MGT 560 Queuing System Simulation Stochastic Modeling © Victor E. Sower, Ph.D., C.Q.E. 2007.

Slides:

Advertisements

Similar presentations

Introduction into Simulation Basic Simulation Modeling.

Advertisements

Simulation of multiple server queuing systems

Module F: Simulation. Introduction What: Simulation Where: To duplicate the features, appearance, and characteristics of a real system Why: To estimate.

FIN 685: Risk Management Topic 5: Simulation Larry Schrenk, Instructor.

Nur Aini Masruroh Queuing Theory. Outlines IntroductionBirth-death processSingle server modelMulti server model.

Model Antrian By : Render, ect. Outline  Characteristics of a Waiting-Line System.  Arrival characteristics.  Waiting-Line characteristics.  Service.

© The McGraw-Hill Companies, Inc., Technical Note 6 Waiting Line Management.

Queuing Systems Chapter 17.

1 Analysis Of Queues For this session, the learning objectives are:  Learn the fundamental structure of a queueing system.  Learn what needs to be specified.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 15-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 15.

Queues / Lines You manage a call center which can answer an average of 20 calls an hour. Your call center gets 17.5 calls in an average hour. On average.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 14-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 14.

Waiting Line Management

Management of Waiting Lines

1 Service A Queuing System Arrival Rate (  Average Number in Queue ( L q ) Avg Time in System ( W ) Avg Number in System ( L ) Average Wait in Queue.

CHAPTER 18 Waiting Lines.

Queuing. Elements of Waiting Lines  Population –Source of customers Infinite or finite.

Queueing Theory Professor Stephen Lawrence Leeds School of Business University of Colorado Boulder, CO

Queueing Theory Professor Stephen Lawrence Leeds School of Business University of Colorado Boulder, CO

Chapter 18 Management of Waiting Lines

19-1 McGraw-Hill Ryerson Operations Management, 2 nd Canadian Edition, by Stevenson & Hojati Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights.

To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 14-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 14.

Lecture 14 – Queuing Systems

1 1 Slide Chapter 6 Simulation n Advantages and Disadvantages of Using Simulation n Modeling n Random Variables and Pseudo-Random Numbers n Time Increments.

Queuing Models and Capacity Planning

___________________________________________________________________________ Operations Research  Jan Fábry Waiting Line Models.

Introduction to Management Science

Queueing Theory Models Training Presentation By: Seth Randall.

(C) 2009 J. M. Garrido1 Object Oriented Simulation with Java.

Waiting Line Models ___________________________________________________________________________ Quantitative Methods of Management  Jan Fábry.

D-1 © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J Operations Management Waiting-Line Models Module D.

McGraw-Hill/Irwin Copyright © 2008 by The McGraw-Hill Companies, Inc. All rights reserved. Service Processes CHAPTER 5.

Introduction to Queueing Theory

4/11: Queuing Models Collect homework, roll call Queuing Theory, Situations Single-Channel Waiting Line System –Distribution of arrivals –Distribution.

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 1.

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.

McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 18 Management of Management of Waiting Lines.

1-1 McGraw-Hill/Irwin ©2009 The McGraw-Hill Companies, All Rights Reserved 1 Chapter 8A Waiting Line Management.

Entities and Objects The major components in a model are entities, entity types are implemented as Java classes The active entities have a life of their.

Queueing Theory What is a queue? Examples of queues: Grocery store checkout Fast food (McDonalds – vs- Wendy’s) Hospital Emergency rooms Machines waiting.

1 Elements of Queuing Theory The queuing model –Core components; –Notation; –Parameters and performance measures –Characteristics; Markov Process –Discrete-time.

Waiting Lines and Queuing Models. Queuing Theory  The study of the behavior of waiting lines Importance to business There is a tradeoff between faster.

1 Queuing Systems (2). Queueing Models (Henry C. Co)2 Queuing Analysis Cost of service capacity Cost of customers waiting Cost Service capacity Total.

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F-1 Operations.

Waiting Lines and Queuing Theory Models

Chapter 1 Introduction. “Wait-in-line” is a common phenomenon in everywhere. Reason: Demand is more than service. “How long must a customer wait?” or.

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.

Waiting Line Theory Akhid Yulianto, SE, MSc (log).

Adeyl Khan, Faculty, BBA, NSU Elements of Queuing System ArrivalsServiceWaiting line Exit Processing order System.

Advantages of simulation 1. New policies, operating procedures, information flows and son on can be explored without disrupting ongoing operation of the.

Waiting Line Theroy BY, PRAYASH NEUPANE, KARAN CHAND & SANTOSH SHERESTHA.

Queuing Models.

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.

Simple Queueing Theory: Page 5.1 CPE Systems Modelling & Simulation Techniques Topic 5: Simple Queueing Theory  Queueing Models  Kendall notation.

QUEUING THOERY. To describe a queuing system, an input process and an output process must be specified. Examples of input and output processes are: SituationInput.

Module D Waiting Line Models.

Queuing Theory. Model Customers arrive randomly in accordance with some arrival time distribution. One server services customers in order of arrival.

McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.

WAITING LINES AND SIMULATION

Chapter 1 Introduction.

McGraw-Hill/Irwin ©2009 The McGraw-Hill Companies, All Rights Reserved

ADVANTAGES OF SIMULATION

Management of Waiting Lines

Queuing Systems Don Sutton.

Simulation Modeling.

Mitchell Jareo MAT4340 – Operations Research Dr. Bauldry

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.

Presentation transcript:

MGT 560 Queuing System Simulation Stochastic Modeling © Victor E. Sower, Ph.D., C.Q.E. 2007

Steps in Simulation Process 1.Define problem 2.Define important variables in problem 3.Collect data 4.Construct mathematical model 5.Validate model 6.Define experiments to run 7.Run experiments 8.Consider results (possible model modification) 9.Decide on course of action Victor E. Sower, Ph.D., C.Q.E. 2007

Advantages of Simulation Straightforward and flexible Can analyze complex real-world situations Can use any distributions—not just standard ones Time compression Can address “what-if” questions Off-line Can study interactions of individual variables and components Victor E. Sower, Ph.D., C.Q.E. 2007

Limitations of Simulation Expensive and time consuming Does not generate optimal solutions The results from the model are limited by the quality of the design of the model Each simulation model is unique to a particular problem Victor E. Sower, Ph.D., C.Q.E. 2007

Types of Queuing Systems Single channel; Single phase Victor E. Sower, Ph.D., C.Q.E Channel – the number of parallel servers Phase – the number of servers in sequence

Types of Queuing Systems Multiple channel; Single phase Victor E. Sower, Ph.D., C.Q.E. 2007

Types of Queuing Systems Single channel/Multiple phase Victor E. Sower, Ph.D., C.Q.E. 2007

Types of Queuing Systems Multiple channel/Multiple phase Victor E. Sower, Ph.D., C.Q.E. 2007

Data Collection Source of customers – Infinite – Finite Victor E. Sower, Ph.D., C.Q.E. 2007

Data Collection Arrival Rate/Interarrival Time – Arrival Rate (Poisson) – Interarrival Time (Exponential) Victor E. Sower, Ph.D., C.Q.E. 2007

Data Collection Service Rate/Service Time – Service Rate (Poisson) – Service Time (Exponential) Victor E. Sower, Ph.D., C.Q.E. 2007

Data Collection Queue Discipline – FCFS – LIFO – Random – Others Victor E. Sower, Ph.D., C.Q.E. 2007

Data Collection Queue Length – Infinite – Finite Balking Victor E. Sower, Ph.D., C.Q.E. 2007

System Operating Characteristics Results from Model LAvg. no. of customers in system L q Avg. no. of customers in the queue WAvg. time customer spends in system W q Avg. time customer spends in queue pUtilization rate Victor E. Sower, Ph.D., C.Q.E. 2007

System Considerations Waiting line costs Service quality Psychology of waiting Balking Victor E. Sower, Ph.D., C.Q.E. 2007