LESSONs NINE and TEN QUEUING MODELS.

Slides:



Advertisements
Similar presentations
Part 3 Probabilistic Decision Models
Advertisements

Introduction Queuing is the study of waiting lines, or queues.
Queuing Models.
QUEUING MODELS Based on slides for Hilier, Hiller, and Lieberman, Introduction to Management Science, Irwin McGraw-Hill.
QMD: Waiting-line analysis
Queuing Models Basic Concepts
Queuing Models Basic Concepts. QUEUING MODELS Queuing is the analysis of waiting lines It can be used to: –Determine the # checkout stands to have open.
Model Antrian By : Render, ect. Outline  Characteristics of a Waiting-Line System.  Arrival characteristics.  Waiting-Line characteristics.  Service.
Waiting Lines and Queuing Theory Models
© The McGraw-Hill Companies, Inc., Technical Note 6 Waiting Line Management.
Queuing Systems Chapter 17.
Waiting Line Models And Service Improvement
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.
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.
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
To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. Chapter 16 Waiting Line Models and.
To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 14-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 14.
Operations Management Waiting-Line Models Module D
QUEUING MODELS Queuing theory is the analysis of waiting lines It can be used to: –Determine the # checkout stands to have open at a store –Determine the.
Chapter 9: Queuing Models
Group members  Hamid Ullah Mian  Mirajuddin  Safi Ullah.

Queuing Theory (Waiting Line Models)
Queuing Models and Capacity Planning
___________________________________________________________________________ Operations Research  Jan Fábry Waiting Line Models.
Introduction to Management Science
Waiting Line Models ___________________________________________________________________________ Quantitative Methods of Management  Jan Fábry.
Management of Waiting Lines McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Copyright © 2006 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Managing Waiting Lines.
Supplement D Waiting Line Models Operations Management by R. Dan Reid & Nada R. Sanders 3rd Edition © Wiley 2005 PowerPoint Presentation by Roger B. Grinde,
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.
Supplement C Waiting Line Models Operations Management by R. Dan Reid & Nada R. Sanders 4th Edition © Wiley 2010.
Copyright 2006 John Wiley & Sons, Inc. Beni Asllani University of Tennessee at Chattanooga Waiting Line Analysis for Service Improvement Operations Management.
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.
Waiting Lines and Queuing Models. Queuing Theory  The study of the behavior of waiting lines Importance to business There is a tradeoff between faster.
Queuing Theory. Introduction Queuing is the study of waiting lines, or queues. The objective of queuing analysis is to design systems that enable organizations.
1 Queuing Systems (2). Queueing Models (Henry C. Co)2 Queuing Analysis Cost of service capacity Cost of customers waiting Cost Service capacity Total.
Supplement D Waiting Line Models
Reid & Sanders, Operations Management © Wiley 2002 Waiting Line Models A SUPPLEMENT.
1 ISE 195 Introduction to Industrial & Systems Engineering.
Copyright 2006 John Wiley & Sons, Inc. Beni Asllani University of Tennessee at Chattanooga Waiting Line Analysis for Service Improvement Operations Management.
Waiting Lines and Queuing Theory Models
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Stevenson and Ozgur First Edition Introduction to Management Science with Spreadsheets McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies,
1 1 Slide © 2009 South-Western, a part of Cengage Learning Slides by John Loucks St. Edward’s University.
1 1 Slide Chapter 12 Waiting Line Models n The Structure of a Waiting Line System n Queuing Systems n Queuing System Input Characteristics n Queuing System.
Waiting Line Theroy BY, PRAYASH NEUPANE, KARAN CHAND & SANTOSH SHERESTHA.
Management of Waiting Lines Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent.
Queuing Models.
OPERATIONS MANAGEMENT INTEGRATING MANUFACTURING AND SERVICES FIFTH EDITION Mark M. Davis Janelle Heineke Copyright ©2005, The McGraw-Hill Companies, Inc.
Simple Queueing Theory: Page 5.1 CPE Systems Modelling & Simulation Techniques Topic 5: Simple Queueing Theory  Queueing Models  Kendall notation.
Managing Customer Waiting Lines and Reservations.
Queueing Theory/Waiting Line: Models and Analysis Navneet Vidyarthi
Abu Bashar Queuing Theory. What is queuing ?? Queues or waiting lines arise when the demand for a service facility exceeds the capacity of that facility,
Managerial Decision Making Chapter 13 Queuing Models.
Module D Waiting Line Models.
Queuing Models Part 1: Basics
population or infinite calling population?
Supplement C Developing the Master Production Schedule
McGraw-Hill/Irwin ©2009 The McGraw-Hill Companies, All Rights Reserved
Chapter 9: Queuing Models
Management of Waiting Lines
Queuing Systems Don Sutton.
Supplement D Waiting Line Models
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.
Queuing Models J. Mercy Arokia Rani Assistant Professor
Presentation transcript:

LESSONs NINE and TEN QUEUING MODELS

Queuing Models Queuing is the study of waiting lines, or queues. The objective of queuing analysis is to design systems that enable organizations to perform optimally according to some criterion. Possible Criteria Maximum Profits. Desired Service Level.

Elements of the queuing process A queuing system consists of five basic components: Calling population Arrival process Queue configuration Queue discipline Service process

CALLING POPULATION

CALLING POPULATION Homogeneous or heterogeneous Can be finite by limiting calls or infinite if population is big

ARRIVAL PROCESS

ARRIVAL PROCESS Static no control Dynamic control by machines or man Balking not joining the queuing Reneging leave line before service

QUEUE CONFIGURATION

QUEUE CONFIGURATION Refers to number of queues, locations, spatial requirement and effect on customer behavior Jockeying – line switching activity

QUEUE DISCIPLINE

SERVICE PROCESS

Possible Service Measurements Average time a customer spends in line. Average length of the waiting line. The probability that an arriving customer must wait for service.

The Arrival Process The random process is more common in businesses. Under three conditions a Poisson Distribution can describe the random arrival process.

The three conditions required for the existence of the Poisson arrival process: Orderliness : one customer, at most, will arrive during any time interval. Stationarity : for a given time frame, the probability of arrivals within a certain time interval is the same for all time intervals of equal length. Independence : the arrival of one customer has no influence on the arrival of another. These conditions are unrestrictive and are approximately satisfied in many situations.

The Poisson Arrival Distribution X k e ! = l t) t ( ) - Where l = mean arrival rate per time unit. t = the length of the interval. e = 2.7182818 (the base of the natural logarithm). k! = k (k -1) (k -2) (k -3) … (3) (2) (1).

HANK’s HARDWARE (An illustration of the Poisson distribution) Customers arrive at Hank’s Hardware according to a Poisson distribution. Between 8:00 a.m. and 9:00 a.m., an average of 6 customers arrive at the store. What is the probability that k = 0, 1, 2, … customers will arrive between 8:00 and 8:30 in the morning.

SOLUTION 1 2 3 Input to the Poisson distribution SOLUTION Input to the Poisson distribution l= 6 customers per hour. t = 0.5 hour. l t = (6)(0.5) = 3. 1 2 3 4 5 6 7 8 3 2 1 1 = 0.224042 0.224042 0.049787 0.149361 2 3 0! 3! 2! 1!

Measures of Queuing System Performance P0 = Probability that there are no customers in the system. Pn = Probability that there are “n” customers in the L = Average number of customers in the system. Lq = Average number of customers in the queue. W = Average time a customer spends in the system. Wq = Average time a customer spends in the queue. Pw = Probability that an arriving customer must wait for service. r = Utilization rate for each server (the percentage of time that each server is busy).

Classification of Queues Queuing system can be classified by: Arrival process. Service process. Number of servers. System size (infinite/finite waiting line). Population size. Notation M (Markovian) = Poisson arrivals or exponential service time. D (Deterministic) = Constant arrival rate or service time. G (General) = General probability for arrivals or service time. Example: M / M / 6 / 10 / 20

M / M / 1 Queuing System Characteristics Poisson arrival process. Exponential service time distribution. A single server. Potentially infinite queue. An infinite population.

Performance Measures for the M / M /1 Queue P0 = 1- (l / m) Pn = [1 - (l / m)] (l/ m)n L = l / (m - l) Lq = l 2 / [m(m - l)] W = 1 / (m - l) Wq = l / [m(m - l)] Pw = l / m r = l / m

MARY’s SHOES Customers arrive at Mary’s Shoes every 12 minutes on the average, according to a Poisson process. Service time is exponentially distributed with an average of 8 minutes per customer. Management is interested in determining the performance measures for this service system.

SOLUTION P0 = 1- (l / m) = 1 - (5 / 7.5) = 0.3333 Input l = 1/ 12 customers per minute = 60/ 12 = 5 per hour. m = 1/ 8 customers per minute = 60/ 8 = 7.5 per hour. Performance Calculations P0 = 1- (l / m) = 1 - (5 / 7.5) = 0.3333 Pn = [1 - (l / m)] (l/ m) = (0.3333)(0.6667)n L = l / (m - l) = 2 Lq = l2/ [m(m - l)] = 1.3333 W = 1 / (m - l) = 0.4 hours = 24 minutes Wq = l / [m(m - l)] = 0.26667 hours = 16 minutes Pw = l / m = 0.6667 r = l / m = 0.6667

m l WINQSB Input Screen

Performance Measurements

M / M / k Queuing Systems Characteristics Customers arrive according to a Poisson process at a mean rate l. Service time follow an exponential distribution. There are k servers, each of which works at a rate of m customers. Infinite population, and possibly infinite line.

Performance Measure

The performance measurements L, Lq, Wq,, can be obtained from Little’s formulas.

Revision Question  At a Food Lion Provision Shop, customers spend an average of 25 mins selecting their groceries and checking out by entering a single line queue served by two cashiers. The service times required for the cashiers to check out customers follow an exponential distribution and average four minutes. Customers arrive at the cashier counter according to a Poisson distribution at the average rate of eight customers per hour. The table below shows part of the computer calculation: Determine the followings : average time a customer spends in the store (in minutes). average number of customers waiting in line prior to being checked out. proportion of customers who will have to wait in line.   What assumption(s) did you make in part (a)? Comment whether the assumption(s) is/are realistic.

Queuing Process Multiple queue advantages : Service can be differentiated Division of labour possible Selection option for customer Deterred balking

Types of services clearly stated Queuing Process Multiple queue disadvantages : Anxiety Lack of fairness Lack of privacy Types of services clearly stated

Queuing Process Single queue advantages : First come first serve (fairness) No anxiety to select fastest line Reneging difficult Queue cutting resolved Privacy enhanced Reducing average waiting time

Unnecessary held up in waiting line Queuing Process Single queue disadvantages : No specialisation Unnecessary held up in waiting line Possible balking

Free to wander about and browse items Queuing Process Take a number advantages : No need for formal line Free to wander about and browse items More relaxing

Require large waiting area Queuing Process Take a number disadvantages : Reneging Require large waiting area

Queuing Process QUEUE DISCIPLINE Policy of selecting next customer from the queue for service First come first serve most popular static method (selection depends on position in queue only)

Queuing Process Dynamic disciplines selection involve some attribute or status in selection Shortest processing time minimise average time of customers

Queuing Process Preemptive priority interrupt current person service for newly arrived customer with higher priority e.g. fire or ambulance services Round robin service give customer partial service and then movers to next waiting customer – alternating between waiting and being served

Queuing Process Service person begins to take orders while customers are still waiting in line is a direct approach to avoid reneging

Queuing Process SERVICE PROCESS Distribution of service times, arrangement of servers, management policies and server behaviour contribute to performance

WAITING PERCEPTION Unoccupied time feels longer than occupied time Preprocess waits feel longer than in process waits Anxiety makes waits seem longer

WAITING PERCEPTION Uncertain waits are longer than known finite waits Unexplained waits are longer than explained wait Unfair waits are longer than equitable waits

WAITING PERCEPTION People more willing to wait for valuable service Solo waiting feels longer than group waiting Customer attitudes

WAITING PERCEPTION Environment Unused facilities and idle staff increase annoyance Unfamiliar music makes perceived time seem longer than familiar background music

REVIEW QUESTIONS 1. What does M/D/1 refers to? 2. What are the characteristics of M / G / 1? 3. How can you measure the performance of a queue?

REVIEW QUESTIONS 4. List and discuss the factors affecting queue perception. 5. What are the five features of a queue? 6. How can you configure queue?