CHAPTER 3 CALL CENTER QUEUING MODEL WITH LOGNORMAL SERVICE TIME DISTRIBUTION


 Albert Turner
 3 years ago
 Views:
Transcription
1 31 CHAPTER 3 CALL CENTER QUEUING MODEL WITH LOGNORMAL SERVICE TIME DISTRIBUTION 3.1 INTRODUCTION In this chapter, construction of queuing model with nonexponential service time distribution, performance measures of the call center, simulation experiment and results are presented. 3.2 MODEL CONSTRUCTION In the present work, queuing model with lognormal service time distribution is constructed to study the performance measures of call center. It is denoted by M/G/s+M with lognormal service time distribution. The basic assumptions behind the M/G/s+M queuing model with lognormal service time distribution of call center are as given below Inter arrival time of calls follows exponential distribution with the parameter 1 Service time of calls follows lognormal distribution with the parameters and The number of servers is s Abandonment time of calls follows exponential distribution with the parameter 2
2 PERFORMANCE MEASURES The call center queuing model involves large and complex model that incorporate many primitives for the call center management. The call center managers are very much interested in evaluating the performance measures of the call center. In order to measure the performance of the call center, the analysis of various performance measures is very much needed to the management of call centers. The commonly encountered performance measures in call center include service level, agent s utilization, abandoned calls, average waiting time, average queue length etc. Among the various performance measures of the call center, the important modeling primitives of complex call center queuing model in evaluating the performance measures appear to be abandoned calls and agent s utilization. These two primitives play an important role in the management of call center industry Abandoned Calls A call that is not served immediately hangs up. Otherwise, it joins the queue from which it will abandon if experiencing waiting time is greater than its patience time. This call is called abandoned calls. In industry, each abandoned calls represents a missed opportunity not only to provide a customer with excellent service but also to generate additional revenue. When trying to manage a large call center in heavy traffic, the call center management should consider the effect of abandoned calls in call center. The major drawback of call center queuing model is the ignorance of abandonment. Due to lack of understanding of abandonment behaviour, most modeler ignores the abandonment. This leads to many diffuiculties in the management of call center.
3 Agent s Utilization Agent s utilization is one of the important primitives of call center management. To improve the quality of service in call center, the agent s utilization is always maintained at maximum level. Due to ignorance of agent s utilization in call center, the call center faces over staffing or under staffing problem. To analyse the staffing problem in call center, agent s utilization plays an important role in call center management. 3.4 SIMULATION EXPERIMENT AND ANALYTICAL METHOD Experimental Setup The value of the parameter 1 of exponential inter arrival time of calls of call center queuing model is assumed as one. The value of the parameter 2 of exponential abandonment time distribution of calls of call center queuing model is assumed as one. The values of parameters and of lognormal service time distribution of calls of the call center queuing model are allowed to vary in their intervals (, ) and (, ) respectively. The numbers of servers are assumed as four. The service discipline of model is FirstCome and FirstServed Simulation Procedure For analysising the abandoned calls and agent s utilization, the approach taken to the simulation experiment was based on the terminology employed by EXTEND simulation software. This software has graphical structure to design through which users can understand the relationship between various modules of simulation model. The modification is also done easily in each module.
4 34 The simulation model for queuing model M/G/s+M is constructed with the help of hierarchical blocks that are used to design the structure of simulation model. The hierarchical blocks are Generate call type, Queue call type and Answer call type. In Generate call type hierarchical block, the inbound calls are generated, based on the assumed distribution and routed to agents for service. If the agent is busy, then calls are placed in the waiting line which is called as queue. The Queue call type hierarchical block transfers the calls from queue to agent whenever agent is free. If waiting time is more than patience time of call, then this call becomes abandoned call which is allowed leave the block through alternative connects. The Answer call type hierarchical block allows the agents to process the calls. The processing time of the calls is based on the assumed distribution in this block. Based on the values of parameters, the mean of exponential distribution for inter arrival and abandonment time distribution, mean and standard deviation of lognormal service time distribution are calculated and are used as input values to the simulation experiment for the model of call center queuing model M/G/s+M. The formulae for calculating the mean and standard deviation of lognormal distribution are Mean = e 2 2 Standard deviation = e e ( 1) The simulation experiment was carried out by fixing the mean of interarrival and abandonment time of calls as one time unit, number of servers as 4 and by varying the mean and standard deviation of lognormal service time distribution for the values of and in their intervals. The simulation experiment of the queuing model M/G/s+M was carried out for replications. Each replication has processed calls at 1 time unit
5 35 in four parallel servers. The results obtained from simulation experiment are the percentage of agent s utilization and abandoned calls for the model Analytical Procedure For the validation of the model, the analytical method is applied to find the agent s utilization. The formula for finding the agent s utilization of E ( s) queuing model is 1 where arrival rate of customer is 1, E(s) is the s mean of service time and s is the number of servers. 3.5 RESULTS BASED ON THE ANALYSIS OF AGENT S UTILIZATION The results are obtained from simulation experiment in terms of abandoned calls and agent s utilization for various input values of the model. The agent s utilization of the queuing model is analysed with the help of simulation method by fixing the parameters value of exponential inter arrival time and abandonment time distribution and allowing the parameters values of lognormal service time distribution in their range. In this section, the agent s utilizations for some of the values of parameters of the model are presented Variations of Percentage of Agent s Utilization with ( ) when = 7 Figure 3.1 shows the variations of percentage of agent s utilization for both analytical and simulation methods when value of is positive and the value of is taken as 7. When value of is greater than 4.5, the percentage of agent s utilization will be more than per cent. When value of
6 36 is from 3.5 to 4.5, the percentage of agent s utilization is increased from 1 per cent to per cent. When value of is less than 3.5, the percentage of agent s utilization will be less than 1 per cent Percentages of Agent's Utilization A nalytic al method S imulation method Figure 3.1 Variations of percentage of agent s utilization with sigma when = 7 The value of parameters of lognormal distribution is allowed to vary in their intervals. Due to this variation of value of parameters of service time distribution, the variance and mean of lognormal service time is changed. Since simulation experiment is done with help of variance and mean of service time distribution as input, the percentage of agent s utilization of call center is increased from per cent to per cent. Therefore, the performance measures of call center are varied from per cent to per cent in all the graphs.
7 Variations of Percentage of Agent s Utilization with when =  4 It is observed from figure 3.2 that the variations of percentage of agent s utilization are changed from per cent to per cent for both analytical and simulation method when value of is positive and the value of is taken as 4. When the values of is greater than 3.5, the percentage of agent s utilization will be more than per cent. When values of is changed from 2.5 to 3.5, the percentage of agent s utilization is changed from 1 per cent to per cent. When value of is less than 2.5, the percentage of agent s utilization will be less than 1 per cent. Percentages of Agent's Utilization A nalytical method S imulation method Figure 3.2 Variations of percentage of agent s utilization with sigma when =  4
8 Variations of Percentage of Agent s Utilization with when =  3 The variations of the percentage of agent s utilization with sigma when = 3 is shown in figure 3.3 for both analytical and simulation method. It is increased from per cent to per cent when is positive values and the value of is taken as 3. When the value of is greater than 3.4, the percentage of agent s utilization will be more than per cent. When value of is changed from 2.5 to 3.4, the percentage of agent s utilization varies from 1 per cent to per cent. When value of is less than 2.5, the percentage of agent s utilization will be less than 1 per cent. Percentages of Agent's Utilization A nalytic al method S imulation method Figure 3.3 Variations of percentage of agent s utilization with sigma when = 3
9 Variations of Percentage of Agent s Utilization with when = 2 Percentages of Agent's Utilization Analytic al method S imulation method Figure 3.4 Variations of percentage of agent s utilization with sigma when = 2 Figure 3.4 presents the variations of percentage of agent s utilization which are increased from per cent to per cent for both analytical and simulation method when value of moves in positive direction and the value of is taken as 2. When value of is greater than 3, the percentage of agent s utilization will be more than per cent. When value of ranges from 1.5 to 3, the percentage of agent s utilization will change from 1 per cent to per cent. When value of is less than 1.5, the percentage of agent s utilization will be less than 1 per cent.
10 3.5.5 Variations of Percentage of Agent s Utilization with when = 1 It is observed from Figure 3.5 that the variations of percentage of agent s utilization increase from per cent to per cent for both analytical and simulation method when value of is in the positive direction and the value of is taken as 1. Percentages of Agent's Utilization Analytic al method S imulation method Figure 3.5 Variations of percentage of agent s utilization with sigma when = 1 When value of is greater than 2.5, the percentage of agent s utilization will be more than per cent. When value of moves from 1 to 2.5, the percentage of agent s utilization will be from 1 per cent to per cent. When value of is less than 1, the percentage of agent s utilization is less than 1 per cent.
11 Variations of Percentage of Agent s Utilization with When = The variations of percentage of agent s utilization are illustrated in figure 3.6 for both analytical and simulation method when the value of increases in positive direction and the value of is assumed as. The variation starts from 1 per cent to per cent. When value of is greater than 2, the percentage of agent s utilization will be more than per cent. When assumes the values from to 2, the percentage of agent s utilization will vary from 1 per cent to per cent. Percentages of Agent's Utilization Analytic al method S imulation method Figure 3.6 Variations of percentage of agent s utilization with sigma when =
12 Variations of Percentage of Agent s Utilization with when = 1 Percentages of Agent's Utilization Analytic al method S imulation method Figure 3.7 Variations of percentage of agent s utilization with sigma when = 1 From Figure 3.7, it is explored that the variations of percentage of agent s utilization has a growth from 3 per cent to per cent for both analytical and simulation method when value of is positive and the value of is taken as 1. When value of is greater than 1.5, the percentage of agent s utilization will be than per cent. When value of varies from to 1.5, the percentage of agent s utilization will lie from 3 per cent to per cent Variations of Percentage of Agent s Utilization with when = 2 It is observed from Figure 3.8 that the variations of percentage of agent s utilization range from per cent to per cent for both analytical
13 43 and simulation method when value of are positive and the value of is taken as 2. Figure 3.8 also shows that for the analytical method, the percentage of agent s utilization remains at per cent. 11 Percentages of Agent's Utilization Analytic al method S imulation method Figure 3.8 Variations of percentage of agent s utilization with sigma when = Variations of Percentage of Agent s Utilization with when = 3 It is observed from Figure 3.9 that the variations of percentage of agent s utilization are increased from 98 per cent to per cent for both analytical and simulation method when value of is positive and the value of is assumed as 3. The percentage of agent s utilization goes near to per cent for positive value of
14 44 11 Percentages of Agent's Utilization Analytic al method S imulation method Figure 3.9 Variations of percentage of agent s utilization with sigma when = 3 Due to the variations of value of parameters in lognormal service time distribution of the queuing model M/G/s+M, the percentage of agent s utilization varies from per cent per cent 3.6 RESULTS BASED ON THE ANALYSIS OF ABANDONED CALLS The analysis of abandoned calls in call center is a very complex task because of the non availability of systematic method or procedures. Hence in this study, the simulation method is applied to analyse the call center. For this analysis, the variations of percentage of abandoned calls are obtained for various values
15 Variations of Percentages of Abandoned Calls with when µ =3 It is observed from Figure 3.1 that the variations of percentage of abandoned calls range from 51 per cent to per cent when (sigma) value is allowed in positive direction and the value of µ is assumed as 3. It is seen that the value of percentage of abandoned calls increases with an increase value of. Percentage of Abandoned Calls Figure 3.1 Variations of percentages of abandoned calls with sigma when µ =3
16 Variations of Percentages of Abandoned Calls with when µ = 2 Percentage of Abandoned Calls Figure 3.11 Variations of percentages of abandoned calls with sigma when µ = 2 It is evident from Figure 3.11 that the variations of percentage of abandoned calls slope from 9 per cent to per cent when (sigma) value is allowed to vary in positive direction and the value of µ is assumed as 2.
17 Variations of Percentage of Abandoned Calls with when µ = 1 Percentage of Abandoned Calls Figure 3.12 Variations of percentage of abandoned calls with sigma when µ = 1 It is depicted from Figure 3.12 that the variations of percentage of abandoned calls shows an upward trend from per cent to per cent when (sigma) value is allowed in positive direction and the value of µ is assumed as 1. When the value of is less than 1.5, the percentage of abandoned calls is less than 1 per cent Variations of Percentage of Abandoned Calls with when µ = Figure 3.13 exhibits that the variations of percentage of abandoned calls witness a positive trend ranging from per cent to per cent when (sigma) value is allowed in positive direction and the value of µ is assumed as
18 48. Moreover, when value of is less than 2, the percentage of abandoned calls will be less than 1 per cent. Percentage of Abandoned Calls Figure 3.13 Variations of percentage of abandoned calls with sigma when µ = Variations of Percentage of Abandoned Calls with when µ = 1 Figure 3.14 reveals that the variations of percentage of abandoned calls experience a positive trend varying from per cent to per cent when (sigma) value is allowed in positive direction and the value of µ is assumed as 1. Further, when value of is less than 2.5, the percentage of abandoned calls would be less than 1 per cent. The percentage of abandoned calls gradually increase when is greater than 2.5.
19 49 Percentage of Abandoned Calls Figure 3.14 Variations of percentage of abandoned calls with sigma when µ = Variations of Percentage of Abandoned Calls with when µ = 2 It is understood from Figure 3.15 that the curve on the variations of percentage of abandoned calls slope up from per cent to per cent when (sigma) value is allowed in positive direction and the value of µ is assumed as 2. It is added that when the value of is less than 2.7, the percentage of abandoned calls will be less than 1 per cent. The curve rises smoothly when is greater than 2.7
20 5 Percentage of Abandoned Calls Figure 3.15 Variations of percentage of abandoned calls with sigma when µ = Variations of Percentage of Abandoned Calls with when µ = 3 Percentage of Abandoned Calls Figure 3.16 Variations of percentage of abandoned calls with sigma when µ = 3
21 51 Figure 3.16 shows that the variations of percentage of abandoned calls vary from per cent to per cent when (sigma) value is met the positive values and the value of µ is assumed as 3. It is seen that the percentage of abandoned calls is less than 1 per cent when value of ranges from to Variations of Percentages of Abandoned Calls with when µ = 4 Percentage of Abandoned Calls Figure 3.17 Variations of percentage of abandoned calls with sigma when µ = 4 Figure 3.17 finds that the variations of percentage of abandoned calls vary between per cent and per cent when (sigma) value is witnessed at an upward trend and the value of µ is assumed as 4. Further, when value of is less than 3.5, the percentage of abandoned calls will be below 1 per cent.
22 Variations of Percentage of Abandoned Calls with when µ = 7 Figure 3.18 presents that the variations of percentage of abandoned calls slope up from per cent to per cent when (sigma) value is allowed in positive direction and the value of µ is assumed as 7. While the value of is below to 4, the percentage of abandoned calls will be less than 1 per cent. Percentage of Abandoned Calls Figure 3.18 Variations of percentages of abandoned calls with sigma when µ = 7 Due to the variations of value of parameters in lognormal service time distribution of the queuing model M/G/s+M, the percentage of abandoned calls varies from per cent per cent
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
More informationWaiting 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
More informationCALL CENTER PERFORMANCE EVALUATION USING QUEUEING NETWORK AND SIMULATION
CALL CENTER PERFORMANCE EVALUATION USING QUEUEING NETWORK AND SIMULATION MA 597 Assignment K.Anjaneyulu, Roll no: 06212303 1. Introduction A call center may be defined as a service unit where a group of
More informationAssignment #2 for Computer Networks
Assignment # for Computer Networks Savvas C. Nikiforou Department of Computer Science and Engineering University of South Florida Tampa, FL 6 Abstract The purpose of this assignment is to compare the queueing
More informationRulebased Traffic Management for Inbound Call Centers
Vrije Universiteit Amsterdam Research Paper Business Analytics Rulebased Traffic Management for Inbound Call Centers Auteur: Tim Steinkuhler Supervisor: Prof. Dr. Ger Koole October 7, 2014 Contents Preface
More informationLecture 3 APPLICATION OF SIMULATION IN SERVICE OPERATIONS MANAGEMENT
Lecture 3 APPLICATION OF SIMULATION IN SERVICE OPERATIONS MANAGEMENT Learning Objective To discuss application of simulation in services 1. SIMULATION Simulation is a powerful technique for solving a wide
More informationASD1 SIMULATION: A KEY TO CALL CENTER MANAGEMENT. Rupesh Chokshi Project Manager
ASD1 SIMULATION: A KEY TO CALL CENTER MANAGEMENT Rupesh Chokshi Project Manager AT&T Laboratories Room 3J325 101 Crawfords Corner Road Holmdel, NJ 07733, U.S.A. Phone: 7323325118 Fax: 7329499112
More informationService Management Capacity Planning and Queuing Models
Service Management Capacity Planning and Queuing Models Univ.Prof. Dr.Ing. Wolfgang Maass Chair in Economics Information and Service Systems (ISS) Saarland University, Saarbrücken, Germany WS 2011/2012
More informationScheduling Policies, Batch Sizes, and Manufacturing Lead Times
To appear in IIE Transactions Scheduling Policies, Batch Sizes, and Manufacturing Lead Times Saifallah Benjaafar and Mehdi Sheikhzadeh Department of Mechanical Engineering University of Minnesota Minneapolis,
More informationConclusions and Suggestions for Future Research
6 Conclusions and Suggestions for Future Research In this thesis dynamic inbound contact centers with heterogeneous agents and retrials of impatient customers were analysed. The term dynamic characterises
More informationOutline. Simulation examples will be given in queuing, inventory, reliability and network analysis.
Simulation Examples Today I ll present several examples of simulations that can be performed by devising a simulation table either manually or with a spreadsheet. This will provide insight into the methodology
More informationProcess simulation. Enn Õunapuu enn.ounapuu@ttu.ee
Process simulation Enn Õunapuu enn.ounapuu@ttu.ee Content Problem How? Example Simulation Definition Modeling and simulation functionality allows for preexecution whatif modeling and simulation. Postexecution
More informationSimple Queuing Theory Tools You Can Use in Healthcare
Simple Queuing Theory Tools You Can Use in Healthcare Jeff Johnson Management Engineering Project Director North Colorado Medical Center Abstract Much has been written about queuing theory and its powerful
More informationUNITY CALL CENTER REPORTING. Version 1.0
UNITY CALL CENTER REPORTING Version 1.0 Contents Introduction... 3 Accessing the Reports... 3 Abandoned Call Report... 4 Settings:... 4 Report:... 5 Agent Activity Detail Report... 6 Settings:... 6 Report:...
More informationPerformance Analysis of a Distributed System under TimeVarying Workload and Processor Failures
Performance Analysis of a Distributed System under TimeVarying Workload and Processor Failures Helen Karatza Department of Informatics, Aristotle University 5 Thessaloniki, Greece Email: karatza@csd.auth.gr
More informationQueuing Formulas. 1 Notation 2. 2 Basic Queueing Formulas 2
Queuing Formulas Contents 1 Notation 2 2 Basic Queueing Formulas 2 3 SingleServer Queues 3 3.1 Formulas...................................... 3 3.2 Some additional useful facts...........................
More informationENGINEERING SOLUTION OF A BASIC CALLCENTER MODEL
ENGINEERING SOLUTION OF A BASIC CALLCENTER MODEL by Ward Whitt Department of Industrial Engineering and Operations Research Columbia University, New York, NY 10027 Abstract An algorithm is developed to
More informationSection A. Index. Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1. Page 1 of 11. EduPristine CMA  Part I
Index Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1 EduPristine CMA  Part I Page 1 of 11 Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting
More informationChapter 10. Verification and Validation of Simulation Models Prof. Dr. Mesut Güneş Ch. 10 Verification and Validation of Simulation Models
Chapter 10 Verification and Validation of Simulation Models 10.1 Contents ModelBuilding, Verification, and Validation Verification of Simulation Models Calibration and Validation 10.2 Purpose & Overview
More informationTHE ROLE OF SIMULATION IN CALL CENTER MANAGEMENT. Roger Klungle AAA Michigan. Introduction
THE ROLE OF SIMULATION IN CALL CENTER MANAGEMENT Roger Klungle AAA Michigan Introduction With recent advances in technology and the changing nature of business, call center management has become a rapidly
More informationModelling the performance of computer mirroring with difference queues
Modelling the performance of computer mirroring with difference queues Przemyslaw Pochec Faculty of Computer Science University of New Brunswick, Fredericton, Canada E3A 5A3 email pochec@unb.ca ABSTRACT
More informationService Level Variability of Inbound Call Centers
Service Level Variability of Inbound Call Centers Alex Roubos, Ger Koole & Raik Stolletz Department of Mathematics, VU University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands Chair
More informationPARTIAL CROSS TRAINING IN CALL CENTERS WITH UNCERTAIN ARRIVALS AND GLOBAL SERVICE LEVEL AGREEMENTS. D. J. Medeiros
Proceedings of the 07 Winter Simulation Conference S. G. Henderson, B. Biller, M.H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds. PARTIAL CROSS TRAINING IN CALL CENTERS WITH UNCERTAIN ARRIVALS
More informationDISCRETE 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
More informationIST 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 AsIs Process The AsIs
More informationUNIT 2 QUEUING THEORY
UNIT 2 QUEUING THEORY LESSON 24 Learning Objective: Apply formulae to find solution that will predict the behaviour of the single server model II. Apply formulae to find solution that will predict the
More informationLECTURE  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
More informationLECTURE 16. Readings: Section 5.1. Lecture outline. Random processes Definition of the Bernoulli process Basic properties of the Bernoulli process
LECTURE 16 Readings: Section 5.1 Lecture outline Random processes Definition of the Bernoulli process Basic properties of the Bernoulli process Number of successes Distribution of interarrival times The
More informationASD2 THE ROLE OF SIMULATION IN CALL CENTER MANAGEMENT. Dr. Roger Klungle Manager, Business Operations Analysis
ASD2 THE ROLE OF SIMULATION IN CALL CENTER MANAGEMENT Dr. Roger Klungle Manager, Business Operations Analysis AAA Michigan 1 Auto Club Drive Dearborn, MI 48126 U.S.A. Phone: (313) 3369946 Fax: (313)
More informationCumulative Diagrams: An Example
Cumulative Diagrams: An Example Consider Figure 1 in which the functions (t) and (t) denote, respectively, the demand rate and the service rate (or capacity ) over time at the runway system of an airport
More informationPerformance Analysis, Autumn 2010
Performance Analysis, Autumn 2010 Bengt Jonsson November 16, 2010 Kendall Notation Queueing process described by A/B/X /Y /Z, where Example A is the arrival distribution B is the service pattern X the
More informationNetwork 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
More informationSimulation of Call Center With.
Chapter 4 4.1 INTRODUCTION A call center is a facility designed to support the delivery of some interactive service via telephone communications; typically an office space with multiple workstations manned
More informationPrescriptive Analytics. A business guide
Prescriptive Analytics A business guide May 2014 Contents 3 The Business Value of Prescriptive Analytics 4 What is Prescriptive Analytics? 6 Prescriptive Analytics Methods 7 Integration 8 Business Applications
More informationRecent Advances in Web System Performance Modeling with Queueing Networks. Author: Nikola Janevski Class: CS 736 Software Performance Engineering
Recent Advances in Web System Performance Modeling with Queueing Networks Author: Nikola Janevski Class: CS 736 Software Performance Engineering 1 How are Web systems different Many users Multitier architecture
More informationMTAT.03.231 Business Process Management (BPM) Lecture 6 Quantitative Process Analysis (Queuing & Simulation)
MTAT.03.231 Business Process Management (BPM) Lecture 6 Quantitative Process Analysis (Queuing & Simulation) Marlon Dumas marlon.dumas ät ut. ee Business Process Analysis 2 Process Analysis Techniques
More information1 st year / 20142015/ Principles of Industrial Eng. Chapter 3 / Dr. May G. Kassir. Chapter Three
Chapter Three Scheduling, Sequencing and Dispatching 31 SCHEDULING Scheduling can be defined as prescribing of when and where each operation necessary to manufacture the product is to be performed. It
More informationOPTIMUM TOUR SCHEDULING OF IT HELP DESK AGENTS
OPTIMUM TOUR SCHEDULING OF IT HELP DESK AGENTS Hesham K. Alfares Systems Engineering Department College of Computer Sciences and Engineering King Fahd University of Petroleum & Minerals Saudi Arabia hesham@ccse.kfupm.edu.sa
More informationForecasting and Planning a MultiSkilled Workforce: What You Need To Know
Welcome Forecasting and Planning a MultiSkilled Workforce: What You Need To Know Presented by: Skills Scheduling What is it? Scheduling that takes into account the fact that employees may have one or
More informationKeywords: Dynamic Load Balancing, Process Migration, Load Indices, Threshold Level, Response Time, Process Age.
Volume 3, Issue 10, October 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Load Measurement
More informationA Contact Center Crystal Ball:
A Contact Center Crystal Ball: Marrying the Analyses of Service, Cost, Revenue, and Now, Customer Experience Ric Kosiba, Ph.D. Vice President Interactive Intelligence, Inc. Table of Contents Introduction...
More informationLoad 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: liuxh@systems.stanford.edu Abstract Load
More informationGlossary TASKE Call Center Management Tools Version 7.0. A caller terminates the call before an agent can answer.
Glossary of Terms Term Abandoned Call ABSBH Account Code ACD ACD Call ACD Call Center ACD Queue ACD Record ACD Search Agent Agent Group Agent Identifier Agent Report Definition A caller terminates the
More informationAachen Summer Simulation Seminar 2014
Aachen Summer Simulation Seminar 2014 Lecture 07 Input Modelling + Experimentation + Output Analysis PeerOlaf Siebers pos@cs.nott.ac.uk Motivation 1. Input modelling Improve the understanding about how
More informationNOVEL PRIORITISED EGPRS MEDIUM ACCESS REGIME FOR REDUCED FILE TRANSFER DELAY DURING CONGESTED PERIODS
NOVEL PRIORITISED EGPRS MEDIUM ACCESS REGIME FOR REDUCED FILE TRANSFER DELAY DURING CONGESTED PERIODS D. Todinca, P. Perry and J. Murphy Dublin City University, Ireland ABSTRACT The goal of this paper
More informationPerformance Analysis of Computer Systems
Performance Analysis of Computer Systems Introduction to Queuing Theory Holger Brunst (holger.brunst@tudresden.de) Matthias S. Mueller (matthias.mueller@tudresden.de) Summary of Previous Lecture Simulation
More informationDrop Call Probability in Established Cellular Networks: from data Analysis to Modelling
Drop Call Probability in Established Cellular Networks: from data Analysis to Modelling G. Boggia, P. Camarda, A. D Alconzo, A. De Biasi and M. Siviero DEE  Politecnico di Bari, Via E. Orabona, 47125
More informationRobust Staff Level Optimisation in Call Centres
Robust Staff Level Optimisation in Call Centres Sam Clarke Jesus College University of Oxford A thesis submitted for the degree of M.Sc. Mathematical Modelling and Scientific Computing Trinity 2007 Abstract
More informationA TUTORIAL ON MODELLING CALL CENTRES USING DISCRETE EVENT SIMULATION
A TUTORIAL ON MODELLING CALL CENTRES USING DISCRETE EVENT SIMULATION Benny Mathew Manoj K. Nambiar Innovation Lab Performance Engineering Innovation Lab Performance Engineering Tata Consultancy Services
More informationTime series Forecasting using HoltWinters Exponential Smoothing
Time series Forecasting using HoltWinters Exponential Smoothing Prajakta S. Kalekar(04329008) Kanwal Rekhi School of Information Technology Under the guidance of Prof. Bernard December 6, 2004 Abstract
More information4. Simple regression. QBUS6840 Predictive Analytics. https://www.otexts.org/fpp/4
4. Simple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/4 Outline The simple linear model Least squares estimation Forecasting with regression Nonlinear functional forms Regression
More informationFluid Approximation of a Priority Call Center With TimeVarying Arrivals
Fluid Approximation of a Priority Call Center With TimeVarying Arrivals Ahmad D. Ridley, Ph.D. William Massey, Ph.D. Michael Fu, Ph.D. In this paper, we model a call center as a preemptiveresume priority
More information1. Implementation of a testbed for testing Energy Efficiency by server consolidation using Vmware
1. Implementation of a testbed for testing Energy Efficiency by server consolidation using Vmware Cloud Data centers used by service providers for offering Cloud Computing services are one of the major
More informationOverview of Monte Carlo Simulation, Probability Review and Introduction to Matlab
Monte Carlo Simulation: IEOR E4703 Fall 2004 c 2004 by Martin Haugh Overview of Monte Carlo Simulation, Probability Review and Introduction to Matlab 1 Overview of Monte Carlo Simulation 1.1 Why use simulation?
More informationRESOURCE POOLING AND STAFFING IN CALL CENTERS WITH SKILLBASED ROUTING
RESOURCE POOLING AND STAFFING IN CALL CENTERS WITH SKILLBASED ROUTING by Rodney B. Wallace IBM and The George Washington University rodney.wallace@us.ibm.com Ward Whitt Columbia University ward.whitt@columbia.edu
More informationMinimize Wait Time and Improve the Waiting Experience
Improving the Customer Experience Minimize Wait Time and Improve the Waiting Experience www.lavi.com (888) 2858605 Overview Waiting lines easily become the source of tension between customers and businesses
More informationDiscreteEvent Simulation
DiscreteEvent Simulation Prateek Sharma Abstract: Simulation can be regarded as the emulation of the behavior of a realworld system over an interval of time. The process of simulation relies upon the
More informationA ContentBased Load Balancing Algorithm for Metadata Servers in Cluster File Systems*
A ContentBased Load Balancing Algorithm for Metadata Servers in Cluster File Systems* Junho Jang, Saeyoung Han, Sungyong Park, and Jihoon Yang Department of Computer Science and Interdisciplinary Program
More informationA SIMULATION STUDY FOR DYNAMIC FLEXIBLE JOB SHOP SCHEDULING WITH SEQUENCEDEPENDENT SETUP TIMES
A SIMULATION STUDY FOR DYNAMIC FLEXIBLE JOB SHOP SCHEDULING WITH SEQUENCEDEPENDENT SETUP TIMES by Zakaria Yahia Abdelrasol Abdelgawad A Thesis Submitted to the Faculty of Engineering at Cairo University
More informationAn Overview of Routing and Staffing Algorithms in MultiSkill Customer Contact Centers. Submitted version
An Overview of Routing and Staffing Algorithms in MultiSkill Customer Contact Centers Ger Koole & Auke Pot Department of Mathematics, Vrije Universiteit Amsterdam, The Netherlands Submitted version 6th
More informationThe 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
More informationVariance and Standard Deviation. Variance = ( X X mean ) 2. Symbols. Created 2007 By Michael Worthington Elizabeth City State University
Variance and Standard Deviation Created 2 By Michael Worthington Elizabeth City State University Variance = ( mean ) 2 The mean ( average) is between the largest and the least observations Subtracting
More informationQuantitative Analysis of Cloudbased Streaming Services
of Cloudbased Streaming Services Fang Yu 1, YatWah Wan 2 and RuaHuan Tsaih 1 1. Department of Management Information Systems National Chengchi University, Taipei, Taiwan 2. Graduate Institute of Logistics
More informationThis paper describes an interactive spreadsheetbased tool that can be used to generate data representative
Vol. 8, No. 2, January 2008, pp. 55 64 issn 15320545 08 0802 0055 informs I N F O R M S Transactions on Education An Interactive SpreadsheetBased Tool to Support Teaching Design of Experiments S. T.
More informationSimulation Software 1
Simulation Software 1 Introduction The features that should be programmed in simulation are: Generating random numbers from the uniform distribution Generating random variates from any distribution Advancing
More informationThe Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy
BMI Paper The Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy Faculty of Sciences VU University Amsterdam De Boelelaan 1081 1081 HV Amsterdam Netherlands Author: R.D.R.
More informationAccurate Forecasting: The Heart of Call Center Success
Accurate Forecasting: The Heart of Call Center Success Accurate Forecasting: The Heart of Call Center Success Page 2 Overview In times of economic crisis and dwindling profits, it is more important than
More information8. Time Series and Prediction
8. Time Series and Prediction Definition: A time series is given by a sequence of the values of a variable observed at sequential points in time. e.g. daily maximum temperature, end of day share prices,
More informationVeri cation and Validation of Simulation Models
of of Simulation Models mpressive slide presentations Faculty of Math and CS  UBB 1st Semester 20102011 Other mportant Validate nput Hypothesis Type Error Con dence nterval Using Historical nput of
More informationProceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. MontoyaTorres, J. Hugan, and E. Yücesan, eds.
Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. MontoyaTorres, J. Hugan, and E. Yücesan, eds. DOES THE ERLANG C MODEL FIT IN REAL CALL CENTERS? Thomas R. Robbins D. J. Medeiros
More informationExamining SelfSimilarity Network Traffic intervals
Examining SelfSimilarity Network Traffic intervals Hengky Susanto ByungGuk Kim Computer Science Department University of Massachusetts at Lowell {hsusanto, kim}@cs.uml.edu Abstract Many studies have
More informationChapter 3 RANDOM VARIATE GENERATION
Chapter 3 RANDOM VARIATE GENERATION In order to do a Monte Carlo simulation either by hand or by computer, techniques must be developed for generating values of random variables having known distributions.
More informationPROCESS FLOW IMPROVEMENT PROPOSAL OF A BATCH MANUFACTURING SYSTEM USING ARENA SIMULATION MODELING
PROCESS FLOW IMPROVEMENT PROPOSAL OF A BATCH MANUFACTURING SYSTEM USING ARENA SIMULATION MODELING Chowdury M. L. RAHMAN 1 Shafayet Ullah SABUJ 2 Abstract: Simulation is frequently the technique of choice
More informationUsing Fuzzy Logic Control to Provide Intelligent Traffic Management Service for HighSpeed Networks ABSTRACT:
Using Fuzzy Logic Control to Provide Intelligent Traffic Management Service for HighSpeed Networks ABSTRACT: In view of the fastgrowing Internet traffic, this paper propose a distributed traffic management
More informationOPTIMIZED PERFORMANCE EVALUATIONS OF CLOUD COMPUTING SERVERS
OPTIMIZED PERFORMANCE EVALUATIONS OF CLOUD COMPUTING SERVERS K. Sarathkumar Computer Science Department, Saveetha School of Engineering Saveetha University, Chennai Abstract: The Cloud computing is one
More informationCreating operational shift schedules for thirdlevel IT support: challenges, models and case study
242 Int. J. Services Operations and Informatics, Vol. 3, Nos. 3/4, 2008 Creating operational shift schedules for thirdlevel IT support: challenges, models and case study Segev Wasserkrug*, Shai Taub,
More informationInternet Traffic Variability (Long Range Dependency Effects) Dheeraj Reddy CS8803 Fall 2003
Internet Traffic Variability (Long Range Dependency Effects) Dheeraj Reddy CS8803 Fall 2003 Selfsimilarity and its evolution in Computer Network Measurements Prior models used Poissonlike models Origins
More informationAPPENDIX 1 USER LEVEL IMPLEMENTATION OF PPATPAN IN LINUX SYSTEM
152 APPENDIX 1 USER LEVEL IMPLEMENTATION OF PPATPAN IN LINUX SYSTEM A1.1 INTRODUCTION PPATPAN is implemented in a test bed with five Linux system arranged in a multihop topology. The system is implemented
More informationArena 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
More informationSTT315 Chapter 4 Random Variables & Probability Distributions KM. Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables
Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables Discrete vs. continuous random variables Examples of continuous distributions o Uniform o Exponential o Normal Recall: A random
More informationHow Useful Is Old Information?
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
More informationLecture 8 Performance Measurements and Metrics. Performance Metrics. Outline. Performance Metrics. Performance Metrics Performance Measurements
Outline Lecture 8 Performance Measurements and Metrics Performance Metrics Performance Measurements KuroseRoss: 1.21.4 (HassanJain: Chapter 3 Performance Measurement of TCP/IP Networks ) 20100217
More informationAccurate Forecasting: The Heart of Call Center Success
Accurate Forecasting: The Heart of Call Center Success Accurate Forecasting: The Heart of Call Center Success Page 2 Overview In times of economic crisis and dwindling profits, it is more important than
More informationSimulation 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
More informationITEE Journal. Information Technology & Electrical Engineering International Journal of Information Technology and Electrical Engineering
Performance Analysis of Short Term Scheduling Algorithms 1 Muhammad Usman, 2 Aamir Iqbal, 3 Ehsan Ahmed, 4 Shaukat Ghani, 5 Muhammad Adnan Khan 1,2,3,4 Federal Urdu University of Arts, Science and Technology,
More informationWeb Server Software Architectures
Web Server Software Architectures Author: Daniel A. Menascé Presenter: Noshaba Bakht Web Site performance and scalability 1.workload characteristics. 2.security mechanisms. 3. Web cluster architectures.
More informationDeployment of express checkout lines at supermarkets
Deployment of express checkout lines at supermarkets Maarten Schimmel Research paper Business Analytics April, 213 Supervisor: René Bekker Faculty of Sciences VU University Amsterdam De Boelelaan 181 181
More informationChapter 5: CPU Scheduling. Operating System Concepts 8 th Edition,
Chapter 5: CPU Scheduling, Silberschatz, Galvin and Gagne 2009 Objectives To introduce CPU scheduling, which is the basis for multiprogrammed operating systems To describe various scheduling algorithms
More informationOptimizing Stochastic Supply Chains via Simulation: What is an Appropriate Simulation Run Length?
Optimizing Stochastic Supply Chains via Simulation: What is an Appropriate Simulation Run Length? ArreolaRisa A 1, FortunySantos J 2, VintróSánchez C 3 Abstract The most common solution strategy for
More informationExploiting Simulation for Call Centre Optimization
, June 30  July 2, 2010, London, U.K. Exploiting Simulation for Centre Optimization Salman khtar and Muhammad Latif bstract The global trend in developed economies from manufacturing towards services
More informationUser s Guide for ContactCenters Simulation Library
User s Guide for ContactCenters Simulation Library Generic Simulator for Blend and Multiskill Call Centers Version: March 17, 2014 Eric Buist This document introduces a generic simulator for blend and
More informationSimple Predictive Analytics Curtis Seare
Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use
More informationSystem 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
More informationQuantileQuantile Plot (QQplot) and the Normal Probability Plot. Section 66 : Normal Probability Plot. MAT 2377 (Winter 2012)
MAT 2377 (Winter 2012) QuantileQuantile Plot (QQplot) and the Normal Probability Plot Section 66 : Normal Probability Plot Goal : To verify the underlying assumption of normality, we want to compare
More informationA MANAGERFRIENDLY PLATFORM FOR SIMULATION MODELING AND ANALYSIS OF CALL CENTER QUEUEING SYSTEMS. Robert Saltzman Vijay Mehrotra
Proceedings of the 2004 Winter Simulation Conference R.G. Ingalls, M. D. Rossetti, J. S. Smith, and B. A. Peters, eds. A MANAGERFRIENDLY PLATFORM FOR SIMULATION MODELING AND ANALYSIS OF CALL CENTER QUEUEING
More informationAppendix: Simple Methods for Shift Scheduling in MultiSkill Call Centers
MSOM.1070.0172 Appendix: Simple Methods for Shift Scheduling in MultiSkill Call Centers In Bhulai et al. (2006) we presented a method for computing optimal schedules, separately, after the optimal staffing
More informationEXAM IN COURSE [EKSAMEN I EMNE] TTM4110 Dependability and Performance with Discrete event Simulation [Pålitelighet og ytelse med simulering]
Norwegian University of Science and Technology Department of Telematics Page 1 of 20 Contact during exam [Faglig kontakt under eksamen]: Poul E. Heegaard (94321 / 99286858) EXAM IN COURSE [EKSAMEN I EMNE]
More informationPart 1 : 07/27/10 21:30:31
Question 1  CIA 593 III64  Forecasting Techniques What coefficient of correlation results from the following data? X Y 1 10 2 8 3 6 4 4 5 2 A. 0 B. 1 C. Cannot be determined from the data given. D.
More informationIntroduction to Scheduling 1
CPU Scheduling Basic Concepts Scheduling Criteria Scheduling Algorithms FCFS, SJF, RR Exponential Averaging Multilevel Queue Scheduling Performance Evaluation Scheduling Terminology Scheduling Terminology
More informationAddressing Arrival Rate Uncertainty in Call Center Workforce Management
1 Addressing Arrival Rate Uncertainty in Call Center Workforce Management Thomas R. Robbins Penn State University Abstract Workforce management is a critical component of call center operations. Since
More information