue length seems to be very long, thus it can discourage customers from joining the queue.

Simulation in JAVA:

Java is a general purpose programming language which is widely used programming language that has been used extensively in simulation. Any discrete event simulation model written in Java contains the components like system state, entities and attributes, sets, events, activities and delays. Some of the common components which is used almost in all models are:

...ICS 2307 SIMULATION AND MODELLING
Course Outline
Systems modelling – discrete event simulation
Design of simulation experiments simulation
Language probability and distribution theory
Statistical estimation, inference and random number generators
Sample event sequences for random number generation
Translation of models for simulation application
References
Simulation modelling and analysis
Introduction
Computers can be used to imitate (simulate) the operations of various kinds of real world facilities or processes. The facility or process of interest is usually called a system and in order to study it scientifically, we often have to make a set of assumptions about how it works.
These assumptions which usually take the form of mathematical or logical relationships constitute a model that is used to try to gain some understanding of how the corresponding system behaves. If the relationships that propose the model are simple enough, it may be possible to use mathematical methods to obtain exact information on questions of interest. This is called an analytic solution. However, most real world systems are too complex to allow realistic models to be evaluated analytically. Such models must be studied by means of simulation.
In a simulation, we use a computer to evaluate a model numerically and data is gathered in order to estimate the desired true...

...www.ncetianz.webs.com
System Modeling And Simulation Notes —————— Presented By
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CHAPTER – 1
INTRODUCTION TO SIMULATION
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Simulation A Simulation is the imitation of the operation of a real-world process or system over time. Brief Explanation • The behavior of a system as it evolves over time is studied by developing asimulation model. • This model takes the form of a set of assumptions concerning the operation of the system. The assumptions are expressed in o Mathematical relationships o Logical relationships o Symbolic relationships Between the entities of the system. Measures of performance The model solved by mathematical methods such as differential calculus, probability theory, algebraic methods has the solution usually consists of one or more numerical parameters which are called measures of performance. 1.1 When Simulation is the Appropriate Tool • Simulation enables the study of and experimentation with the internal interactions of a complex system, or of a subsystem within a complex system. • Informational, organizational and environmental changes can be simulated and the effect of those alternations on the model’s behavior can be observer. • The knowledge gained in designing a simulation model can be of great value toward suggesting...

...Computer Simulation
* A software program that runs on any computer that attempts to simulate some phenomenon based on a scientist's conceptual and mathematical understanding of the given phenomenon.
* The scientist's conceptual understanding is reduced to an algorithmic or mathematical logic, which is then programmed in one of many programming languages (Fortran, C, C++, etc.) and compiled to produce a binary code that runs on a computer.
* Have become a useful part of modeling many natural systems in physics, chemistry and biology, human systems in economics and social science and in the process of engineering new technology, to gain insight into the operation of those systems
Areas that Uses Simulation
* Designing and analyzing manufacturing systems;
* Evaluating military weapons systems or their logistics requirements;
* Determining hardware requirements or protocols for communication networks;
* Determining hardware and software requirements for a computer system;
* Designing and operating transportation systems such as airports, freeways, ports, and subways;
* Evaluating designs for service organizations such as call centers, fast-food restaurants, hospitals, and post offices;
* Reengineering of business processes;
* Determining ordering policies for an inventory systems; and,
* Analyzing financial or economic systems.
Model
* is a representation of the construction and...

...MGT5088 CASE TWO - RISK ASSESSMENT REPORT
Program Evaluation and Review Technique (PERT) Scheduling with Resource Constraints Using Qualitative Simulation Graphs (QSG)
Prepared by: Susan H. Davenport
July 6th, 2009
This report assesses the risk in the application of the Qualitative Simulation Graph
Methodology (QSGM) model that addresses Program Evaluation and Review
Technique (PERT) scheduling-with-resources problem. PERT scheduling is a network
Analysis technique based on mathematical equations known as Runge-Kutta that
establishes and weights best and worst-case scenarios against the most likely set of
occurrences, but PERT does not factor in the most likely estimates that could leave the
project manager with a programmatic risk.
The QSGM model, used to mitigate the possible programmatic risk, is a general purpose
Qualitative Discrete-Event Simulation (QDES) framework that can be used for any type of
Discrete-Event Simulation (DES) problem. QSGM adds a coverage property that consists
of every possible scenario known to a scheduling problem, including both best and worst-
case scenarios. The QSGM’s coverage property defines any possible sequence of events
with all possible sequence of events with specific timing. Due to the technical risk of the
large number of possible...

...April 2010
‘The problems of Monte Carlo Simulation’ by David Nawrocki
This article describes the problems associated with using the Monte Carlo Simulation Model
as a tool for determining future investment outcomes for investors. The tool is widely used
by Financial Advisors as a means of showing investors future returns on investments. The
article discusses why the use of Monte Carlo Simulation in financial planning is difficult and
can lead to incorrect decisions which can have a detrimental impact on investors’
expectations of expected returns. The article tells us that Monte Carlo Simulation uses
assumptions based on normal distributions and correlation coefficients of zero, neither of
which are real in the financial markets.
The article discusses why Monte Carlo Simulation should only be used when there is no data available or it is too expensive to implement and why other methods may provide the same or better answers without being assumptive.
The author uses evidence from previous authors highlighting the problems with Monte Carlo Simulation and the use of alternatives as a more accurate way of forecasting future returns for an investor. There are four alternatives shown and discussed, however, the article explores the use of exploratory simulation which states can provide more accurate answers, without assumptions and is easier to use.
The article...

... MarketReportsStore.com publishes report on “The Global Military Simulation and Virtual Training Market 2014-2024 – Country Analysis: Market Profile”.
Synopsis
This report offers detailed analysis of the global Military Simulation and Virtual Training market over the next ten years, and provides extensive market size forecasts by country and sub sector. It covers the key technological and market trends in the Military Simulation and Virtual Training market.
Summary
"The Global Military Simulation and Virtual Training Market 2014-2024 - Country Analysis: Market Profile" provides details of the key markets in each region, offering an analysis of the top segments of Military Simulation and Virtual Training, expected to be in demand. It also investigates the top three expected Military Simulation and Virtual Training programs, in terms of demand in the key markets in each region.
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Reasons To Buy
"The Global Military Simulation and Virtual Training Market 2014-2024 - Country Analysis: Market Profile" allows you to:
Have access to a detailed analysis of defense spending patterns including forecasts of Military Simulation and Virtual Training spending till 2024 by region.
Gain insight into various defense modernization initiatives around the world.
Obtain...

...Using Simulation to Educate the Healthcare Professional
The purpose to the article was to give an overview of types, implementations and resources for human simulation in nursing education. “Gaba (2004) has defined simulation as a “ ...technique, not a technology, to replace or amplify real experiences with guided experiences (as sited in Galloway, 2009). Aldrich (2005 ) stated “[t]he objective in creating any simulation experience is achieving fidelity, i.e., a close replication of the real-life, human situation” (as cited in Galloway, 2009). The fidelity created the environment for learning, when fidelity is high there is a greater potential for learning. There are six types of simulations role-playing, standardized patients, partial task trainers, complex task, integrated simulators or human patient stimulators, and full mission simulation (Galloway, 2009).
The author showed how the use of simulation for learning was not limited to nursing students and that regardless of the limited numbers for studies, the results for simulation have been positive in many areas of high-risk training. The evidence base for the use of simulation in patient care is limited (Galloway, 2009). “The sky is the limit in terms of how much it will cost to incorporate simulation into health professional education” (Galloway, 2009). The technology for...

...Prediction of Cross-axis-sensitivity of inertial micro-sensor through modeling and simulation
B. P. Joshi1, A. B. Joshi2, A. S. Chaware2 , S. A. Gangal*2
1
Armament Research & Development Establishment (ARDE), DRDO Ministry of Defence, Dr Homi Bhabha Road, Pashan Pune-411021, India Ph. No.+91-20-2588 4795, Fax No.+91-20-2589 3102 E-mail:bpjoshi@ieee.org 2 Department of Electronic Science, University of Pune, Pune-411 007, India Abstract: In addition to sensitivity and bandwidth, the cross-sensitivity is an important design parameter for acceleration/ inertial sensor design. In this paper prediction of cross-axis sensitivity of cantilever type of piezoresistive accelerometer is discussed. The effect of variation in geometrical parameters such as width and thickness of flexure & proof mass (PM) on crosssensitivity are studied. Optimization of cross-sensitivity by varying geometrical parameters has been attempted. This paper deals with simulations of skewed type (Flexure perpendicular to proof mass) and planar type (Flexure in plane with Proof mass) structure for cross-axis sensitivity analysis. The simulation and modeling has been carried using Coventorware MEMSCAD software. Keywords: Inertial sensor, Cross-sensitivity, MEMSCAD, FEM.
1 Introduction
Micromachined accelerometers are widely used in many applications. Large number of scientists all over the world are working on MEMS based acceleration sensors that are mostly...