Project 1.

Slides:



Advertisements
Similar presentations
Discrete Event (time) Simulation Kenneth.
Advertisements

Homework 5. Homework 2: Post Office Simulator Implementing a discrete event simulator to evaluate the performance of a post office Basic Requirement (75%):
IE 429, Parisay, January 2003 Review of Probability and Statistics: Experiment outcome: constant, random variable Random variable: discrete, continuous.
 1  Outline  performance measures for a single-server station  discrete-event simulation  hand simulation  process-oriented simulation approach.
Simulation A Queuing Simulation. Example The arrival pattern to a bank is not Poisson There are three clerks with different service rates A customer must.
Previously Optimization Probability Review Inventory Models Markov Decision Processes.
Chap. 20, page 1051 Queuing Theory Arrival process Service process Queue Discipline Method to join queue IE 417, Chap 20, Jan 99.
Model Antrian By : Render, ect. Outline  Characteristics of a Waiting-Line System.  Arrival characteristics.  Waiting-Line characteristics.  Service.
Waiting Lines and Queuing Theory Models
Previously Optimization Probability Review Inventory Models Markov Decision Processes Queues.
Simulation with ArenaChapter 2 – Fundamental Simulation Concepts Discrete Event “Hand” Simulation of a GI/GI/1 Queue.
1 Queuing Theory 2 Queuing theory is the study of waiting in lines or queues. Server Pool of potential customers Rear of queue Front of queue Line (or.
Data Communication and Networks Lecture 13 Performance December 9, 2004 Joseph Conron Computer Science Department New York University
Queuing. Elements of Waiting Lines  Population –Source of customers Infinite or finite.
7/3/2015© 2007 Raymond P. Jefferis III1 Queuing Systems.
The Poisson process and exponentially distributed service time In real life customers don’t arrive at pre-ordained times as specified in the last tutorial…
Queuing Theory. Queuing theory is the study of waiting in lines or queues. Server Pool of potential customers Rear of queue Front of queue Line (or queue)
Internet Queuing Delay Introduction How many packets in the queue? How long a packet takes to go through?
Limiting probabilities. The limiting probabilities P j exist if (a) all states of the Markov chain communicate (i.e., starting in state i, there is.
 1  Outline  simulating GI/G/1 queues  M/M/1 queues  theoretical results of queueing systems  an inventory system  simulation program with an event.
Graduate Program in Engineering and Technology Management
___________________________________________________________________________ Operations Research  Jan Fábry Waiting Line Models.
(C) 2009 J. M. Garrido1 Object Oriented Simulation with Java.
Waiting Line Models ___________________________________________________________________________ Quantitative Methods of Management  Jan Fábry.
CS433 Modeling and Simulation Lecture 13 Queueing Theory Dr. Anis Koubâa 03 May 2009 Al-Imam Mohammad Ibn Saud University.
Queueing Analysis of Production Systems (Factory Physics)
1 Queuing Analysis Overview What is queuing analysis? - to study how people behave in waiting in line so that we could provide a solution with minimizing.
Queueing Theory What is a queue? Examples of queues: Grocery store checkout Fast food (McDonalds – vs- Wendy’s) Hospital Emergency rooms Machines waiting.
1 Systems Analysis Methods Dr. Jerrell T. Stracener, SAE Fellow SMU EMIS 5300/7300 NTU SY-521-N NTU SY-521-N SMU EMIS 5300/7300 Queuing Modeling and Analysis.
An Optimal Service Ordering for a World Wide Web Server A Presentation for the Fifth INFORMS Telecommunications Conference March 6, 2000 Amy Csizmar Dalal.
Simulation with ArenaChapter 2 – Fundamental Simulation ConceptsSlide 1 of 46 Simulation by Hand: Setup.
1 Part 2 & 3 Performance evaluation. 2 Goals Understand the complex behavior of systems subject to "random phenomena" Develop intuitive understanding.
Discrete Event (time) Simulation. What is a simulation? “Simulation is the process of designing a model of a real system and conducting experiments with.
1 Ardavan Asef-Vaziri Sep-09Operations Management: Waiting Lines3  Mean inter-arrival time = 1/Ri = 1/R  Probability that the time between two arrivals.
OPERATING SYSTEMS CS 3530 Summer 2014 Systems and Models Chapter 03.
Structure of a Waiting Line System Queuing theory is the study of waiting lines Four characteristics of a queuing system: –The manner in which customers.
1 Ardavan Asef-Vaziri Sep-09Operations Management: Waiting Lines3 Example: The arrival rate to a GAP store is 6 customers per hour and has Poisson distribution.
Chapter 6 Queueing Models
Dr. Anis Koubâa CS433 Modeling and Simulation
EVOLUTION OF INDUSTRIAL ENGINEERING Prof. Dr. Orhan TORKUL Res. Asst. M. Raşit CESUR Res. Asst. Furkan YENER.
Queueing Theory. The study of queues – why they form, how they can be evaluated, and how they can be optimized. Building blocks – arrival process and.
QUEUING THEORY 1.  - means the number of arrivals per second   - service rate of a device  T - mean service time for each arrival   = ( ) Utilization,
Objectives Model a Subway sandwich shop in Simio Perform basic verification analysis o Define experiment “Responses” o Learn how to interpret SMORE plots.
OPERATING SYSTEMS CS 3502 Fall 2017
WAITING LINES AND SIMULATION
18 Management of Waiting Lines
Simulation Statistics
Al-Imam Mohammad Ibn Saud University
Application of Queueing
ETM 607 – Spreadsheet Simulations
Queueing Theory What is a queue? Examples of queues:
Internet Queuing Delay Introduction
Demo on Queuing Concepts
Internet Queuing Delay Introduction
Introduction Notation Little’s Law aka Little’s Result
Solutions Hwk Que3 1 The port of Miami has 3 docking berths for loading and unloading ships but is considering adding a 4th berth.
Queuing Theory By: Brian Murphy.
Mitchell Jareo MAT4340 – Operations Research Dr. Bauldry
Topic IV. Single Channel Systems (Limited queue length system)
Discrete Event “Hand” Simulation of a GI/GI/1 Queue
Performance evaluation
Queuing Theory III.
Queueing Theory 2008.
Queuing Theory III.
REMOTE++: A Tool for Automatic Remote
Waiting Line Models Waiting takes place in virtually every productive process or service. Since the time spent by people and things waiting in line is.
Kendall’s Notation ❚ Simple way of summarizing the characteristics of a queue. Arrival characteristics / Departure characteristics / Number of servers.
CS723 - Probability and Stochastic Processes
SIMULATION EXAMPLES QUEUEING SYSTEMS.
Presentation transcript:

Project 1

Homework : Post Office Simulator Implementing a discrete event simulator to evaluate the performance of a post office Basic Requirement (85%): Two Servers and a Single Queue Bonus 1 (10%): Two Servers and Two Queues Bonus 2 (15%) Basic Requirement (General distribution) + Restroom Events Note: no other bonus!!

Basic Requirement: Two Servers and Single Queue Two servers have the same service rate FIFO queue Infinite queue capacity Inter-arrival and service time: i.i.d. and exponential distribution

Basic Requirement: Two Servers and Single Queue input.txt Inter-arrival time (time unit): float(mean) Service time (time unit): float(mean) Simulation time (time unit): int (Max: 231-1)

Basic Requirement: Two Servers and Single Queue output.txt (free format) Average waiting time: Tstart_service - Tarrival Average system time: Tend_service - Tarrival System utilization ratio: the probability that at least one staff is busy Full utilization ratio: the probability that both two staffs are busy

Bonus 1 (10%): Two Servers and Two Queues Each server has a single queue The policy for queue selection: choosing the queue having fewer customers After entering a queue, a customer can not change his queue Other setups = “Basic Requirement”

Bonus 2 (15%): G/G/2 + restroom events Single queue Inter-arrival and service time: normal distribution (<0) A staff would go to the restroom Single restroom (service time: exponential distribution) Single queue for the restroom: a staff needs to line up while the washroom is busy

Bonus 2 (15%): M/M/2 + restroom events A staff can not go to the restroom while serving a customer Alice needs to go to the bathroom @ 08:28:36 Staff Alice begins serving customer Bob @ 08:23:10 Staff Alice finishes serving customer Bob @ 08:33:42 time Alice goes to the restroom

Bonus 2 (15%): M/M/2 + restroom events Inter-rest-time: Tneed_to_restroom(Staff) – Tfinish_restroom(Staff) Exponential Dis. The restroom finishes serving staff Alice @ Tfinish_restroom(Alice) Alice needs to go to the bathroom @ Tneed_to_restroom(Alice) The restroom finishes serving staff Jane @ Tfinish_restroom(Jane) time Inter-rest-time for Alice Alice goes to the restroom and lines up @ Tgo_to_restroom(Alice) The restroom begins serving staff Alice @ Tstart_restroom(Alice)

Bonus 2 (15%): M/M/2 + restroom events input.txt Inter-arrival time (time unit): float(mean) float(variance) Service time (time unit): float(mean) float(variance) Simulation time (time unit): int (231-1) Inter-rest-time (time unit): float Service rate of the restroom (# of staffs/time unit): float Normal Dis. Exp. Dis.

Bonus 2 (15%): M/M/2 + restroom events output.txt (free format) Average waiting time: Tstart_service - Tarrival Average system time: Tend_service - Tarrival System utilization ratio: the probability that at least one staff is busy Full utilization ratio: the probability that both two staffs are busy Average waiting time for restroom events: Tstart_restroom - Tneed_to_restroom

Notes Deadline: 2016/4/27 14:20 Email to: r03922038@ntu.edu.tw Subject: [Perf.] Project 1 submit Programming language: C/C++/Java Student ID_v1.rar (EX: r03922038_v1.rar) Including: readme.txt and source codes readme.txt How to execute (compile) the code? OS platform: linux or windows Don’t implement simulation with “time-slices approach” 0 pt !!!!