Simulation Software 1
|
|
|
- Domenic Harper
- 10 years ago
- Views:
Transcription
1 Simulation Software 1
2 Introduction The features that should be programmed in simulation are: Generating random numbers from the uniform distribution Generating random variates from any distribution Advancing simulation time Determining the next event and passing control to the appropriate block of code Adding records to, or deleting records from, a list Collecting output statistics and reporting them Detecting error conditions These common features are programmed using general-purpose languages like FORTRAN, C or C++ to develop simulation packages. 2
3 How to simulate a system with a computer code? Planning the study Description of the system Definitions of the performance measures Algorithm Flowchart: Process in the system, collection of statistics to estimate the performance measures, reporting Algorithm that corresponds to the flowchart Computer code (Matlab, Java, ) Data structures (sequential vs linked allocation: in book) Random number generation Coding the algorithm
4 Simulating a Multi-Teller Bank The bank opens its doors at 9 A.M. and closes its doors at 5 P.M., but operates until all customers still in the bank are served. Customer interarrival times are IID and exponential with mean 1 minute and services are IID and exponential with mean 4.5 minutes. Each teller has a separate queue. An arriving customer joins the shortest queue, choosing the leftmost shortest queue if there is a tie. Let n i be the number of customers in front of teller i. If the completion of service at teller i causes n j > n i + 1 for some other teller j, then the customer from the tail of queue j jockeys to the tail of queue i. If there are two or more such customers, the one from the closest leftmost queue joins.
5 Multiteller Bank with Jockeying 5
6 Events and state transitions 6
7 Events and state transitions: arrival to a non-empty system 7
8 Events and state transitions: arrival to an empty system 8
9 Events and state transitions: departure from a non-empty system, followed by jockeying 9
10 Performance measures Expected number in each queue Expected waiting time of a customer Expected utilization of each server 10
11 Flowchart Events? a separate flowchart for each event Customer arrival Customer departure Jockeying How to combine them? main flowchart 11
12 Arrival Flowchart Function arrive Schedule the next arrival event Yes Set a delay of 0 for this customer Is a teller idle? No Find the number of the leftmost shortest queue (shortest_q) Make the teller busy Schedule the departure of this customer Place the customer at the end of queue number shortest_q i.e., increase the # in the queue by 1 Record the arrival time of this customer Return
13 Departure Flowchart Yes Function depart Is the queue for this teller empty? No Make this teller idle Remove the first customer from this queue Compute this customer s delay and gather statistics Schedule the departure event for this customer Call function Jockey Return
14 Jockeying Flowchart Remove this customer from the tail of his or her current queue Yes Function jockey Is there a customer to jockey? No Yes Is the teller who just completed service now busy? No Compute this customer s delay and gather statistics Place the jockeying customer at the tail of the queue of the teller who just completed service Make this teller busy Schedule the departure event for the jockeying customer Return
15 Flowchart Combining Departure and Jockeying Yes Function depart Is the queue for this teller empty? No Yes Is there a customer to jockey? No Remove the first customer from this queue Compute this customer s delay and gather statistics Remove this customer from the tail of his or her current queue Compute this customer s delay and gather statistics Make this teller busy Schedule the departure event for the jockeying customer Make this teller idle Yes Place the jockeying customer at the tail of this queue Schedule the departure event for this customer Is there a customer to jockey? No Return
16 Main flowchart Start Initialization Yes Is current_time < finish_time No Find the next event Update current_time: current_time+time_till_next Report Call the function corresponding to the event Stop
17 Common mistakes in the flowcharts Schedule next departure only if you make the server busy with the next customer. Collect the statistics: How many customers were waiting between the previous event and the departure now? The waiting time of the customer who just started service? 17
18 How to think to draw a flowchart: Process Departure function is called, when the minimum of the event times (in this case, time-to-departure and time-toarrival) corresponds to a departure. So we know that a customer is leaving the system, which means that the server serving that customer is now idle and has to look for a customer if there is any in the system. Is there any customer to serve for this server? Check: The server s queue Other queues Once the server s state is determined, check for jockeying 18
19 How to think to draw a flowchart: Process Which event will happen next? Events: Arrival Potentially departure from queue i, i=1,2,,5 End of simulation 19
20 How to think to draw a flowchart: Information gathering What do you need to know about the queues? The number of customers in each queue: State variable: (n 1 (t),n 2 (t),..n 5 (t)), where n i (t) denotes the total number of customers in queue i (including the customer in service). Server state? What do we need to know about customers? For each customer: The queue that he/she is waiting for (denote by cust_queue) His/her position in the cust_queue His/her arrival time 20
21 How to modify the states? Let (n 1 (t),n 2 (t),..n 5 (t)) be our state variable where n i (t) denotes the total number of customers in queue i (including the customer in service). Arrival event: If there is at least one n i (t - ) =0. Let j be the smallest index of the queue that is empty, then n j (t)=n j (t - )+1 Else Findargmin j ((n 1 (t),n 2 (t),..n 5 (t)) Let j be the smallest index of the queue has the smallest number of customers, then n j (t)=n j (t - )+1 21
22 How to gather statistics Define the variables: Cum_delay Cum_queue Cum_busy At each event epoch, update these variables 22
23 How to think to draw a flowchart: Information gathering For example: Cum_queue Inter-arrival times: A 1 = 0.4, A 2 = 1.2, A 3 = 0.5, A 4 = 1.7, A 5 = 0.2, A 6 = 1.6, A 7 = 0.2, A 8 = 1.4, A 9 = 1.9 Processing times: S 1 = 2.0, S 2 = 0.7, S 3 = 0.4, S 4 = 1.1, S 5 = 3.7, S 6 = 0.6 At each event epoch, update these variables t Events Next Event 0 {I 1 } I 1 (0.4) 0 0 N 1 (t) Cum_queue {I 2, C 1 } I 2 (1.6) =0 1.6 {I 3, C 1, C 2 } I 3 (2.1) 1 0+0*( )=0 2.1 {I 3, C 1, C 2, C 3 } C2 (2.3) 1 0+1*( )=0.5 t 0 N ( d 1 ) 23
24 Output Report for Multite ller Bank 24
25 Output Report for Multiteller Bank 25
26 Comparison of Simulation Packages with Programming Languages Advantages of simulation packages They automatically provide most of the features, requiring less programming time and cost. They provide a natural framework for simulation modeling. Models are easier to modify and maintain. They provide better error detection because potential errors are checked for automatically. 26
27 Comparison of Simulation Packages with Programming Languages Advantages of general purpose languages Most modelers already know a language, but this is often not the case with a simulation package. A simulation model efficiently written in a language may require less execution time. Programming languages may allow greater programming flexibility. Software cost is generally lower, but total project cost may not be. 27
28 Classification of Simulation Software Earlier times: A combination of general purpose language and simulation concepts such as Simscript, Siman, or SLAM Recently: Simulation software packages Easy-to-use User friendly graphical model building approach involving use of modules and icons selected by the user on screen Entities represented by icons with a wide range of animation capabilities. 28
29 Classification of Simulation Software General-purpose versus application-oriented simulation packages A general-purpose simulation package can be used for any application, but might have special features for certain ones (like manufacturing, communications, or business process reengineering). An application-oriented simulation package is designed to be used for a certain class of application (like manufacturing, healthcare, or call centers). 29
30 Classification of Simulation Software Modeling approach Event-scheduling approach is based on simulating over time by executing the events selected from the event list in increasing order of their time. Process approach is based on simulating the time-ordered sequence of processes experienced by a single entity as it flows through the system. 30
31 Process Approach 31
32 Prototype customerprocess routine for a singleserver queueing system 32
33 Common Modeling Elements Simulation packages typically include entities, attributes, resources and queues as part of their modeling framework. 33
34 Desirable Software Features 1. General capabilities Modeling flexibility, Ease of use, Hierarchical modeling, Debugging aids, Fast model execution speed, etc. 2. Hardware and software requirement Computer platforms (PC s, UNIX workstations, Apple s), RAM requirement, Operating system requirement (Windows, UNIX, Mac OS) 3. Animation and dynamic graphics Concurrent and post-processed animation, Vector based and pixel based graphics, Two and three dimensional animation, Dynamic graphics and statistics 34
35 Desirable Software Features 4. Statistical capabilities Good random number generation, Theoretical discrete and continuous distributions, Empirical distributions, Independent replications or runs, Performance estimation, Confidence interval determination, Warmup period, Optimization via simulation 5. Customer support and documentation Public and customized training Technical support Good documentation 6. Output reports and graphics Standart and customized reports Descriptive statistics (histograms, time plots, bar chart, pie chart, etc.) 35
36 General-Purpose Simulation Packages Arena This is the package we will be using in this course. Modeling is done using modules arranged into a number of templates: Basic Process template has modules used in many models for modeling arrivals (create), services (process) and departures (dispose). Advanced Process template contains modules to perform very specific logical functions such as choosing a queue when several are available or coordinating the advancement of multiple entities in different areas Advanced Transfer template contains modules (like conveyors and transporters) that are used to describe the transfer of entities from one part of the system to another. 36
37 Arena A model is constructed by dragging and dropping modules into the model window, connecting them to indicate the flow of entities through the simulated system, and then detailing the modules using dialog boxes of Arena s built-in spreadsheet. 37
38 Arena 38
39 Arena 39
40 Arena 40
41 Arena 41
42 Arena 42
43 Other General-Purpose Simulation Packages Extend AweSim GPSS/H Micro Saint MODSIM III SES/workbench SIMPLE++ SIMUL8 SLX Taylor Enterprise Dynamics 43
44 Application-Oriented Simulation Packages Manufacturing: AutoMod, AutoSched AP, Extend + Manufacturing, Arena Packaging Edition, ProModel, QUEST, Taylor Enterprise Dynamics, WITNESS Communication Networks: COMNET, IT DecisionGuru, OPNET Modeler Process Reengineering and Services: Arena Business Edition, Extend + BPR, ProcessModel, ServiceModel, SIMPROCESS Heath Care: MedModel Call Centers: Arena Call Center Edition Animation: Proof Animation 44
Chapter 3 Simulation Software. Simulation Modeling and Analysis Chapter 3 Simulation Software Slide 1 of 13
Chapter 3 Simulation Software Simulation Modeling and Analysis Chapter 3 Simulation Software Slide 1 of 13 3.1 Introduction CONTENTS 3.2 Comparison of Simulation Packages with Programming Languages 3.3
Arena Tutorial 1. Installation STUDENT 2. Overall Features of Arena
Arena Tutorial This Arena tutorial aims to provide a minimum but sufficient guide for a beginner to get started with Arena. For more details, the reader is referred to the Arena user s guide, which can
1: B asic S imu lati on Modeling
Network Simulation Chapter 1: Basic Simulation Modeling Prof. Dr. Jürgen Jasperneite 1 Contents The Nature of Simulation Systems, Models and Simulation Discrete Event Simulation Simulation of a Single-Server
INTRODUCTION TO MODELING AND SIMULATION. Anu Maria
INTRODUCTION TO MODELING AND SIMULATION Anu Maria State University of New York at Binghamton Department of Systems Science and Industrial Engineering Binghamton, NY 13902-6000, U.S.A. ABSTRACT This introductory
Discrete-Event Simulation
Discrete-Event Simulation Prateek Sharma Abstract: Simulation can be regarded as the emulation of the behavior of a real-world system over an interval of time. The process of simulation relies upon the
Chapter 8 Detailed Modeling
Chapter 8 Detailed Modeling What We ll Do... Exploit hierarchical structure of Arena to blend in lower-level modeling for greater detail Example: call-center system Nonstationary arrival processes Build
HSR HOCHSCHULE FÜR TECHNIK RA PPERSW I L
1 An Introduction into Modelling and Simulation 4. A Series of Labs to Learn Simio af&e Prof. Dr.-Ing. Andreas Rinkel [email protected] Tel.: +41 (0) 55 2224928 Mobil: +41 (0) 79 3320562 Lab 1 Lab
Jump-Start Tutorial For ProcessModel
Jump-Start Tutorial For ProcessModel www.blueorange.org.uk ProcessModel Jump-Start Tutorial This tutorial provides step-by-step instructions for creating a process model, running the simulation, and viewing
Simulation Software: Practical guidelines for approaching the selection process
Practical guidelines for approaching the selection process Randall R. Gibson, Principal / Vice President Craig Dickson, Senior Analyst TranSystems I Automation Associates, Inc. Challenge Selecting from
System Simulation - Modeling and Analysis
System Simulation - Modeling and Analysis Jason R. W. Merrick Jill Hardin Department of Statistical Sciences & Operations Research Virginia Commonwealth University Table of Contents 1 Simulation Modeling
KNOWLEDGE-BASED MODELING OF DISCRETE-EVENT SIMULATION SYSTEMS. Henk de Swaan Arons
KNOWLEDGE-BASED MODELING OF DISCRETE-EVENT SIMULATION SYSTEMS Henk de Swaan Arons Erasmus University Rotterdam Faculty of Economics, ment of Computer Science P.O. Box 1738, H9-28 3000 DR Rotterdam, The
Arena 9.0 Basic Modules based on Arena Online Help
Arena 9.0 Basic Modules based on Arena Online Help Create This module is intended as the starting point for entities in a simulation model. Entities are created using a schedule or based on a time between
AS-D1 SIMULATION: A KEY TO CALL CENTER MANAGEMENT. Rupesh Chokshi Project Manager
AS-D1 SIMULATION: A KEY TO CALL CENTER MANAGEMENT Rupesh Chokshi Project Manager AT&T Laboratories Room 3J-325 101 Crawfords Corner Road Holmdel, NJ 07733, U.S.A. Phone: 732-332-5118 Fax: 732-949-9112
Systems Modelling and Simulation (Lab session 3)
Systems Modelling and Simulation (Lab session 3) After this session you should understand. How to model resource failures. 2. How to schedule resources. 3. How to add animations Resource pictures Entity
THE EXTEND SIMULATION ENVIRONMENT. David Krahl. Imagine That, Inc. 6830 Via Del Oro, Suite 230 San Jose, CA 95119, U.S.A.
Proceedings of the 2001 Winter Simulation Conference B. A. Peters, J. S. Smith, D. J. Medeiros, and M. W. Rohrer, eds. THE EXTEND SIMULATION ENVIRONMENT David Krahl Imagine That, Inc. 6830 Via Del Oro,
CHAPTER 3 CALL CENTER QUEUING MODEL WITH LOGNORMAL SERVICE TIME DISTRIBUTION
31 CHAPTER 3 CALL CENTER QUEUING MODEL WITH LOGNORMAL SERVICE TIME DISTRIBUTION 3.1 INTRODUCTION In this chapter, construction of queuing model with non-exponential service time distribution, performance
Introduction. Continue to Step 1: Creating a Process Simulator Model. Goal: 40 units/week of new component. Process Simulator Tutorial
Introduction This tutorial places you in the position of a process manager for a specialty electronics manufacturing firm that makes small lots of prototype boards for medical device manufacturers. Your
Load Balancing and Switch Scheduling
EE384Y Project Final Report Load Balancing and Switch Scheduling Xiangheng Liu Department of Electrical Engineering Stanford University, Stanford CA 94305 Email: [email protected] Abstract Load
Network Design Performance Evaluation, and Simulation #6
Network Design Performance Evaluation, and Simulation #6 1 Network Design Problem Goal Given QoS metric, e.g., Average delay Loss probability Characterization of the traffic, e.g., Average interarrival
SYSTEMS ANALYSIS AND MODELING OF INTEGRATED WORLD SYSTEMS - Vol. I - Simulation Software - Yu. I. Brodsky
SYSTEMS ANALYSIS AND MODELING OF INTEGRATED WORLD SYSTEMS - Vol. I - Software - Yu. I. Brodsky SIMULATION SOFTWARE Yu. I. Brodsky Computer Center of Russian Academy of Sciences, Moscow, Russia Keywords:,
Waiting Times Chapter 7
Waiting Times Chapter 7 1 Learning Objectives Interarrival and Service Times and their variability Obtaining the average time spent in the queue Pooling of server capacities Priority rules Where are the
HSR HOCHSCHULE FÜR TECHNIK RA PPERSW I L
1 An Introduction into Modelling and Simulation Prof. Dr.-Ing. Andreas Rinkel af&e [email protected] Tel.: +41 (0) 55 2224928 Mobil: +41 (0) 79 3320562 Goal After the whole lecture you: will have an
Chapter 2. Simulation Examples 2.1. Prof. Dr. Mesut Güneş Ch. 2 Simulation Examples
Chapter 2 Simulation Examples 2.1 Contents Simulation using Tables Simulation of Queueing Systems Examples A Grocery Call Center Inventory System Appendix: Random Digits 1.2 Simulation using Tables 1.3
I. M. Ibrahim Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Seri Iskandar, Perak MALAYSIA [email protected].
A Simulation Approach: Improving Patient Waiting for Multiphase Patient Flow of Obstetrics and Gynecology Department (O&G Department) in Local Specialist Centre A. F. NAJMUDDIN Faculty of Computer and
Modeling Stochastic Inventory Policy with Simulation
Modeling Stochastic Inventory Policy with Simulation 1 Modeling Stochastic Inventory Policy with Simulation János BENKŐ Department of Material Handling and Logistics, Institute of Engineering Management
A CRITICAL EVALUATION AND COMPARISON OF FOUR MANUFACTURING SIMULATION SOFTWARES
A CRITICAL EVALUATION AND COMPARISON OF FOUR MANUFACTURING SIMULATION SOFTWARES 1 Rajesh Verma*, 2 Ashu Gupta, 3 Kawaljeet Singh 1 Lovely School of Business, Lovely Professional University Phagwara, Punjab
CE 504 Computational Hydrology Computational Environments and Tools Fritz R. Fiedler
CE 504 Computational Hydrology Computational Environments and Tools Fritz R. Fiedler 1) Operating systems a) Windows b) Unix and Linux c) Macintosh 2) Data manipulation tools a) Text Editors b) Spreadsheets
Stage 5 Information and Software Technology
Stage 5 Information and Software Technology Year: Year 9 Teacher: Topic: Option 8: Software Development and Programming Time: This option involves students undertaking a range of activities that will lead
Modeling Basic Operations and Inputs
Modeling Basic Operations and Inputs Chapter 4 Last revision June 7, 2003 Simulation with Arena, 3 rd ed. Chapter 4 Modeling Basic Operations and Inputs Slide 1 of 66 What We ll Do... Model 4-1: Electronic
The problem with waiting time
The problem with waiting time Why the only way to real optimization of any process requires discrete event simulation Bill Nordgren, MS CIM, FlexSim Software Products Over the years there have been many
Justifying Simulation. Why use simulation? Accurate Depiction of Reality. Insightful system evaluations
Why use simulation? Accurate Depiction of Reality Anyone can perform a simple analysis manually. However, as the complexity of the analysis increases, so does the need to employ computer-based tools. While
Chapter 3 General Principles in Simulation
Chapter 3 General Principles in Simulation Banks, Carson, Nelson & Nicol Discrete-Event System Simulation Concepts In Discrete-Event Simulation System A collection of entities (people and machines..) that
I/O Management. General Computer Architecture. Goals for I/O. Levels of I/O. Naming. I/O Management. COMP755 Advanced Operating Systems 1
General Computer Architecture I/O Management COMP755 Advanced Operating Systems Goals for I/O Users should access all devices in a uniform manner. Devices should be named in a uniform manner. The OS, without
Malay A. Dalal Madhav Erraguntla Perakath Benjamin. Knowledge Based Systems, Inc. (KBSI) College Station, TX 77840, U.S.A.
AN INTRODUCTION TO USING PROSIM FOR BUSINESS PROCESS SIMULATION AND ANALYSIS Malay A. Dalal Madhav Erraguntla Perakath Benjamin Knowledge Based Systems, Inc. (KBSI) College Station, TX 77840, U.S.A. ABSTRACT
Simulation Tools Evaluation using Theoretical Manufacturing Model
Acta Polytechnica Hungarica Vol. 10, No. 2, 2013 Simulation Tools Evaluation using Theoretical Manufacturing Model Pavol Semanco, David Marton Faculty of Manufacturing Technologies with seat in Presov
Simulation of DNS(Domain Name System) Using SimLib
Simulation of DNS(Domain Name System) Using SimLib Submitted by Prem Tamang Submitted to Dr. Lawrence J. Osborne Table of Contents 1. Introduction 3 2. Motivation and Challenges. 5 3. Assumptions 5 4.
There are a number of factors that increase the risk of performance problems in complex computer and software systems, such as e-commerce systems.
ASSURING PERFORMANCE IN E-COMMERCE SYSTEMS Dr. John Murphy Abstract Performance Assurance is a methodology that, when applied during the design and development cycle, will greatly increase the chances
AUTOMATIC CREATION OF SIMULATION MODELS FOR FLOW ASSEMBLY LINES
AUTOMATIC CREATION OF SIMULATION MODELS FOR FLOW ASSEMBLY LINES João Weinholtz, Rui Loureiro, Carlos Cardeira, João M. Sousa Instituto Superior Técnico, Technical University of Lisbon Dept. of Mechanical
Axiomatic design of software systems
Axiomatic design of software systems N.P. Suh (1), S.H. Do Abstract Software is playing an increasingly important role in manufacturing. Many manufacturing firms have problems with software development.
STATISTICAL DATA ANALYSIS COURSE VIA THE MATLAB WEB SERVER
STATISTICAL DATA ANALYSIS COURSE VIA THE MATLAB WEB SERVER Ale š LINKA Dept. of Textile Materials, TU Liberec Hálkova 6, 461 17 Liberec, Czech Republic e-mail: [email protected] Petr VOLF Dept. of Applied
TEACHING SIMULATION WITH SPREADSHEETS
TEACHING SIMULATION WITH SPREADSHEETS Jelena Pecherska and Yuri Merkuryev Deptartment of Modelling and Simulation Riga Technical University 1, Kalku Street, LV-1658 Riga, Latvia E-mail: [email protected],
Cisco Unified CallConnector Operator
Cisco Unified CallConnector Operator Cisco Unified CallConnector Operator enhances business efficiency by quickly answering the mainnumber calls and accurately delivering them to appropriate and available
Quantitative Analysis of Cloud-based Streaming Services
of Cloud-based Streaming Services Fang Yu 1, Yat-Wah Wan 2 and Rua-Huan Tsaih 1 1. Department of Management Information Systems National Chengchi University, Taipei, Taiwan 2. Graduate Institute of Logistics
Computer Science, Informatik 4 Communication and Distributed Systems. Simulation. Discrete-Event System Simulation. Dr.
Simulation Discrete-Event System Simulation Chapter 3 General Principles General Principles Introduction Framework for modeling systems by discrete-event simulation A system is modeled in terms of its
A Comparison of Dynamic Load Balancing Algorithms
A Comparison of Dynamic Load Balancing Algorithms Toufik Taibi 1, Abdelouahab Abid 2 and Engku Fariez Engku Azahan 2 1 College of Information Technology, United Arab Emirates University, P.O. Box 17555,
Delivery Fleet Optimization with GIS Functionality Webinar
Delivery Fleet Optimization with GIS Functionality Webinar May 28, 2015 Tom Baggio 1. Problem Definition The webinar objectives is a step-by-step process of building a simulation model of an aircraft part
Supplement to Call Centers with Delay Information: Models and Insights
Supplement to Call Centers with Delay Information: Models and Insights Oualid Jouini 1 Zeynep Akşin 2 Yves Dallery 1 1 Laboratoire Genie Industriel, Ecole Centrale Paris, Grande Voie des Vignes, 92290
2WB05 Simulation Lecture 8: Generating random variables
2WB05 Simulation Lecture 8: Generating random variables Marko Boon http://www.win.tue.nl/courses/2wb05 January 7, 2013 Outline 2/36 1. How do we generate random variables? 2. Fitting distributions Generating
Stochastic Processes and Queueing Theory used in Cloud Computer Performance Simulations
56 Stochastic Processes and Queueing Theory used in Cloud Computer Performance Simulations Stochastic Processes and Queueing Theory used in Cloud Computer Performance Simulations Florin-Cătălin ENACHE
Modeling Detailed Operations
Modeling Detailed Operations Chapter 5 Last revision August 20, 2009 Simulation with Arena, 5th ed. Chapter 5 Modeling Detailed Operations Slide 1 of 51 What We ll Do... Model 5-1: Simple call center Lower-level
- 1 - intelligence. showing the layout, and products moving around on the screen during simulation
- 1 - LIST OF SYMBOLS, TERMS AND EXPRESSIONS This list of symbols, terms and expressions gives an explanation or definition of how they are used in this thesis. Most of them are defined in the references
IST 301. Class Exercise: Simulating Business Processes
IST 301 Class Exercise: Simulating Business Processes Learning Objectives: To use simulation to analyze and design business processes. To implement scenario and sensitivity analysis As-Is Process The As-Is
Computer Literacy. Hardware & Software Classification
Computer Literacy Hardware & Software Classification Hardware Classification Hardware is just another word for computer equipment; it is the physical parts of the computer that we can see and touch. All
LECTURE - 1 INTRODUCTION TO QUEUING SYSTEM
LECTURE - 1 INTRODUCTION TO QUEUING SYSTEM Learning objective To introduce features of queuing system 9.1 Queue or Waiting lines Customers waiting to get service from server are represented by queue and
Basic Queuing Relationships
Queueing Theory Basic Queuing Relationships Resident items Waiting items Residence time Single server Utilisation System Utilisation Little s formulae are the most important equation in queuing theory
MEDMODEL - HEALTHCARE SIMULATION SOFTWARE. Steve H. Denney
MEDMODEL - HEALTHCARE SIMULATION SOFTWARE Steve H. Denney PROMODEL Corporation Healthcare Systems Division 13333 Blanco Road, Suite 302 San Antonio, Texas 78216, U.S.A. ABSTRACT In response to the expressed
File System & Device Drive. Overview of Mass Storage Structure. Moving head Disk Mechanism. HDD Pictures 11/13/2014. CS341: Operating System
CS341: Operating System Lect 36: 1 st Nov 2014 Dr. A. Sahu Dept of Comp. Sc. & Engg. Indian Institute of Technology Guwahati File System & Device Drive Mass Storage Disk Structure Disk Arm Scheduling RAID
Total Quality Management (TQM) Quality, Success and Failure. Total Quality Management (TQM) vs. Process Reengineering (BPR)
Total Quality Management (TQM) Quality, Success and Failure Total Quality Management (TQM) is a concept that makes quality control a responsibility to be shared by all people in an organization. M7011
NEW MODELS FOR PRODUCTION SIMULATION AND VALIDATION USING ARENA SOFTWARE
NEW MODELS FOR PRODUCTION SIMULATION AND VALIDATION USING ARENA SOFTWARE Marinela INŢĂ 1 and Achim MUNTEAN 1 ABSTRACT: Currently, Lean Manufacturing is a recognized topic in many research fields, which
SIMULATION SOFTWARE AND NUMERICAL ISSUES. Alexander L. Pugh Massachusetts Institute of Technology, Cambridge, USA
SIMULATION SOFTWARE AND NUMERICAL ISSUES Alexander L. Pugh Massachusetts Institute of Technology, Cambridge, USA Keywords: Adams integration method, adaptive integration methods, Euler integration method,
TAYLOR II MANUFACTURING SIMULATION SOFTWARE
Prnceedings of the 1996 WinteT Simulation ConfeTence ed. J. M. ClIarnes, D. J. Morrice, D. T. Brunner, and J. J. 8lvain TAYLOR II MANUFACTURING SIMULATION SOFTWARE Cliff B. King F&H Simulations, Inc. P.O.
QoS-Aware Storage Virtualization for Cloud File Systems. Christoph Kleineweber (Speaker) Alexander Reinefeld Thorsten Schütt. Zuse Institute Berlin
QoS-Aware Storage Virtualization for Cloud File Systems Christoph Kleineweber (Speaker) Alexander Reinefeld Thorsten Schütt Zuse Institute Berlin 1 Outline Introduction Performance Models Reservation Scheduling
IC 1101 Basic Electronic Practice for Electronics and Information Engineering
7. INDUSTRIAL CENTRE TRAINING In the summer between Year 1 and Year 2, students will undergo Industrial Centre Training I in the Industrial Centre (IC). In the summer between Year 2 and Year 3, they will
Remote Desktop Services
Remote Desktop Services White Paper November 2014 Maximizing the Value and Performance of QuickBooks Enterprise with Remote Desktop Services Formerly known as Windows Terminal Services, Remote Desktop
Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012
Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts
Process simulation. Enn Õunapuu [email protected]
Process simulation Enn Õunapuu [email protected] Content Problem How? Example Simulation Definition Modeling and simulation functionality allows for preexecution what-if modeling and simulation. Postexecution
UPSentry Smart 2000. User s Manual. Shutdown Management Software. for Mac OS X 10.2
UPSentry Smart 2000 Shutdown Management Software for Mac OS X 10.2 User s Manual Table of Contents Introduction...3 Installing/Removing UPSentry Smart 2000...4 User Interface: UPSentry...5 System Menu...6
Report Paper: MatLab/Database Connectivity
Report Paper: MatLab/Database Connectivity Samuel Moyle March 2003 Experiment Introduction This experiment was run following a visit to the University of Queensland, where a simulation engine has been
Chapter 11 I/O Management and Disk Scheduling
Operating Systems: Internals and Design Principles, 6/E William Stallings Chapter 11 I/O Management and Disk Scheduling Dave Bremer Otago Polytechnic, NZ 2008, Prentice Hall I/O Devices Roadmap Organization
PCHS ALGEBRA PLACEMENT TEST
MATHEMATICS Students must pass all math courses with a C or better to advance to the next math level. Only classes passed with a C or better will count towards meeting college entrance requirements. If
User s Guide. Publication ARENA-UM001B-EN-P - October 2005 Supercedes Publication ARENA-UM001A-EN-P
User s Guide Publication ARENA-UM001B-EN-P - October 2005 Supercedes Publication ARENA-UM001A-EN-P Contacting Rockwell Software Copyright Notice Trademark Notice Warranty Technical Support Telephone 1.440.646.5800
Citrix EdgeSight for Load Testing User s Guide. Citrx EdgeSight for Load Testing 2.7
Citrix EdgeSight for Load Testing User s Guide Citrx EdgeSight for Load Testing 2.7 Copyright Use of the product documented in this guide is subject to your prior acceptance of the End User License Agreement.
USING OPNET TO SIMULATE THE COMPUTER SYSTEM THAT GIVES SUPPORT TO AN ON-LINE UNIVERSITY INTRANET
USING OPNET TO SIMULATE THE COMPUTER SYSTEM THAT GIVES SUPPORT TO AN ON-LINE UNIVERSITY INTRANET Norbert Martínez 1, Angel A. Juan 2, Joan M. Marquès 3, Javier Faulin 4 {1, 3, 5} [ [email protected], [email protected]
Appendix 2.1 Tabular and Graphical Methods Using Excel
Appendix 2.1 Tabular and Graphical Methods Using Excel 1 Appendix 2.1 Tabular and Graphical Methods Using Excel The instructions in this section begin by describing the entry of data into an Excel spreadsheet.
Critical Path Analysis & PERT Charts (taken from www.business.com.
Critical Path Analysis & PERT Charts (taken from www.business.com. - Planning and scheduling more complex projects Critical Path Analysis and PERT are powerful tools that help you to schedule and manage
DISCRETE EVENT SIMULATION COURSE INSTRUCTOR DR. J. BOOKBINDER PROJECT REPORT. Simulation Study of an Inbound Call Center SACHIN JAYASWAL
DISCRETE EVENT SIMULATION MSCI 632 SPRING 5 COURSE INSTRUCTOR DR. J. BOOKBINDER PROJECT REPORT Simulation Study of an Inbound Call Center Submitted by SACHIN JAYASWAL & GAURAV CHHABRA Table of Contents
Proposed Pricing Model for Cloud Computing
Computer Science and Information Technology 2(4): 211-218, 2014 DOI: 10.13189/csit.2014.020405 http://www.hrpub.org Proposed Pricing Model for Cloud Computing Muhammad Adeel Javaid Member Vendor Advisory
Veri cation and Validation of Simulation Models
of of Simulation Models mpressive slide presentations Faculty of Math and CS - UBB 1st Semester 2010-2011 Other mportant Validate nput- Hypothesis Type Error Con dence nterval Using Historical nput of
Kernel. What is an Operating System? Systems Software and Application Software. The core of an OS is called kernel, which. Module 9: Operating Systems
Module 9: Operating Systems Objective What is an operating system (OS)? OS kernel, and basic functions OS Examples: MS-DOS, MS Windows, Mac OS Unix/Linux Features of modern OS Graphical operating system
Smart Queue Scheduling for QoS Spring 2001 Final Report
ENSC 833-3: NETWORK PROTOCOLS AND PERFORMANCE CMPT 885-3: SPECIAL TOPICS: HIGH-PERFORMANCE NETWORKS Smart Queue Scheduling for QoS Spring 2001 Final Report By Haijing Fang([email protected]) & Liu Tang([email protected])
File Manager base component
Providing flexible, easy-to-use application development tools designed to enhance file processing IBM File Manager for z/os, V13.1 Figure 1: File Manager environment Highlights Supports development and
How To Balance In A Distributed System
6 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 11, NO. 1, JANUARY 2000 How Useful Is Old Information? Michael Mitzenmacher AbstractÐWe consider the problem of load balancing in dynamic distributed
Discrete-Event Simulation
Discrete-Event Simulation 14.11.2001 Introduction to Simulation WS01/02 - L 04 1/40 Graham Horton Contents Models and some modelling terminology How a discrete-event simulation works The classic example
Objectives. Chapter 2: Operating-System Structures. Operating System Services (Cont.) Operating System Services. Operating System Services (Cont.
Objectives To describe the services an operating system provides to users, processes, and other systems To discuss the various ways of structuring an operating system Chapter 2: Operating-System Structures
Minimize Wait Time and Improve the Waiting Experience
Improving the Customer Experience Minimize Wait Time and Improve the Waiting Experience www.lavi.com (888) 285-8605 Overview Waiting lines easily become the source of tension between customers and businesses
2 Simulation Examples
2 Simulation Examples This chapter presents several examples of simulations that can be performed by devising a simulation table either manually or with a spreadsheet. The simulation table provides a systematic
