IOSR Journal of Business and Management (IOSR-JBM)
e-ISSN: 2278-487X, p-ISSN: 2319-7668. Volume 10, Issue 1 (May. - Jun. 2013), PP 22-29
www.iosrjournals.org
www.iosrjournals.org 22 | Page
Solving Of Waiting Lines Models in the Bank Using Queuing Theory Model the Practice Case: Islami Bank Bangladesh Limited, Chawkbazar Branch, Chittagong Mohammad Shyfur Rahman chowdhury*, Mohammad Toufiqur Rahman ** and Mohammad Rokibul Kabir*** *Mohammad Shyfur Rahman Chydhury, Lecturer, Department of Business Administration, International Islamic University Chittagong, Bangladesh ** Mohammad Toufiqur Rahman,Lecturer , Department of Business Administration, International Islamic University Chittagong, Bangladesh *** Mohammad Rokibul Kabir, Assistant Professor, Department of Business Administration, International Islamic University Chittagong, Bangladesh Abstract: Waiting lines and service systems are important parts of the business world. In this article we describe several common queuing situations and present mathematical models for analyzing waiting lines following certain assumptions. Those assumptions are that (1) arrivals come from an infinite or very large population, (2) arrivals are Poisson distributed, (3) arrivals are treated on a FIFO basis and do not balk or renege, (4) service times follow the negative exponential distribution or are constant, and (5) the average service rate is faster than the average arrival rate. The model illustrated in this Bank for customers on a level with service is the multiple-channel queuing model with Poisson Arrival and Exponential Service Times (M/M/S). After a series of operating characteristics are computed, total expected costs are studied, total costs is the sum of the cost of providing service plus the cost of waiting time. Finally we find the total minimum expected cost. Keywords: Service; FIFO; M/M/s; Poisson distribution; Queue; Service cost; Utilization factor; Waiting cost; Waiting time, optimization. History: Queuing theory had its beginning in the research work of a Danish engineer named A.K. Erlang. In 1909 Erland experimented with fluctuating demand in telephone traffic. Eight years letter he published a report addressing the delays in automatic dialling equipment. At the end of World War II, Erlang’s early work was extended to more general problems and to business applications of waiting lines.
I. Introudction:
The study of waiting lines, called queuing theory, is one of the oldest and most widely used quantitative analysis techniques. Waiting lines are an everyday occurrence, affective people shopping for groceries buying gasoline, making a bank deposit, or waiting on the telephone for the first available airline reservationists to answer. Queues, another term for waiting lines, may also take the form of machines waiting to be repaired, trucks in line to be unloaded, or airplanes lined up on a runway waiting for permission to take off. The three basic components of a queuing process are arrivals, the actual waiting line and service facilities.
II. Characteristics of a Queuing System:
We take a look at the three part of queuing system (1) the arrival or inputs to the system (sometimes referred to as the calling population), (2) the queue# or the waiting line itself, and (3) the service facility. These three components have certain characteristics that must be examined before mathematical queuing models can be developed. Arrival Characteristics The input source that generates arrivals or customers for the service system ahs three major characteristics. It is important to consider the size of the calling population, the pattern of arrivals at the queuing system, and the behavior of the arrivals.
Size of the Calling Population: Population sizes are considered to be either unlimited (essential infinite) or limited (finite). When the number of or arrivals on hand at any customers given moment is just a small portion of potential arrivals, the calling population is considered unlimited. For practical purpose, in our examples the limited customers arriving at the bank for deposit cash. Most queuing models assume such an infinite calling
Solving Of Waiting Lines Models In The Bank Using Queuing Theory Model The Practice Case:
www.iosrjournals.org 23 | Page
population. When this is not the case, modelling becomes much more complex. An example of a finite
population is a shop with only eight machines that might deposit cash break down and require service.
* The word queue is pronounced like the letter Q, that is, “kew”
Pattern of arrivals at the system: Customers either arrive at a service facility according to some known
schedule customers or else they arrive randomly. Arrivals are considered random when they are independent of
one another and their occurrence cannot be predicted exactly. Frequently in queuing problems, the number of
arrivals per unit of time can be estimated by a probability distribution known as the Poisson distribution., For
any given arrival rate, such as two passengers per hour, or four airplanes per minute, a discrete, Poisson
distribution can be established by using the formula :
t
n
e
n
t
P n t
!
; for n = 0,1,2,3,4,......
Where
P (n;t) = probability of n arrivals
= average arrival rate
e = 2.18
n = number of arrivals per unit of time
Behavior of the Arrival: Most queuing models assume that an arriving passenger is a patient traveler. Patient
customer is people or machines that wait in the queue until they are served and do not switch between lines.
Unfortunately, life and quantitative analysis are complicated by the fact that people have been known to balk or
renege. Balking refers to customers who refuse to join the waiting lines because it is to suit their needs or
interests. Reneging customers are those who enter the queue but then become impatient and leave the need for
queuing theory and waiting line analysis. How many times have you seen a shopper with a basket full of
groceries, including perishables such as milk, frozen food, or meats, simply abandon the shopping cart before
checking out because the line was too long? This expensive occurrence for the store makes managers acutely
aware of the importance of service level decisions.
Waiting Line characteristics
Queue: The waiting line itself is the second component of a queuing system. The length of a line can be either
limited or unlimited. A queue is limited when it cannot, by law of physical restrictions, increase to an infinite
length. Analytic queuing models are treated in this article under an assumption of unlimited queue length. A
queue is unlimited when its size is unrestricted, as in the case of the tollbooth serving arriving automobiles.
Queue discipline: A second waiting line characteristic deals with queue discipline. The refers to the rule by
which passengers in the line are to receive service., Most systems use a queue discipline known as the first in,
first out rule (FIFO). This is obviously not appropriate in all service system, especially those dealing with
emergencies.
In most large companies, when computer-produced pay checks are due out on a specific date, the payroll
program has highest priority over other runs.
Service Facility Characteristics
The third part of any queuing system is the service facility. It is important to examine two basic properties: (1)
the configuration of the service system and (2) the pattern of service times.
Basic Queuing System Configurations: Service systems are usually classified in terms of their number of
channels, or number of servers, and number of phases, or number of service stops, that must be made.
The term FIFS (first in, first served) is often used in place of FIFO. Another discipline, LIFS (last in,
first served), is common when material is stacked or piled and the items on top are used first.
A single-channel system, with one server, is typified by the drive in bank that has only one open teller.
If, on the other hand, the bank had several tellers on duty and each customer waited in one common line for the
first available teller, we would have a multi-channel system at work. Many banks today are multi-channel
service systems, as are most large barbershops and many airline ticket counters.
A single-phase system is one in which the customer receives service from only one station and then exits the
system. Multiphase implies two or more stops before leaving the system.
Service Time Distribution: Service patterns are like arrival patterns in that they can be either constant or
random. If service time is constant, it takes the same amount of time to take Care of each customer. More often,
service times are randomly distributed in many cases it can be assumed that random service times are described
Solving Of Waiting Lines Models In The Bank Using Queuing Theory Model The Practice Case:
www.iosrjournals.org 24 | Page
by the negative exponential probability distribution. This is a mathematically convenient assumption if arrival
rates are Poisson distributed.
The exponential distribution is important to the process of building mathematical queuing models because many
of the models‟ theoretical underpinning are based on the assumption of Poisson arrivals and exponential
services. Before they are applied, however, the quantitative analyst can and should observe, collect, and pilot
service time data to determine if they fit the exponential distribution.
III. Mathematical Models:
3.1 Single-Channel Queuing Model with Poisson Arrivals and Exponential service times (M/M/1):
We present an analytical approach to determine important measures of performance in a typical service system.
After these numerical measures have been computed, it will be possible to add in cost data and begin to make
decisions that balance desirable service levels with waiting line service costs.
Assumptions of the Model
The single-channel, single-phase model considered here is one of the most widely used and simplest queuing
models. It invo
IOSR Journal of Business and Management (IOSR-JBM)
e-ISSN: 2278-487X, p-ISSN: 2319-7668. Volume 10, Issue 1 (May. - Jun. 2013), PP 22-29
www.iosrjournals.org
www.iosrjournals.org 22 | Page
Solving Of Waiting Lines Models in the Bank Using Queuing Theory Model the Practice Case: Islami Bank Bangladesh Limited, Chawkbazar Branch, Chittagong Mohammad Shyfur Rahman chowdhury*, Mohammad Toufiqur Rahman ** and Mohammad Rokibul Kabir*** *Mohammad Shyfur Rahman Chydhury, Lecturer, Department of Business Administration, International Islamic University Chittagong, Bangladesh ** Mohammad Toufiqur Rahman,Lecturer , Department of Business Administration, International Islamic University Chittagong, Bangladesh *** Mohammad Rokibul Kabir, Assistant Professor, Department of Business Administration, International Islamic University Chittagong, Bangladesh Abstract: Waiting lines and service systems are important parts of the business world. In this article we describe several common queuing situations and present mathematical models for analyzing waiting lines following certain assumptions. Those assumptions are that (1) arrivals come from an infinite or very large population, (2) arrivals are Poisson distributed, (3) arrivals are treated on a FIFO basis and do not balk or renege, (4) service times follow the negative exponential distribution or are constant, and (5) the average service rate is faster than the average arrival rate. The model illustrated in this Bank for customers on a level with service is the multiple-channel queuing model with Poisson Arrival and Exponential Service Times (M/M/S). After a series of operating characteristics are computed, total expected costs are studied, total costs is the sum of the cost of providing service plus the cost of waiting time. Finally we find the total minimum expected cost. Keywords: Service; FIFO; M/M/s; Poisson distribution; Queue; Service cost; Utilization factor; Waiting cost; Waiting time, optimization. History: Queuing theory had its beginning in the research work of a Danish engineer named A.K. Erlang. In 1909 Erland experimented with fluctuating demand in telephone traffic. Eight years letter he published a report addressing the delays in automatic dialling equipment. At the end of World War II, Erlang’s early work was extended to more general problems and to business applications of waiting lines.
I. Introudction:
The study of waiting lines, called queuing theory, is one of the oldest and most widely used quantitative analysis techniques. Waiting lines are an everyday occurrence, affective people shopping for groceries buying gasoline, making a bank deposit, or waiting on the telephone for the first available airline reservationists to answer. Queues, another term for waiting lines, may also take the form of machines waiting to be repaired, trucks in line to be unloaded, or airplanes lined up on a runway waiting for permission to take off. The three basic components of a queuing process are arrivals, the actual waiting line and service facilities.
II. Characteristics of a Queuing System:
We take a look at the three part of queuing system (1) the arrival or inputs to the system (sometimes referred to as the calling population), (2) the queue# or the waiting line itself, and (3) the service facility. These three components have certain characteristics that must be examined before mathematical queuing models can be developed. Arrival Characteristics The input source that generates arrivals or customers for the service system ahs three major characteristics. It is important to consider the size of the calling population, the pattern of arrivals at the queuing system, and the behavior of the arrivals.
Size of the Calling Population: Population sizes are considered to be either unlimited (essential infinite) or limited (finite). When the number of or arrivals on hand at any customers given moment is just a small portion of potential arrivals, the calling population is considered unlimited. For practical purpose, in our examples the limited customers arriving at the bank for deposit cash. Most queuing models assume such an infinite calling
Solving Of Waiting Lines Models In The Bank Using Queuing Theory Model The Practice Case:
www.iosrjournals.org 23 | Page
population. When this is not the case, modelling becomes much more complex. An example of a finite
population is a shop with only eight machines that might deposit cash break down and require service.
* The word queue is pronounced like the letter Q, that is, “kew”
Pattern of arrivals at the system: Customers either arrive at a service facility according to some known
schedule customers or else they arrive randomly. Arrivals are considered random when they are independent of
one another and their occurrence cannot be predicted exactly. Frequently in queuing problems, the number of
arrivals per unit of time can be estimated by a probability distribution known as the Poisson distribution., For
any given arrival rate, such as two passengers per hour, or four airplanes per minute, a discrete, Poisson
distribution can be established by using the formula :
t
n
e
n
t
P n t
!
; for n = 0,1,2,3,4,......
Where
P (n;t) = probability of n arrivals
= average arrival rate
e = 2.18
n = number of arrivals per unit of time
Behavior of the Arrival: Most queuing models assume that an arriving passenger is a patient traveler. Patient
customer is people or machines that wait in the queue until they are served and do not switch between lines.
Unfortunately, life and quantitative analysis are complicated by the fact that people have been known to balk or
renege. Balking refers to customers who refuse to join the waiting lines because it is to suit their needs or
interests. Reneging customers are those who enter the queue but then become impatient and leave the need for
queuing theory and waiting line analysis. How many times have you seen a shopper with a basket full of
groceries, including perishables such as milk, frozen food, or meats, simply abandon the shopping cart before
checking out because the line was too long? This expensive occurrence for the store makes managers acutely
aware of the importance of service level decisions.
Waiting Line characteristics
Queue: The waiting line itself is the second component of a queuing system. The length of a line can be either
limited or unlimited. A queue is limited when it cannot, by law of physical restrictions, increase to an infinite
length. Analytic queuing models are treated in this article under an assumption of unlimited queue length. A
queue is unlimited when its size is unrestricted, as in the case of the tollbooth serving arriving automobiles.
Queue discipline: A second waiting line characteristic deals with queue discipline. The refers to the rule by
which passengers in the line are to receive service., Most systems use a queue discipline known as the first in,
first out rule (FIFO). This is obviously not appropriate in all service system, especially those dealing with
emergencies.
In most large companies, when computer-produced pay checks are due out on a specific date, the payroll
program has highest priority over other runs.
Service Facility Characteristics
The third part of any queuing system is the service facility. It is important to examine two basic properties: (1)
the configuration of the service system and (2) the pattern of service times.
Basic Queuing System Configurations: Service systems are usually classified in terms of their number of
channels, or number of servers, and number of phases, or number of service stops, that must be made.
The term FIFS (first in, first served) is often used in place of FIFO. Another discipline, LIFS (last in,
first served), is common when material is stacked or piled and the items on top are used first.
A single-channel system, with one server, is typified by the drive in bank that has only one open teller.
If, on the other hand, the bank had several tellers on duty and each customer waited in one common line for the
first available teller, we would have a multi-channel system at work. Many banks today are multi-channel
service systems, as are most large barbershops and many airline ticket counters.
A single-phase system is one in which the customer receives service from only one station and then exits the
system. Multiphase implies two or more stops before leaving the system.
Service Time Distribution: Service patterns are like arrival patterns in that they can be either constant or
random. If service time is constant, it takes the same amount of time to take Care of each customer. More often,
service times are randomly distributed in many cases it can be assumed that random service times are described
Solving Of Waiting Lines Models In The Bank Using Queuing Theory Model The Practice Case:
www.iosrjournals.org 24 | Page
by the negative exponential probability distribution. This is a mathematically convenient assumption if arrival
rates are Poisson distributed.
The exponential distribution is important to the process of building mathematical queuing models because many
of the models‟ theoretical underpinning are based on the assumption of Poisson arrivals and exponential
services. Before they are applied, however, the quantitative analyst can and should observe, collect, and pilot
service time data to determine if they fit the exponential distribution.
III. Mathematical Models:
3.1 Single-Channel Queuing Model with Poisson Arrivals and Exponential service times (M/M/1):
We present an analytical approach to determine important measures of performance in a typical service system.
After these numerical measures have been computed, it will be possible to add in cost data and begin to make
decisions that balance desirable service levels with waiting line service costs.
Assumptions of the Model
The single-channel, single-phase model considered here is one of the most widely used and simplest queuing
models. It invo
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