Control chart for number of customers in the system of M [X] / M / 1 Queueing system

Similar documents
Construction of Control Chart for Random Queue Length for (M / M / c): ( / FCFS) Queueing Model Using Skewness

B. Maddah ENMG 622 ENMG /27/07

CS/ECE 715 Spring 2004 Homework 5 (Due date: March 16)

M/M/1 queueing system with server start up, unreliable server and balking

Simulation of Discrete Event Systems

Analysis Of Single Server Queueing System With Batch Service. Under Multiple Vacations With Loss And Feedback


First come, first served (FCFS) Batch

On Poisson Bulk Arrival Queue: M / M /2/ N with. Balking, Reneging and Heterogeneous servers

Control Charts for Mean for Non-Normally Correlated Data

Asymptotic distribution of products of sums of independent random variables

Approximate Confidence Interval for the Reciprocal of a Normal Mean with a Known Coefficient of Variation

Mathematical Modeling of Optimum 3 Step Stress Accelerated Life Testing for Generalized Pareto Distribution

FLUID LIMIT FOR CUMULATIVE IDLE TIME IN MULTIPHASE QUEUES. Akademijos 4, LT-08663, Vilnius, LITHUANIA 1,2 Vilnius University

Reliability and Queueing

ANOTHER WEIGHTED WEIBULL DISTRIBUTION FROM AZZALINI S FAMILY

SINGLE-CHANNEL QUEUING PROBLEMS APPROACH

A Feedback Retrial Queuing System with Starting Failures and Single Vacation

CONTROL CHARTS FOR THE LOGNORMAL DISTRIBUTION

Statistical Fundamentals and Control Charts

There is no straightforward approach for choosing the warmup period l.

Bayesian and E- Bayesian Method of Estimation of Parameter of Rayleigh Distribution- A Bayesian Approach under Linex Loss Function

1 Introduction to reducing variance in Monte Carlo simulations

Estimating the Change Point of Bivariate Binomial Processes Experiencing Step Changes in Their Mean

Maximum likelihood estimation from record-breaking data for the generalized Pareto distribution

Comparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series

A New Multivariate Markov Chain Model with Applications to Sales Demand Forecasting

OPTIMAL PIECEWISE UNIFORM VECTOR QUANTIZATION OF THE MEMORYLESS LAPLACIAN SOURCE

Average Number of Real Zeros of Random Fractional Polynomial-II

THE POTENTIALS METHOD FOR A CLOSED QUEUEING SYSTEM WITH HYSTERETIC STRATEGY OF THE SERVICE TIME CHANGE

Queuing Theory. Basic properties, Markovian models, Networks of queues, General service time distributions, Finite source models, Multiserver queues

Mixed Acceptance Sampling Plans for Multiple Products Indexed by Cost of Inspection

R. van Zyl 1, A.J. van der Merwe 2. Quintiles International, University of the Free State

POWER AKASH DISTRIBUTION AND ITS APPLICATION

Announcements. Queueing Systems: Lecture 1. Lecture Outline. Topics in Queueing Theory

The standard deviation of the mean

Solution of Differential Equation from the Transform Technique

The statistical pattern of the arrival can be indicated through the probability distribution of the number of the arrivals in an interval.

On Distance and Similarity Measures of Intuitionistic Fuzzy Multi Set

An M/G/1 Retrial Queue with a Single Vacation Scheme and General Retrial Times

Extreme Value Charts and Analysis of Means (ANOM) Based on the Log Logistic Distribution

A General Family of Estimators for Estimating Population Variance Using Known Value of Some Population Parameter(s)

Dominating Sets and Domination Polynomials of Square Of Cycles

Output Analysis (2, Chapters 10 &11 Law)

MOMENT-METHOD ESTIMATION BASED ON CENSORED SAMPLE

Confidence Interval for Standard Deviation of Normal Distribution with Known Coefficients of Variation

Research Article A New Second-Order Iteration Method for Solving Nonlinear Equations

Introducing a Novel Bivariate Generalized Skew-Symmetric Normal Distribution

Comparison of Minimum Initial Capital with Investment and Non-investment Discrete Time Surplus Processes

ESTIMATION AND PREDICTION BASED ON K-RECORD VALUES FROM NORMAL DISTRIBUTION

ON POINTWISE BINOMIAL APPROXIMATION

COM-Poisson Neyman Type A Distribution and its Properties

Estimation of Gumbel Parameters under Ranked Set Sampling

c. Explain the basic Newsvendor model. Why is it useful for SC models? e. What additional research do you believe will be helpful in this area?

Double Stage Shrinkage Estimator of Two Parameters. Generalized Exponential Distribution

Research Article A Unified Weight Formula for Calculating the Sample Variance from Weighted Successive Differences

Analysis of a queueing model with service threshold

Estimation of the Population Mean in Presence of Non-Response

Decoupling Zeros of Positive Discrete-Time Linear Systems*

Department of Mathematics

Approximating the ruin probability of finite-time surplus process with Adaptive Moving Total Exponential Least Square

The Sampling Distribution of the Maximum. Likelihood Estimators for the Parameters of. Beta-Binomial Distribution

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.

Fuzzy Shortest Path with α- Cuts

Research Article Control of Traffic Intensity in Hyperexponential and Mixed Erlang Queueing Systems with a Method Based on SPRT

A NEW CLASS OF 2-STEP RATIONAL MULTISTEP METHODS

This section is optional.

A Note on the Distribution of the Number of Prime Factors of the Integers

Increasing timing capacity using packet coloring

Estimation of Population Mean Using Co-Efficient of Variation and Median of an Auxiliary Variable

THE NUMERICAL SOLUTION OF THE NEWTONIAN FLUIDS FLOW DUE TO A STRETCHING CYLINDER BY SOR ITERATIVE PROCEDURE ABSTRACT

ADVANCED SOFTWARE ENGINEERING

Observations on Derived K-Fibonacci and Derived K- Lucas Sequences

Stat 319 Theory of Statistics (2) Exercises

G. R. Pasha Department of Statistics Bahauddin Zakariya University Multan, Pakistan

Uniform Strict Practical Stability Criteria for Impulsive Functional Differential Equations

Goodness-Of-Fit For The Generalized Exponential Distribution. Abstract

Using the IML Procedure to Examine the Efficacy of a New Control Charting Technique

Application of Homotopy Analysis Method for Solving various types of Problems of Ordinary Differential Equations

Research Article Nonexistence of Homoclinic Solutions for a Class of Discrete Hamiltonian Systems

6.041/6.431 Spring 2009 Final Exam Thursday, May 21, 1:30-4:30 PM.

Queueing theory and Replacement model

CHAPTER 4 BIVARIATE DISTRIBUTION EXTENSION

Modified Logistic Maps for Cryptographic Application

Two-step Extrapolated Newton s Method with High Efficiency Index

Stability of fractional positive nonlinear systems

Intermittent demand forecasting by using Neural Network with simulated data

A New Class of Ternary Zero Correlation Zone Sequence Sets Based on Mutually Orthogonal Complementary Sets

On an Application of Bayesian Estimation

Record Values from T-X Family of. Pareto-Exponential Distribution with. Properties and Simulations

Bayesian Control Charts for the Two-parameter Exponential Distribution

Central limit theorem and almost sure central limit theorem for the product of some partial sums

Reliability Measures of a Series System with Weibull Failure Laws

Probability and statistics: basic terms

Surveying the Variance Reduction Methods

Minimax Estimation of the Parameter of Maxwell Distribution Under Different Loss Functions

Bernoulli trials with variable probabilities - an observation by Feller

Section 5.5. Infinite Series: The Ratio Test

4. Partial Sums and the Central Limit Theorem

Some Common Fixed Point Theorems in Cone Rectangular Metric Space under T Kannan and T Reich Contractive Conditions

Transcription:

Iteratioal Joural of Iovative Research i Sciece, Egieerig ad Techology (A ISO 3297: 07 Certified Orgaiatio) Cotrol chart for umber of customers i the system of M [X] / M / Queueig system T.Poogodi, Dr. (Mrs.) S.Muthulakshmi 2 Assistat Professor, Departmet of Sciece ad Humaities,Aviashiligam Uiversity, Coimbatore, Tamil Nadu, Idia Professor, Departmet of Mathematics, Aviashiligam Uiversity, Coimbatore, Tamil Nadu, Idia 2 Abstract:Queueig models characteried by bulk arrival or bulk service of either fixed or variable sies are commoly foud i modellig of traffic ad trasportatio systems, complex computer ad telecommuicatio systems, ivetory repleishmet system ad other real-life applicatios. Cotrol chart techique eables to moitor the performace of these queues. Average queue legth ad average waitig time are the mai observable performace characteristics of ay queueig system. I this paper cotrol limits are established for M [X] /M / queueig model whe the batch sie follows geometric distributio. Numerical results are added to highlight its applicatios. Keywords:bulk arrival, queue legth, geometric distributio, Poisso arrival, expoetial service, cotrol limits. I. INTRODUCTION I geeral, i queueig models, it is assumed that the customers arrive sigly at service facility. But this assumptio is violated i may real life queueig situatios. Letters arrivig at a post office, ships arrivig at a port i a covoy, people attedig a weddig receptio etc., are some of the examples of queueig situatios i which customers arrive i groups. Queueig model cosistig of bulk arrival has bee discussed by Gross ad Harris (998), Carme Armero ad David Coesa (00) ad several others. Cotrol chart is a quality cotrol techique evolved iitially to moitor productio processes. Motgomery (05) proposed a umber of applicatios of Shewhart cotrol charts i assurig quality i maufacturig idustries. Shore (00) developed cotrol chart for radom queue legth of M /M /S queueig model by cosiderig the first three momets ad also Shore (06) developed Shewhart-like geeral cotrol charts for G/G/S queueig system usig skewess. Khaparde ad Dhabe (0) costructed the cotrol chart for radom queue legth of M/M/ queueig model usig method of weighted variace. Poogodi ad Muthulakshmi (2) aalysed umber of customers i system of M/E k / queueig model usig cotrol chart techique. I this paper Shewhart cotrol chart for umber of customers i the system of bulk arrival queueig model M [X] /M / is proposed. Sectio 2 relates to the queueig model descriptio. Mea ad variace of umber of customers i the queueig system are derived i sectio 3. Parameters of cotrol chart for umber of customers are established i sectio 4. Numerical results are obtaied i sectio 5 to aalyse the effect of parameters. II. M [X] /M/ MODEL DESCRIPTION Cosider a sigle server queueig model i which the arrivals occur i batches accordig to Poisso process with rate 0. The batch sie X is a radom variable with P(X = k) = C k, k =, 2, 3,. Customers are served oe by oe ad the service time distributio is expoetial with rate µ. Let P be the probability that there are customers i the system. Copyright to IJIRSET www.ijirset.com 9977

Iteratioal Joural of Iovative Research i Sciece, Egieerig ad Techology The steady state equatios goverig this model are 0 = - ( + µ) P + µ P + + P kc k ( ) k (A ISO 3297: 07 Certified Orgaiatio) 0 = -P 0 + µp () The system of equatios () may be solved usig geeratig fuctio approach. Defie the geeratig fuctios of the steady state probabilities {P }ad the batch sie distributio {c } respectively as P() 0 P, C() c, Multiplyig equatio () by appropriate powers of ad summig, we get μ 0 λ P μ P P λ P kc k (2) 0 k k k Cosider Pk c k c k Pk C()P(). (3) k k k Usig equatio (3), equatio (2) becomes μ 0 = λp() μ(p() P0 ) (P() P0 ) λc()p() Solvig for P(), we get μp P() = 0 ( ), (4) μ( ) λ( C()) The geeratig fuctio of the complemetary batch sie probabilities Pr(X > x) = -C x = C ~ is give by C ~ () ~ c C() Takig r = /µ, equatio (4) yields P 0 P() = rc ~ () E(X(X )) Clearly, C ~ () E(X) ad C ~ () 2 Usig the ormaliig coditios, we obtai P 0 = - ρ, where ρ = r E(X). If N s ad N q are umber of customers i the system ad umber of customers i the queue respectively the ρ re(x ) N s = 2( adn q = N s ρ. 2 x Copyright to IJIRSET www.ijirset.com 9978

Iteratioal Joural of Iovative Research i Sciece, Egieerig ad Techology (A ISO 3297: 07 Certified Orgaiatio) Assume that the umber of customers i ay arrivig batch follows geometric distributio with parameter. The the probability mass fuctio of the batch sie is c x = (-) x-, 0<< ( ) The C () = ad E(X) with α r ρ α (5) From equatios (4) ad (5), we obtai ( (α ( 0 Compariso of like powers of o both sides gives P () = 0 α α ( ρ P = ρ( ( (α (, 0. III. MEAN AND VARIANCE Let N s deote the umber of customers i the system (both i queue ad i service).the the expected umber of customers i the system is ρ E(N s ) = (6) ( ( ad the variace of the umber of customers i the systemis ρ( α( ) Var (N s ) = (7) 2 2 ( ( IV. PARAMETERS OF THE CONTROL CHART The parameters of Shewhart cotrol chart, uder the assumptio that the umber of customers i the system follows ormal distributio, are give by UCL = E (N s ) + 3 Var(N s ) (8) CL = E (N s ) LCL = E (N s ) - 3 Var(N s ) The parameters of the cotrol chart for M [X] /M / queueig model, usig (6) ad (7) i (8), are obtaied as ρ CL = ( ( UCL = ρ 3 ρ( α( ) ( ( Copyright to IJIRSET www.ijirset.com 9979

Iteratioal Joural of Iovative Research i Sciece, Egieerig ad Techology (A ISO 3297: 07 Certified Orgaiatio) LCL = ρ 3 ρ( α( ) ( ( V. NUMERICAL ANALYSIS Numerical aalysis is carried out to aalye the performace of queueig system with referece to the parameters λ, µ ad α. As LCL values are egative for the selected values of the parameters, they are cosidered as ero ad therefore ot show i the table as a separate colum. Table gives the traffic itesity ad the cotrol chart parameters for umber of customers i the queueig system for various values of λ, µ ad α. TABLE CONTROL CHART PARAMETERS FOR THE SYSTEM SIZE OF M [X] /M / MODEL λ µ α ρ CL UCL 0. 0.47.046 5.797 0 0.6 0.476.082 5.980 0.7 0.482.2 6.72 0. 0.34 0.538 3.563 0.6 0.37 0.554 3.659 0.7 0.32 0.570 3.758 0.8 0.325 0.588 3.862 4 0. 0.235 0.362 2.726 0.6 0.238 0.372 2.795 0.7 0.24 0.382 2.866 0.8 0.244 0.393 2.940 0. 0.706 2.824 3.26 0 0.6 0.74 2.976 3.779 0.7 0.723 3.43 4.49 6 0.8 0.732 3.326.269 0. 0.47.046 5.797 0.6 0.476.082 5.980 0.7 0.482.2 6.73 0. 0.353 0.642 4.0358 0.6 0.357 0.66 4.48 0.7 0.36 0.682 4.265 0.8 0.366 0.704 4.387 Copyright to IJIRSET www.ijirset.com 9980

Iteratioal Joural of Iovative Research i Sciece, Egieerig ad Techology (A ISO 3297: 07 Certified Orgaiatio) 0. 0.94 8.823 77.288 8 0 0.6 0.952 23.80 97.280 0.7 0.964 32.29 30.606 0.8 0.976 48.780 97.265 0. 0.627.98 9.693 0.6 0.635 2.070 0.089 0.7 0.643 2.66 0.5 0.8 0.650 2.269 0.970 0. 0.47.046 5.797 0.6 0.476.082 5.980 0.7 0.482.2 6.73 Numerical values i the table reveal the followig features:. (a) icrease i α icreases the parameters ρ, CL ad UCL for fixed values of λ ad µ. (b) icrease i µ decreases the parameters ρ, CL ad UCL for fixed values of λ ad α. (c) icrease i λ icreases the parameters ρ, CL ad UCL for fixed values of µ ad α. VI. CONCLUSION The preseted model has potetial applicatios i practical systems such as maufacturig systems, telecommuicatio systems ad computer etworks. REFERENCES [] Carme Armero ad David Coesa, Predictio i Markovia Bulk arrival queues, Spriger, Queueig systems 34,327-350, 00. [2] Gross. D. ad Harris. M., Fudametals of queueig theory, 5th editio, Joh Wiley & Sos, Ic., 998. [3] Khaparde.M.V. ad Dhabe.S. D., Cotrol chart for radom queue legth N for (M/M/): ( /FCFS) Queueig model, Iteratioal Joural of Agricultural ad Statistical scieces, Vol., 39-334, 0. [4] Motgomery D.C., Itroductio to statistical quality cotrol, 5th editio, Joh Wiley & Sos, Ic., 05. [5] Shore. H., Cotrol charts for the queue legth i a G/G/S System, IIE Trasactios, 38, 7-3, 06. [6] Shore. H., Geeral cotrol charts for attributes, IIE trasactios, 32, 49-60, 00. [7] Poogodi.T ad Muthulakshmi. S., Radom queue legth cotrol chart for (M/Ek/): ( /FCFS) queueig model, Iteratioal Joural of Mathematical Archive- 3(9), 3340-3344, 2. Copyright to IJIRSET www.ijirset.com 998