OPERATION RESEARCH FOR BEGINNERS.pdf

Operation Research for
Beginners
By
Girish Bhimani
Tushar Bhatt
Disha Rank
Jalpa Rank
Self-Published
Publisher’s Address:
Department of Statistics
Saurashtra University
Rajkot-360005
Printer’s Details:
Parshva Printery
Rajkot-360005
ISBN: 978-93-5526-378-0
Copyright @ Rank Disha

PREFACE
The purpose of the book is to present the current techniques of operations research in such
a way that they can be readily comprehended by the average business student taking an
introductory course in operations research. Several OR teachers and teachers from management
schools suggested that we should bring out a separate volume on OR with a view to meet the
requirements of OR courses, which can also be used by the practising managers. The book can be
used for one semester/term introductory course in operations research. Instructors may like to
decide the appropriate sequencing of major topics covered.
This book will be useful to the students of management, OR, industrial and production
engineering, computer sciences, chartered and cost-accountancy, economics and commerce. The
approach taken here is to illustrate the practical use of OR techniques and therefore, at places
complicated mathematical proofs have been avoided. To enhance the understanding of the
application of OR techniques, illustrations have been drawn from real life situations. The problems
given at the end of each chapter have been designed to strengthen the student's understanding of
the subject matter. Our long teaching experience indicates that an individual's comprehension of
the various quantitative methods is improved immeasurably by working through and
understanding the solutions to the problems.
It is not possible for us to thank individually all those who have contributed to the case
histories. Our colleagues and many people have contributed to these studies and we gratefully
acknowledge their help. Without their support and cooperation this book could not have been
brought out. Our special thanks are due to Dr. K. H. Atkotiya who have assisted me in editing the
case studies. we wish to express my sincere thanks to Mr. Chandraprakash Shah making available
all facilities needed for this job. We express my gratitude to my parents who have been a constant
source of Inspiration.
We Strongly believe that the road to improvement is never-ending. Suggestions and
criticism of the books will be very much appreciated and most gratefully acknowledged.
Girish Bhimani
Tushar Bhatt
Disha Rank
Jalpa Rank

Index
Module – 1: Fundamental of Operations Research.....……………………………………………….......1
1.1 Historical background of O.R. ……………………………… 3
1.2 Definition and Meaning of O.R ……………………………… 3
1.3 Features of O.R ……………………………… 3
1.4 Modeling in O.R ……………………………… 4
1.5 Types of Models ……………………………… 5
1.6 General solution methods for O.R. models ……………………………… 5
1.7 Advantages and Limitations of O.R / O.R Models ……………………………… 6
1.8 Multiple Choice Questions – I ……………………………… 6
1.9 Review Questions – I ……………………………… 7
1.10 Linear Programming ……………………………… 7
1.11 Components of Linear Programming Problem ……………………………… 8
1.12 Basic Assumption in LPP [Properties of LP - models] ……………………………… 8
1.13 Different Types of LPP’s ……………………………… 9
1.14 Steps for Mathematical Formulation of LPP’s ……………………………… 9
1.15 Production Allocation Problem ……………………………… 10
1.16 Multiple Choice Questions – II ……………………………… 12
1.17 Review Questions – II ……………………………… 13
1.18 LPP - Graphical Solution and Extension ……………………………… 14
1.19 Standard Form of LPP ……………………………… 22
1.20 Some Basic Definitions ……………………………… 22
1.21 Simplex Method for solving LPP ……………………………… 23
1.22 Steps for solving L.P.P by Simplex method ……………………………… 26
1.23 Review Exercise ……………………………… 31
Module – 2: Transportation Problem…………………………………………………………………………...32
2.1 Introduction ……………………………… 34
2.2 Structure of Transportation Problem ……………………………… 34
2.3 Types of Transportation Problems ……………………………… 35
2.4 The methods for solving Transportation Problems ……………………………… 35
2.5 North – West Corner Method (NWCM) ……………………………… 35
2.6 Examples for Balanced T.P’s ……………………………… 36
2.7 Least – Cost Method (LCM) ……………………………… 42
2.8 Vogel’s Approximation Method (VAM) ……………………………… 49
2.9 Optimal solution: MODI Method – UV Method ……………………………… 53
2.10 Degeneracy in Transportation Problem ……………………………… 61
2.11 Exercise ……………………………… 65

Index
Module – 3: Assignment Problem…………………………………………………………………………………66
3.1 Introduction ……………………………… 67
3.2 Structure of assignment problem ……………………………… 67
3.3 Hungarian Method for solving AP ……………………………… 68
3.4 Exercise ……………………………… 82
Module – 4: Assignment Problem…………………………………………………………………………………83
4.1 Introduction ……………………………… 84
4.2 Failure Mechanism of Items ……………………………… 85
4.3 Costs to be considered ……………………………… 86
4.4 When The Replacement Is Justified? ……………………………… 86
4.5 Replacement model ……………………………… 86
4.6 Replacement policy ……………………………… 87
4.7 Replacement of items whose maintenance cost
increases with time and the money value changes at a
constant rate
……………………………… 97
4.8 Exercise ……………………………… 103
Module – 5: Network Analysis……………………………………………………………………………………..104
5.1 Introduction ……………………………… 105
5.2 Phases of CPM and PERT ……………………………… 105
5.3 Some important definitions ……………………………… 106
5.4 Project management or representation by a network
diagram
………………………………
107
5.5 Types of activities ……………………………… 107
5.6 Types of events ……………………………… 108
5.7 Common Errors ……………………………… 109
5.8 Rules of network construction ……………………………… 110
5.9 Numbering the events ……………………………… 110
5.10 Time analysis ……………………………… 110
5.11 Determination of Floats and Slack times ……………………………… 111
5.12 Critical activity and Critical path ……………………………… 112
5.13 Project Evaluation and Review Technique (PERT) ……………………………… 121
5.14 Exercise ……………………………… 127
Module – 6: Network Analysis……………………………………………………………………………………..128
6.1 Introduction ……………………………… 129
6.2 Properties of Game ……………………………… 130
6.3 Characteristics of Game Theory ……………………………… 130
6.4 Classification of Games ……………………………… 132
6.5 The MaxiMin-MiniMax Principle ……………………………… 133
6.6 Two-Person and Zero-Sum Game ……………………………… 134
6.7 Exercise ……………………………… 143

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In a sense, every effort to apply science to management of organized systems, and to their
understanding, was a predecessor of operations research. It began as a separate discipline,
however, in 1937 in Britain as a result of the initiative of A.P. Rowe, superintendent of the
Bawdsey Research Station, who led British scientists to teach military leaders how to use
the then newly developed radar to locate enemy aircraft. By 1939 the Royal Air
Force formally commenced efforts to extend the range of radar equipment so as to increase
the time between the first warning provided by radar and the attack by enemy aircraft. At
first they analyzed physical equipment and communication networks, but later they
examined behaviour of the operating personnel and relevant executives. Results of the
studies revealed ways of improving the operators’ techniques and also revealed
unappreciated limitations in the network.
Similar developments took place in the British Army and the Royal Navy, and in both cases
radar again was the instigator. In the army, use of operations research had grown out of the
initial inability to use radar effectively in controlling the fire of antiaircraft weapons. Since
the traditional way of testing equipment did not seem to apply to radar gunsights,
scientists found it necessary to test in the field under operating conditions, and the
distinguished British physicist and future Nobel Laureate P.M.S. Blackett organized a team
to solve the antiaircraft problem. Blackett’s Antiaircraft Command Research Group
included two physiologists, two mathematical physicists, an astrophysicist, an army officer,
a former surveyor, and subsequently a third physiologist, a general physicist, and two
mathematicians.
By 1942 formal operations research groups had been established in all three of Britain’s
military services.Development of operations research paralleling that in Britain took place
in Australia, Canada, France, and, most significantly for future developments, in the United
States, which was the beneficiary of a number of contacts with British researchers. Sir
Robert Watson-Watt, who with A.P. Rowe launched the first two operational studies of
radar in 1937 and who claims to have given the discipline its name, visited the United
States in 1942 and urged that operations research be introduced into the War and Navy
departments. Reports of the British work had already been sent from London by American
observers, and James B. Conant, then chairman of the National Defense Research
Committee, had become aware of operations research during a visit to England in the latter
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Fundamental of Operations Research

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half of 1940. Another stimulant was Blackett’s memorandum, “Scientists at the Operational
Level,” of December 1941, which was widely circulated in the U.S. service departments.
The first organized operations research activity in the United States began in 1942 in
the Naval Ordnance Laboratory. This group, which dealt with mine warfare problems, was
later transferred to the Navy Department, from which it designed the aircraft mining
blockade of the Inland Sea of Japan.
As in Britain, radar stimulated developments in the U.S. Air Force. In October 1942, all Air
Force commands were urged to include operations research groups in their staffs. By the
end of World War II there were 26 such groups in the Air Force. In 1943 Gen. George
Marshall suggested to all theatre commanders that they form teams to study amphibious
and ground operations.
At the end of World War II a number of British operations research workers moved to
government and industry. Nationalization of several British industries was an important
factor. One of the first industrial groups was established at the National Coal Board.
Electricity and transport, both nationalized industries, began to use operations research
shortly thereafter. Parts of the private sector began to follow suit, particularly in those
industries with cooperative research associations; for example, in the British Iron and Steel
Research Association.
The early development of industrial operations research was cautious, and for some years
most industrial groups were quite small. In the late 1950s, largely stimulated by
developments in the United States, the development of industrial operations research in
Britain was greatly accelerated.
Although in the United States military research increased at the end of the war, and groups
were expanded, it was not until the early 1950s that American industry began to take
operations research seriously. The advent of the computer brought an awareness of a host
of broad system problems and the potentiality for solving them, and within the decade
about half the large corporations in the United States began to use operations research.
Elsewhere the technique also spread through industry.
Societies were organized, beginning with the Operational Research Club of Britain, formed
in 1948, which in 1954 became the Operational Research Society. The Operations Research
Society in America was formed in 1952. Many other national societies appeared; the first
international conference on operations research was held at Oxford University in 1957. In
1959 an International Federation of Operational Research Societies was formed.

Module – 1: Fundamental of Operations Research
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1.1 Introduction
The term O.R. was first coined in 1940 by McClosky and Trefthen in a small town, Bowdsey
of the United Kingdom. This new science came into existence as a result of research on
military operations during World War II.
During the war there were strategic and tactical problems which were greatly complicated
to expect adequate solutions from individuals or specialists were unrealistic. Therefore the
military management called on scientists from various disciplines and organized them into
teams to assist in solving strategic and tactical problems, i.e. to discuss, evolve and suggest
ways and deliberations, they suggested certain approaches that showed remarkable
progress. This new approach to systematic and scientific study of the operations of the
system was called the operations research or operational research.
It was only in the early 1950’s that the industries in U.S.A realized the importance of this
new science in solving their management problems. Since then industrial O.R. developed
rapidly in U.S.A as compared to U.K.
Throughout the module we use abbreviation O.R for operations research.
1.2 Definition and Meaning of O.R
O.R. is a scientific approach to problem solving for executive management. –H.M. Wagner
, in which the operation means identify problems and in
scientific way to represent it and research means inventing some new ideas techniques,
models for solving desired problems.
O.R. is the application of the methods of science to solve complex problems arising in the
management of large systems of man, machine, and materials in industry.
It measures the factors such as risk, chances, comparisons, controlling and decision-making
extra. It is experimental.
1.3 Features of O.R
1. Systematic approach: Apply scientific methods for the purpose of solving
problems. It is a formalized process of reasoning.
2. Objectivity: It is attempts to locate the best or optimal solution to the problem
under consideration.

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3. Decision making: Primarily O.R. is addressed to managerial decision – making or
problem solving. A major premise of O.R. is that decision – making, irrespective of
the situation involved, can be considered as a general systematic process.
4. Inter-disciplinary team approach: O.R. is inter-disciplinary in nature and requires
a team approach to a solution of the problem. Managerial problems have economic,
physical, psychological, biological, sociological and engineering aspects. This
requires a blend of people with expertise in the areas of mathematics, statistics,
engineering, economics, management, computer science and so on.
5. Digital computer: Use of a digital computer has become an integral part of the O.R.
approach to decision making. The computer may be required due to complexity of
the model.
1.4 Modeling in O.R
A model in O.R. is a simplified representation of an operation or a process in which only the
basic aspects or the most important features of a typical problem under investigation are
considered. Models, in general cannot represent every aspect of reality because of
innumerable and changing characteristics of the real life problems to be represented.
Instead, they are limited approximations of the reality.
Following are the main characteristics that a good model for O.R.
1. A good model should be capable of taking into account new formulation without
having any significant change in its frame.
2. Assumptions made in the model should be as small as possible.
3. It should be simple and coherent. Number of variables used should be less.
4. It should be open to parametric type of treatment.
5. It should not take much time in its construction for any problem.
The models are developed by using following methods:
1. Function
2. Predictive
3. Problem structure
4. Nature of the environment
5. Extent of generality

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1.5 Types of Models
Based on the significance of the problem and its nature suitable model can be selected. If
the problem is trivial and routine any one the verbal, schematic or iconic models can be
chosen. If the outcome of the decision is not significant to the organization, opinion or
judgmental methods can be used to make a decision. Mainly there are four types of models
can be used in decision making in O.R. as follows:
1.6 General solution methods for O.R. models
1. Analytical methods: In these methods classical optimization techniques such as
calculus, finite differences and etc. are used for solving an O.R. model. The kind of
mathematics required depends upon the nature of the model.
2. Numerical methods: Numerical methods are concerned with the iterative or trial
and error procedures, through the use of numerical computation at each step. These
numerical methods are used when some analytical methods fail to derive the
solution.
3. Monte Carlo methods: These involve the use of probability and sampling concepts.
The various steps associated with a Monte Carlo method are as follows:
3.1 For appropriate model of the system, make sample observations and determine
the probability distribution for the variables of interest.
Models
Verbal Physical
(Iconic)
Schematic Mathematical
Verbal Scale Charts & Diagrams Equations

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3.2 Convert the probability distribution to cumulative distribution.
3.3 Select the sequence of random numbers with the help of random tables.
3.4 Determine the sequence of values of variables of interest with the sequence of
random numbers obtained in the above step.
3.5 Fit an appropriate standard mathematical function to the values obtained in
step-3.4.
The Monte Carlo method is essentially a simulation technique in which statistical
distribution functions are created by generating a series of random numbers.
1.7 Advantages and Limitations of O.R / O.R Models
Models in O.R. are used as an aid for analyzing complex problems. The main advantages of a
model are:
1. Through a model, the problem under consideration becomes controllable.
2. It provides some logical and systematic approach to the problem.
3. It indicates the limitations and scope of an activity.
4. It helps in finding avenues for new research and improvements in a system.
However besides the above advantages a model has the following limitations:
1. Models are only an attempt in understanding operations and should never be
considered as absolute in any sense.
2. Validity of any model with regard to corresponding operation can only be verified
by carrying out the experiment and observing relevant data characteristics.
3. Construction of models requires experts from various disciplines.
1.8 Multiple Choice Questions – I
1. Operation research came into existence _____.
(a) In the year 1940
(b) In the military context
(c) During World War I
(d) During World War II
2. Operation research is _____.
(a) Applied decision theory
(b) A scientific approach to problems solving for executive management
(c) The science of use
(d) all of the above

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3. Operation research approach is _____.
(a) intuitive
(b) Objective
(c) Multi-disciplinary
4. A model in Operation research is _____.
(a) An approximation
(b) An idealization
(c) An essence of reality
5. A model in Operations Research is _________
(a) An approximation
(b) An idealization
(c) An essence of reality
(d)All of the above
6. A physical model is an example of __________
(a)Ironic model
(b) Analogue model
(c) Verbal model
(d) Symbolic model
7. The scientific method in Operations Research consists of __________.
(a) Judgment Phase
(b) Research Phase
(c) Action Phase
(d)All of the above
1.9 Review Questions – I
1. What is Operation Research?
2. State the features of O.R.
3. Explain in detail the modeling in O.R.
4. Explain the advantages and limitations of O.R.
5. Explain in detail: General solution methods for O.R. models.
1.10 Linear Programming
 In O.R. our main focus to obtain an optimal solution of any problem, the techniques
for obtaining an optimal solution will be known as optimization. Optimization is the
act of obtaining the best result under given circumstances.

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 Optimization can be defined as the process of finding the conditions that give
maximum or minimum value of a function.
 Any problem that requires a positive decision to be made can be classified as an
operation research (OR) type problem and solutions can be made according to them
as known as Optimization Techniques.
 The linear programming is one of the best tool or technique of OR.
 George B. Dantzig is generally recognized as the pioneer of linear programming.
1.11 Components of Linear Programming Problem
A linear programming problem (LPP) consists of three components namely,
 Decision Variables / Activities: It refers to the activities that are competing one
another for sharing the resources available. These variables are usually inter-related
in terms of utilization of resources and need simultaneous solutions. All the decision
variables are considered as continuous, controllable and non-negative.
 The Objective / Goal: A linear programming problem must have an objective which
should be clearly identifiable and measurable in quantitative terms. It could be of
profit (sales) maximization, cost (time) minimization and so on. The relationship
among the variables representing objective must be linear.
 The Constraints / Restrictions: There are always certain limitations or
constraints on the use of resources, such as labor, space, raw material, money and
many more that limit the degree to which an objective can be achieved.
1.12 Basic Assumption in LPP [Properties of LP - models]
The following four basic assumptions are necessary for all linear programming problems:
 Certainty: In all LPP’s, it is assumed that all the parameters such as availability of
resources, profit (or cost) contribution of a unit of decision variable and
consumption of resources by a unit decision variable must be known and fixed.
 Divisibility / Continuity: This implies that solution values of the decision variables
can take only non-negative values, including fraction values.

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 Proportionality: This requires the contribution of each decision variable in both
the objective function and the constraints to be directly proportional to the value of
the variable.
 Additivity: The value of the objective function for the given values of decision
variables and the total sum of resources used, must be equal to the sum of the
contributions (profit or cost) earned from each decision variable and the sum of the
resources used by each decision variable respectively.
1.13 Different Types of LPP’s
 Manufacturing Problems: Determine the number of units that should be produced
and sold in order to maximize profit, when each product requires a fixed manpower,
machine hours and raw materials.
 Diet Problems: Determine the amount of different kinds of to be included in the
diet, minimizing the cost and subject to the availability of food and their prices.
 Transportation Problems: Determine a transportation schedule to find the
cheapest way of transporting a product from plants or factories situated at different
location to different markets.
1.14 Steps for Mathematical Formulation of LPP’s
 Step – 1: To identify the decision variables from the problem.
 Step – 2: To construct the objective function as a linear combination of the
decision variables.
 Note: Let are some decision variables and are some
constants the is called the linear combination of
and .
 Step – 3: To identify the constraints of the problem such as resources limitations,
Inter-relationship between variables etc. Formulae of these constraints as linear
equations or inequations in terms of non-negative decision variables.
Thus, LPP is a collection of objective function, the set of constraints and the set of the non –
negative constraints.

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1.15 Production Allocation Problem
Ex-1: A manufacturer produce two types of models M and N each M model requires 4 hours
grinding and 2 hours for polishing whereas each N model requires 2 hours of grinding and
5 hours for polishing. The manufacturer has 2 grinders and 3 polishers. Each grinder works
for 40 hrs a week and each polisher’s works for 60 hrs a week. Profit on an M model is 3 RS.
and on an N model is 4 Rs. whatever is produced in a week sold in the market. How should
the manufacturers allocate this production capacity to the two types of models? So that he
may make the maximum profit in a week?
Solution:
Step – 1: Here first we identify the decision variables
Step – 2: Construct the Objective Function
Maximize
Step – 3: Identify the Constraints
Subject to constraints
And
Ex-2: A firm manufacturers 3 products A, B and C. The profits are 3 Rs, 2Rs, and 4 Rs,
respectively. The firm has two machines and below is the required time in minutes
for each machine on each product.
Products
A B C
4 3 5
2 2 4

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Machines M1 and M2 have 2000 and 2500 machine minutes respectively. The firm must
manufacture 100A’s, 200B’s and 50C’s but not more than 150A’s. Set up an LPP to
maximize profit.
Solution:
Maximize
And
Ex-3: A mine company has two different mines X and Y that produce an ore which after
being crushed, is graded into three classes: high medium and low grade. The company has
contracted to provide a smelting plant with 12 tons of high grade, 8-tons of medium grade
and 24-tons of low grade ore per week. The two mines have different operating
characteristic as detailed below:
Mine Cost/day
(Rs’ 000)
Production(Tons/day)
High Medium Low
X 180 6 3 4
Y 160 1 1 6
How many days per week should each mine be operated to fulfils the smelting plant
contract? For minimizing the cost of product.
Solution:

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Maximize
Days per week constraints we cannot work more than a certain maximum number of days
a week consider 5 working days in a week therefore and It is obvious
1.16 Multiple Choice Questions – II
8. Linear programming problem (LPP) must have _____.
(a) Objective that we aim to maximize or minimize.
(b) Constraints that we need to specify.
(c) Decision variables that we need to determine.
(d) All of the above
9. Which of the following is not correct about LPP?
(a) All constraints must be linear relationships.
(b) Objective function must be linear
(c) All the constraints and decision variables must be of either or type
(d) All decision variables must be non – negative
10. Which of the following is not associated with an LPP?
(a) Proportionality
(b) Uncertainty
(c) Additivity
(d) Divisibility
11. Which of the following is correct?
(a) Linear programming takes into consideration the effect of time and uncertainty
(b) An LPP can have only two decision variables
(c) Decision variables in an LPP may be more or less than the number of
constraints
(d) Linear programming deals with problems involving only a single objective

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12. A Constraint in an LPP is expressed as _____
(a) An equation with sign
(b) Inequality with sign
(c) Inequality with sign
(d)Any of the above
1.17 Review Questions – II
1. Discuss about the components of Linear Programming Problem.
2. What is Linear Programming Problem? Discuss about the basic assumptions in LPP.
1.18 LPP - Graphical Solution and Extension
Linear programming problems involving two decision variables can be easily solved by
graphical method. If the problem has more than two variables, graphical method is either
impossible or impractical. Graphical solution to a two variables LP problem can be
obtained using following steps:
Step – 1: To convert all constraints into equations
Step – 2: To plot the graph of equations and apply the conditions given in the constraints.
Step – 3: To identify feasible region (that satisfies all the conditions) and find it’s corners
(extreme points).
Step – 4: To test the maximum / minimum value of objective function for these corners.
Ex – 1: Solve the following LP problem by Graphical method.
Maximize
Subject to
And .
Solution:
Step – 1: Convert all constraints into equations

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Decision Variables Points
0 5
3 0
------ (2)
0 2
5 0
Step – 2: To plot the graph of equations and apply the conditions given in the
constraints.
(5, 0)
0(0, 0) 1 2 3 4 5
1
2
3
4
5
B(2, 0)
A (0, 3)
(0, 5)
Feasible Region

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Consider equation ------ (1) – equation ------ (2) we get
Therefore put in equation ------ (1) we get,
Therefore
Now the feasible region is: OACBO
Feasible Region
Points
Co-ordinates Maximum
A (0, 3) 9
12.36
B
(2, 0) 10
C 12.36
O 0

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Therefore the point follows Maximize , and hence and are the
required solutions.
Maximize
Subject to
And .
Solution:
------ (1)
0 50
100 0
------ (2)
0 80
80 0
Consider equation ------ (1) – equation ------ (2) we get,
------ (1)
------ (2)
_____________________________________________________________________
Put in equation ------ (2) we get .
.

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Now the feasible region is: OBCEO
A (0, 80)
B (0, 50)
C (20, 60)
D (100, 0)
E (80, 0)
Feasible Region
0(0, 0) 20 40 60 80 100
20
40
60
80
100

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Feasible Region
Points
Co-ordinates Maximum
O
(0, 0)
0
4000
B
(0, 50) 900
C (20, 60) 2080
E 4000
Therefore the point follows Maximize , and hence and are the
required solutions.
Minimize
Subject to: , , , and .
Solution:
…… (1)
0 -10
0
…… (2)
0 6
6 0
…… (3)
0 2
-2 0
Now next we find the point of intersection of lines form equations (1) and (2) as well as
equations (2) and (3) as follows:
…… (1)

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…… (2)
put in equation (2) we get
Therefore the point of intersection is
…… (2)
…… (3)
put in equation (2) we get
Therefore the point of intersection is

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2 4 6 8 10
-10 -8 -6 -4 -2
-2
-4
-6
-8
-10
O
2
4
6
8
10
Feasible Region
A (0, 3.3)
B (-10, 0)
(1)
C (0, 6)
D (6, 0)
(2)
E (0, -2)
(3)
F (2, 0)
G (2, 4)
H (4, 2)

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Now the feasible region is: OBCEO
Feasible Region
Points
Co-ordinates Minimum
O
(0, 0)
0
-6
A
(0, 3.3) 6.6
G (2, 4) 6
H (4, 2) 0
D (6, 0) -6
F (2, 0) -2
Therefore the point follows Minimize , and hence and are the
required solutions.
Ex-4: A company manufactures two types of boxes, corrugated and ordinary cartons. The
boxes undergo two major processes: cutting and pinning operations. The profits per unit
are Rs. 6 and Rs. 4 respectively. Each corrugated box requires 2 minutes for cutting and 3
minutes for pinning operation, whereas each carton box requires 2 minutes for cutting and
1 minute for pinning. The available operating time is 120 minutes and 60 minutes for
cutting and pinning machines. Determine the optimum quantities of the two boxes to
maximize the profits using graphical method.
Answer:
Maximize
Subject to
And .
Maximum with
Solution: H.W.

22 | P a g e
1.19 Standard Form of LPP
In order to get the general solution of the LPP id first put into a common format known as
standard form. The standard form should be including the following properties:
1. All Constraints should be converted into equations with non – negative RHS.
2. All the variables should be non – negative.
3. The objective function is to maximize or to minimize.
Converting Constraints into equations
(1) A constraint of the type is converted into an equation by adding a slack variable
to the LHS of the constraint.
For example, in the constraint,
Add slack variable to the LHS hence we get
(2) A constraint of the type is converted into an equation by subtracting a surplus
variable to the LHS of the constraint
For example, in the constraint,
Add slack variable to the LHS hence we get
1.20 Some Basic Definitions
1. Basic Feasible Solution: A basic feasible solution is a basic solution which also
satisfies the non-negative that is, all basic variables are non- negative.
2. Slack Variable: If constraints have sign, then in order to make it an equality we
have to add something positive to the LHS is called a Slack variable.

23 | P a g e
3. Surplus Variable: If constraints have sign, then in order to make it an equality we
have to subtract something positive to the LHS is called a Surplus variable.
1.21 Simplex Method for solving LPP
Simplex Method: The Simplex method is computational procedure - an algorithm for
solving linear programming problems. In the simplex process, we must first find an initial
basis solution (extreme point). We then proceed to an adjacent extreme point. We continue
moving from point to point until we reach an optimal solution.
For a maximization problem, the Simplex method always moves in the direction of
steepest ascent, thus ensuring that value of the objective function improves with each
solution.
Advantages for solving LPP by simplex method: The Simplex method was an invention
of Dr. George Dantzig in 1947, a replacement for other methods of solving linear
programming problems. It effectively replaced them due to its power and efficiency.
1. For complex problems involving many variables, the Simplex method is much
faster than other algorithms at solving linear systems. The Simplex method's
efficiency is important for computer programming, as the need for processing
power is significantly lower when using it.
2. If more than three variables are in the problem, graphical methods will fail, as
dimensions over 3 cannot be visualized using them. The Simplex method can
apply where graphical methods can't.
3. The Simplex method necessitates taking a set of vertices and testing them with
adjacent vertices, until none are left to test. In the method you use two states.
Either the function improves or remains unchanged. Any other change is ignored.
4. The Simplex method necessitates taking a set of vertices and testing them with
adjacent vertices, until none are left to test. Either the function improves or remains
unchanged.
5. If a system is comprised of entities whose behavior can be modeled with a linear
function, you can employ the Simplex method. Systems appropriate for the
Simplex method include numerous applications in economics, such as optimizing
the price given supply and demand, or in science, monitoring predators and prey
in a given environment.

24 | P a g e
Limitations of Simplex Method for solving LPP
1. Simplex method Involves understanding of many conceptual technical aspects. These
cannot be understood by any manager not conversant with the subject.
2. Linear programming problems need lot of expertise, time and are cumbersome. A
number of steps have to be adopted to proceed in a systematic manner before one can
arrive at the solution.
3. Graphic solution method has lot of applications and is relatively short and simple.
However, it has limitations and cannot be applied to problems with more than two
variables in the objective function.
4. Simplex method of LPP can be applied to problems with more than two variables in the
objective function; the procedure adopted is complicated and long. It may need
computation of 4 to 5 simplex tables and can test the patience of the problem solver.
Computers are of course helpful in such cases.
5. LPP does not lead to ‘a unique’ optimal solution. It can provide different types of
solutions like feasible solution, infeasible solution, unbounded solution, degenerate
solution etc.
6. It gives absurd or impractical results in many solutions. The solution may ask for
providing men or 3· 89 machines which is not possible.
7. LPP model makes many assumptions in the values of objective function and constraint
variables, like the rate of profit. In fact, such assumptions may not be right.
8. The whole approach to the solution is based on the linearity of the functions i.e., all the
variables involved in the problem increase or decrease in a linear manner. This assumption
does not hold well in all cases. In many cases, the objective function may assume the form
of a quadratic equation.
LPP method cannot be used where a number of objectives are required to be fulfilled. It
deals with other maximizing of profits, minimization of costs etc.

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Algorithm for solving LPP by Simplex method:
No No
 Convert LPP into Standard form
 Decide Coefficient of these variables in the Objective function
Setup initial Simplex table to obtain initial
solution
Compute and
values
Is LPP of
Max or
Min type?
Maximization
Minimization
Do
positive
value
exist?
Do
negative
value
exist?
This solution is
optimal
Yes
Select key column
with largest negative
value
Select key row with
If all then current solution is unbounded and stops the procedure.
Yes
Select key column
with largest
positive
value
Identity key element at the intersection of key row and key column
Update the entries in the Simplex table by
(a) 1st obtaining key row values, and
(b) Apply elementary row operations

26 | P a g e
1.22 Steps for solving L.P.P by Simplex method
 Step – 1: To convert given LPP into standard form
 Step – 2: To Make an initial simplex table
 Step – 3: To calculate and .
 Step – 4: To find maximum value from .
 Step – 5: The corresponding column to max. Value denotes as key
column and similarly find and it is denote as key
row. The intersection point of key row and key column mark as key element.
 Step – 6: To replace key row by key column and make zero entry to up
side and down side of the key element. Here observe that the key
element is must be one.
 Step – 7: To be continuing above process until the difference is either
zero or negative values. Hence we get the optimum solution.
Ex- 1: Solve the following LPP by simplex method
Maximize
Subject to
And
Solution:
(1) Standard Form of LPP
Maximize
And

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(2) Initial Simplex table
INITIAL TABLE - 0
3 2 0 0
B
0 40 1 1 1 0
0 20 1 -1 0 1
0 0 0 0
3 2 0 0
All is not therefore further revision is required.
Key element = 1
Incoming variable
Outgoing variable
40 1 1 1 0
20 1 -1 0 1
20 0 2 1 -1
MODIFIED TABLE - 1
3 2 0 0
B
0 20 0 2 1 -1
3 20 1 -1 0 1
3 -3 0 3
0 5 0 -3
All is not therefore further revision is required.
Key element , here we must make the key element . Therefore 1st row in above table
is divided by 2.

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Incoming Variable
Outgoing Variable
MODIFIED TABLE - 2
3 2 0 0
B
2 10 0 1
3 30 1 0
3 2
0 0 0
In the above table all therefore there is no need for revision and the values of
basic variables are our required solution.
And
And hence maximize .
Ex – 2: Solve the following LPP by Simplex method:
Minimize
Subject to
And .
Solution: Here we are convert first, the given LPP into maximize Z and all numerical values
of right hand side of constraints are non-negative. So we get

29 | P a g e
Maximize
Subject to
And
Standard Form
Maximize
Subject to
And
Initial table - 0
-1 3 -3 0 0 0
B
0 7 3 -1 2 1 0 0 -
0 12 -2 -4 0 0 1 0 -
0 10 -4 3 8 0 0 1
0 0 0 0 0 0
-1 3 -3 0 0 0

30 | P a g e
Incoming variable
Outgoing variable
Key element
Modified table - 1
-1 3 -3 0 0 0
B
0
0 1 0
0
0 0 1
-
3
1 0 0
-
-4 3 8 0 0 1
3 0 -11 0 0 -1
Incoming variable , Outgoing variable , Key element
Modified table - 2
-1 3 -3 0 0 0
B
-1
0 0
0
0 1
-
3
1 0
-
-1 3 0
0 0 0

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According to modified table-2
All then the optimal/optimum solution is
And maximum
Required minimum
1.23 Review Exercise
Ex – 1: Solve the following LPP’s using Simplex Method.
Maximize
Subject to
And .
Answer: and .
Maximize
Subject to
And .
Answer: and .
.
Maximize
Subject to
And .
Answer: and .

32 | P a g e
Transportation theory is a name given to the study of optimal transportation and allocation
of resources. The problem was formalized by the French mathematician Gaspard
Monge(1871). In the 1920s A.N. Tolstoi was one of the first to study the transportation
problem mathematically. In 1930, in the collection Transportation Planning Volume I for
the National Commissariat of Transportation of the Soviet Union, he published a paper
Methods of Finding the Minimal Kilometrage in Cargo-transportation in space.
Tolstoi(1939) illuminated his approach by applications to the transportation of salt,
cement, and other cargo between sources and destinations along the railway network of
the Soviet Union. In particular, a, for that large-scale, instance of the transportation
problem was solved to optimality. Major advances in transportation theory were made in
the field during World War II by the Soviet/Russian
mathematician and economist Leonid Kantorovich Consequently, the problem as it is stated
is sometimes known as the Monge–Kantorovich transportation problem. Kantorovich won
the Nobel prize for economics in 1975 for his work on the optimal allocation of scarce
resources, the only winner of the prestigious award to come from the USSR. F.L. Hitchcock
(1941) worked on the distribution of a production from several sources to numerous
localities.
Koopman also worked on the optimum utilization of transportation system and used model
of transportation, in activity analysis of production and allocation. Charnes and Cooper
(1961) mentioned about transportation in their book – Management Models and Industrial
Applications of Linear Programming.
Followed by Ijiri (1965) who mentioned about transportation problem in his book-
Management Goals and Accounting for Control M. Klein (1967) developed a primal method
for minimal cash flows with applications to the assignment and transportation problems.
Hadley(1972) also included transportation problem in his book: Linear Programming. Lee
(1972) and Ignizio(1976) used goal programming to solve transportation problem.
Mackinnon & James (1975) developed an algorithm for the generalized transportation
2
Funda
menta
l of
Opera
tions
Resea
rch
Transportation Problem

33 | P a g e
problem. Moore et-al performed analysis of a transshipment problem with multiple
conflicting objectives. Kwak (1979) developed a goal programming model for improved
transportation problem solutions, followed by Kvanli (1980). OhEigeartaigh (1982)
developed a fuzzy transportation algorithm Arthur-et-al (1982) worked on the multiple
goal production and logistics planning in a chemical and pharmaceutical company.
Olson (1984) has compared four goal programming algorithm. Goyal(1984) worked on
improving VAM for unbalanced transportation problem.
Kwak & Schniederjans(1985) framed goal programming solutions to transportation
problem with variable supply and demand requirement. R.K. Ahuja (1986) developed an
algorithm for minimax transportation problem. In the same Romero has done a survey of
generalized goal programming also Currin worked on the transportation problem with
inadmissible routes.
Romero (1991) has written a book on critical issues in goal programming, followed by
Tamiz & Jones (1995) who has done a review of goal programming and its applications.
Hemaida & Kwak(1994) developed a linear goal programming model for transshipment
problem with flexible supply and demand constraints. Sharma et-al (1999) analyzed
various applications of multi-objective programming in MS/OR.
Sun (2002) worked on the transportation problem with exclusionary side constraints and
branch and bound algorithm. Schrijver(2002) worked on the history of transportation and
maximum flows. Okunbor(2004) worked on the management decision making for
transportation problems through goal programming.

Module – 2: Transportation Problem
34 | P a g e
2.1 Introduction
The transportation problem is a special type of linear programming problem where the
objective consists in minimizing transportation cost of a given commodity from a number
of sources or origins (e.g. factory, manufacturing facility) to a number of destinations (e.g.
warehouse, store). Each source has a limited supply (i.e. maximum number of products that
can be sent from it) while each destination has a demand to be satisfied (i.e. minimum
number of products that need to be shipped to it). The cost of shipping from a source to a
destination is directly proportional to the number of units shipped.
2.2 Structure of Transportation Problem
Basic Notations:






Sources are represented by rows while destinations are represented by columns. In general,
a transportation problem has rows and columns. The problem is solvable if there
are exactly basic variables.

35 | P a g e
2.3 Types of Transportation Problems
There are two different types of transportation problems based on the initial given
information:
 Balanced Transportation Problems: cases where the total supply is equal to the total
demand. In short .
 Unbalanced Transportation Problems: cases where the total supply is not equal to
the total demand. When the supply is higher than the demand, a dummy destination is
introduced in the equation to make it equal to the supply (with shipping costs of $0);
the excess supply is assumed to go to inventory. On the other hand, when the demand is
higher than the supply, a dummy source is introduced in the equation to make it equal
to the demand (in these cases there is usually a penalty cost associated for not fulfilling
the demand). In short .
In order to proceed with the solution of any given transportation problem, the first step
consists in verifying if it is balanced. If it is not, it must be balanced accordingly.
2.4 The methods for solving Transportation Problems
Here we are having mainly four methods for solving T.P’s as follows:
1. North – West Corner Method (NWCM)
2. Least – Cost Method (LCM)
3. Vogel’s Approximation Method (VAM)
2.5 North – West Corner Method (NWCM)
Consider the following matrix form of T.P

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Source
Destination
Supply
1
2
3
4
Demand
Working Rules: (Balanced T.P)
1. Select the upper left (North – West) cell of the transportation matrix and allocate
minimum of Supply and Demand value in that cell.
2. After allocation the minimum value to the cell, the cell corresponding row or
columns is completely discard, not consider in the further allocation.
3. Now with the new reduced table, again select the North – West cell and allocate the
available minimum values from the demand and supply.
4. Repeat steps (i), (ii) and (iii) until all supply and demand values are becomes zeros.
5. Hence obtain the initial basic feasible solution.
2.6 Examples for Balanced T.P’s
Ex -1: Solve the following T.P using NWCM
Source
Destination
Supply
1 21 16 25 13 11
2 17 18 14 23 13
3 32 27 18 41 19
Demand 6 10 12 15
Solution: Here given T.P is balanced because .

37 | P a g e
TABLE - 1
Source
Destination
Supply
1
21
6
16 25 13 11, 5
2
17 18 14 23 13
3
32 27 18 41 19
Demand 6, 0 10 12 15
TABLE - 2
Source
Destination
Supply
1
16
5
25 13 11, 5, 0
2
18 14 23 13
3
27 18 41 19
Demand 10, 5 12 15

38 | P a g e
TABLE - 3
Source
Destination
Supply
1
2 5
18
14 23 13, 8
3
27 18 41 19
Demand 10, 5, 0 12 15
TABLE - 4
Source
Destination
Supply
1
2 8
14
23 13, 8, 0
3
18 41 19
Demand 12,4 15

39 | P a g e
TABLE - 5
Source
Destination
Supply
3
18
4
41 19, 15
Demand 12,4, 0 15
TABLE - 6
Source
Destination
Supply
3
41
15
19, 15, 0
Demand 15, 0

40 | P a g e
Minimum Transportation Cost
Ex -2: Solve the following T.P using NWCM
Source
Destination
Supply
1 3 1 7 4 250
2 2 6 5 9 350
3 8 3 3 2 400
Demand 200 300 350 150
Solution: Here given T.P is balanced because .
Source
Destination
Supply
1 3 1 7 4
250, 50,
0
2 2
6 5
9
350
,100,0
3 8 3 3 2
400, 150,
0
Demand 200, 0 300, 250, 0
350,
250, 0
150, 0
200 50
250
100
0
250 150

41 | P a g e
Working Rules: (Unbalanced T.P)
1. First make a matrix unbalanced to balance by adding dummy row or column with
zero cost.
2. If the total supply is more than the total demand, then we add a new column, with
transportation cost 0
3. If the total demand is more than the total supply, then we add a new row, with
transportation cost 0
4. Select the upper left (North – West) cell of the transportation matrix and allocate
minimum of Supply and Demand value in that cell.
5. After allocation the minimum value to the cell, the cell corresponding row or
columns is completely discard, not consider in the further allocation.
6. Now with the new reduced table, again select the North – West cell and allocate the
available minimum values from the demand and supply.
7. Repeat steps (i), (ii) and (iii) until all supply and demand values are becomes zeros.
8. Hence obtain the initial basic feasible solution.
Ex - 3: Solve the following T.P using NWCM
Destination
Source
Supply
1 4 8 8 76
2 16 25 16 82
3 8 16 24 77
Demand 72 102 41
235
215

42 | P a g e
Solution: Here given T.P is unbalanced because .
So first we make the balanced transportation matrix by adding dummy
Destination
DUMMY
COLUMN
Source
Supply
1 4 (72) 8(4) 8 0 76,4,0
2 16 25(82) 16 0 82,0
3 8 16(16) 24(41) 0(20) 77,61,20,0
Demand 72,0 102,98,16,0 41,0 20,0
235
2.7 Least – Cost Method (LCM)
Working Rules: (Balanced T.P)
1. To select the smallest transportation cost cell available in the entire table and
allocate the minimum value from the supply and demand.
2. To delete the row/column which has exhausted means there is zero supply or
demand. The deleted row/column must not be considered for further allocation.
3. Again select the smallest cost cell in the existing table and allocate. (In case, if there
are more than one smallest cost, select the cells where maximum allocation can be
made).
4. And hence obtain the initial basic feasible solution.

43 | P a g e
Ex - 1: Solve the following T.P using LCM
Destination
Source
A B C D Supply
1 3 1 7 4 250
2 2 6 5 9 350
3 8 3 3 2 400
Demand 200 300 350 150 1000
Solution: Here given TP is balanced because .
First we find the minimum cost cell from the entire table and give the minimum values
from the supply and demand.
Destination
Source
A B C D Supply
1 3
1
(250)
7 4 250, 0
2 2 6 5 9 350
3 8 3 3 2 400
Demand 200
300,
50
350 150
Destination
Source
A B C D Supply
1
1
(250)
2
2
(200)
6 5 9
350,
150
3 8 3 3 2 400
Demand 200, 0
300,
50
350 150

44 | P a g e
Destination
Source
A B C D Supply
1
1
(250)
2
2
(200)
6 5 9
350,
150
3 3 3
2
(150)
400,
250
Demand
300,
50
350 150, 0
Destination
Source
A B C D Supply
1
1
(250)
2
2
(200)
6 5
350,
150
3
3
(50)
3
2
(150)
400,
250,
200
Demand
300,
50, 0
350
Destination
Source
A B C D Supply
1
1
(250)
2
2
(200)
5
350,
150
3
3
(50)
3
(200)
2
(150)
400,
250,
200 ,0
Demand
350,
150

45 | P a g e
Total minimum cost
=
Ex - 2: Solve the following T.P using LCM
Source
Destination
Supply
21 16 25 13 11
17 18 14 23 13
32 27 18 41 19
Demand 6 10 12 15 43
First we find the minimum cost cell from the entire table and give the minimum values
from the supply and demand.
Destination
Source
A B C D Supply
1
1
(250)
2
2
(200)
5
(150)
350,
150,0
3
3
(50)
3
(200)
2
(150)
400,
250,
200 ,0
Demand
350,
150, 0

46 | P a g e
Source
Destination
Supply
21 16 25
13
(11)
11,0
17 18 14 23 13
32 27 18 41 19
Demand 6 10 12 15,4 43
Source
Destination
Supply
13
(11)
17 18
14
(12)
23 13,1
32 27 18 41 19
Demand 6 10 12,0 15,4 43

47 | P a g e
Source
Destination
Supply
13
(11)
17
(1)
18
14
(12)
23 13,1, 0
32 27 41 19
Demand 6, 5 10 15,4 43
Source
Destination
Supply
13
(11)
17
(1)
14
(12)
32
27
(10)
41 19, 9
Demand 6, 5 10, 0 15,4 43

48 | P a g e
Source
Destination
Supply
13
(11)
17
(1)
14
(12)
32
(5)
27
(10)
41 19, 9, 4
Demand 6, 5, 0 15,4 43
Source
Destination
Supply
13
(11)
17
(1)
14
(12)
32
(5)
27
(10)
41
(4)
19, 9, 4, 0
Demand 15,4, 0 43

49 | P a g e
Total minimum cost
2.8 Vogel’s Approximation Method (VAM)
Working Rule for balanced TP
1. Calculate penalties for each row and column by taking the difference between the
smallest cost and next highest cost available in that row/column. If there are two
smallest costs, then the penalty is zero.
2. Select the row/column, which has the largest penalty and makes allocation in the
cell having the least cost in the selected row/column, contains minimum unit cost. If
there is again a tie, select one where maximum allocation can be made.
3. Delete the row/column, which has satisfied the supply and demand.
4. Repeat steps 1 and 2 until the entire supply and demands are satisfied.
5. And hence obtained the initial basic feasible solution.
Ex - 1: Solve the following T.P using VAM.
A B C D Supply
1 3 1 7 4 300
2 2 6 5 9 400
3 8 3 3 2 500
Demand 250 350 400 200 1200
Here first we calculate the penalties for each row and column say

50 | P a g e

51 | P a g e
Total Minimum cost
Ex - 2: Solve the following T.P using VAM.
A B C Supply
1 2 7 4 5
2 3 3 1 8
3 5 4 7 7
Demand 7 9 4 20
Here first we calculate the penalties for each row and column say

52 | P a g e

53 | P a g e
Total minimum cost
2.9 Optimal solution: MODI Method – UV Method
There are two phases to solve the transportation problem. In the first phase, the initial
basic feasible solution has to be found and the second phase involves optimization of the
initial basic feasible solution that was obtained in the first phase. There are three methods
for finding an initial basic feasible solution,
1. North-West Corner Method
2. Least Cost Method
3. Vogel’s Approximation Method
Will discuss how to optimize the initial basic feasible solution through an explained
example. Consider the below transportation problem

54 | P a g e
Solution:
Step 1: Check whether the problem is balanced or not. If the total sum of all the supply
from sources O1, O2, and O3 is equal to the total sum of all the demands for destinations
D1, D2, D3 and D4 then the transportation problem is a balanced transportation problem.
Note: If the problem is not unbalanced then the concept of a dummy row or a dummy
column to transform the unbalanced problem to balanced can be followed as discussed.

55 | P a g e
Step 2: Finding the initial basic feasible solution. Any of the three aforementioned methods
can be used to find the initial basic feasible solution. Here, North-West Corner Method will
be used. And according to the North-West Corner Method this is the final initial basic
feasible solution:
Now, the total cost of transportation will be
Step 3: U-V method to optimize the initial basic feasible solution.
The following is the initial basic feasible solution:
For U-V method the values and have to be found for the rows and the columns
respectively. As there are three rows so three ui values have to be found i.e. for the first
row, for the second row and for the third row.

56 | P a g e
Similarly, for four columns four vj values have to be found i.e. and . Check the
image below:
There is a separate formula to find and ,
, where is the cost value only for the allocated cell. Before applying the
above formula we need to check whether – is equal to the total number of
allocated cells or not where m is the total number of rows and n is the total number of
columns.
In this case and total number of allocated cells is so – . The
case when – is not equal to the total number of allocated cells will be discussed in
the later posts.
Now to find the value for u and v we assign any of the three u or any of the four as . Let
we assign in this case. Then using the above formula we will get as
and as .
Similarly, we have got the value for so we get the value for which
implies . From the value of we get which implies .

57 | P a g e
Now, compute penalties using the formula – only for unallocated cells.
We have two unallocated cells in the first row, two in the second row and two in the third
row. Let’s compute this one by one.
1. For –
2. For
3. For –
4. For –
5. For –
6. For –
The Rule: If we get all the penalties value as zero or negative values that mean the
optimality is reached and this answer is the final answer.
But if we get any positive value means we need to proceed with the sum in the next step.
Now find the maximum positive penalty.
Here the maximum value is 6 which correspond to cell. Now this cell is new basic cell.
This cell will also be included in the solution.

58 | P a g e
The rule for drawing closed-path or loop. Starting from the new basic cell draw a closed-
path in such a way that the right angle turn is done only at the allocated cell or at the new
basic cell.
Assign alternate plus-minus sign to all the cells with right angle turn (or the corner) in the
loop with plus sign assigned at the new basic cell.

59 | P a g e
Consider the cells with a negative sign. Compare the allocated value (i.e. 200 and 250 in this
case) and select the minimum (i.e. select 200 in this case). Now subtract 200 from the cells
with a minus sign and add 200 to the cells with a plus sign. And draw a new iteration. The
work of the loop is over and the new solution looks as shown below
Check the total number of allocated cells is equal to – . Again find values and
values using the formula where is the cost value only for allocated cell.
Assign then we get . Similarly, we will get following values for and .
Find the penalties for all the unallocated cells using the formula – .
1. For –
2. For –
3. For –
4. For –
5. For –

60 | P a g e
6. For –
There is one positive value i.e. 1 for . Now this cell becomes new basic cell.
Now draw a loop starting from the new basic cell. Assign alternate plus and minus sign
with new basic cell assigned as a plus sign.
Select the minimum value from allocated values to the cell with a minus sign. Subtract this
value from the cell with a minus sign and add to the cell with a plus sign. Now the solution
looks as shown in the image below:

61 | P a g e
Check if the total number of allocated cells is equal to – . Find u and v values as
above.
Now again find the penalties for the unallocated cells as above.
1. For –
2. For –
3. For –
4. For –
5. For –
6. For –
All the penalty values are negative values. So the optimality is reached. Now, find the total
cost i.e.
.
2.10 Degeneracy in Transportation Problem
This session will discuss degeneracy in transportation problem through an explained
example.

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Solution: This problem is balanced transportation problem as total supply is equal to total
demand.
Initial basic feasible solution: Least Cost Cell Method will be used here to find the initial
basic feasible solution. One can also use North-West Corner Method or Vogel’s
Approximation Method to find the initial basic feasible solution. Using Least Cost Method
we get the following solution.

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Optimization of the solution using U-V Method:
Check whether – total number of allocated cells.
In this case – – where as total number of allocated cells are 7,
hence this is the case of degeneracy in transportation problem.
So in this case we convert the necessary number (in this case it is m + n – 1 = total number
of allocated cells i.e. 8 – 7 = 1) of unallocated cells into allocated cells to satisfy the above
condition. Steps to convert unallocated cells into allocated cells:
 Start from the least value of the unallocated cell.
 Check the loop formation one by one.
 There should be no closed-loop formation.
 Select that loop as a new allocated cell and assign a value ‘e’.
The closed loop can be in any form but all the turning point should be only at allocated cell
or at the cell from the loop is started

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There are 13 unallocated cells. Select the least value (i.e. 5 in this case) from unallocated
cells. There are two 5s here so you can select randomly any one. Let’s select the cell with
star marked.
Check if there is any closed-loop formation starting from this cell. If a closed-loop is drawn
from this cell following the condition for closed-loop then it can be observed that this cell
cannot be reached to complete the closed-loop. So this cell will be selected and assigned a
random value ‘e’.
Note: If the closed loop would have been formed from that cell then we would try another
cell with least value and do the same procedure and check whether closed loop is possible
or not. Now total number of allocated cells becomes 8 and – – .
Now this solution can be optimized using U-V method. We get the below solution after
performing optimization using U-V method.

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The presence of two ‘e’ in the final solution means after doing some iterations during
optimization, the condition for degeneracy will be met once again.
While finding the total cost, just leave the ‘e’ and multiply the allocated value with its cell’s
cost value and add all of them.
So, the transportation cost is
.
2.11 Exercise
1. Find the initial basic feasible solution for the following transportation problem,
using North-West Corner Rule method.
Sources Supply
3 8 5 7
4 4 2 8
6 5 8 10
2 6 3 15
Demand 8 10 22
 Answer: 169
2. Find out the minimum cost solution for the following transportation problem, using
North West Corner Rule method.
Sources Supply
A 16 19 12 14
B 22 13 19 16
C 14 28 8 12
Demand 10 15 17

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 Answer: 570
3. Obtain the initial basic feasible solution to the following TP using least cost method.
Sources Supply
1 2 3 4 6
4 3 2 5 8
5 2 2 1 10
Demand 4 6 8 6
 Answer: 38
4. Find the initial basic feasible solution for the following TP by VAM:
Sources Supply
11 13 17 17 250
16 18 14 10 300
21 24 13 10 400
Demand 200 225 275 250
 Answer: 12075
5. Find the initial basic feasible solution for the following TP by VAM:
Sources Supply
6 4 1 50
3 8 7 40
4 4 2 60
Demand 20 95 35 150
 Answer: 555

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As a coherent mathematical discipline, combinatorial optimization is relatively young.
When studying the history of the field, one observes a number of independent lines of
research, separately considering problems like optimum assignment, shortest spanning
tree, transportation, and the traveling salesman problem.
Only in the 1950’s, when the unifying tool of linear and integer programming became
available and the area of operations research got intensive attention, these problems were
put into one framework, and relations between them were laid. Indeed, linear
programming forms the hinge in the history of combinatorial optimization.
Its initial conception by Kantorovich and Koopmans was motivated by combinatorial
applications, in particular in transportation and transshipment. After the formulation of
linear programming as generic problem, and the development in 1947 by Dantzig of the
Simplex method as a tool, one has tried to attack about all combinatorial optimization
problems with linear programming techniques, quite often very successfully.
A cause of the diversity of roots of combinatorial optimization is that several of its
problems descend directly from practice, and instances of them were, and still are, attacked
daily. One can imagine that even in very primitive (even animal) societies, finding short
paths and searching (for instance, for food) is essential. A traveling salesman problem
crops up when you plan shopping or sightseeing, or when a doctor or mailman plans his
tour. Similarly, assigning jobs to men, transporting goods, and making connections, form
elementary problems not just considered by the mathematician.
It makes that these problems probably can be traced back far in history. In this survey
however we restrict ourselves to the mathematical study of these problems. At the other
end of the time scale, we do not pass 1960, to keep size in hand.
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Assignment Problem

Module – 3: Assignment Problem
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3.1 Introduction
Assignment Problem is a special type of linear programming problem where the objective
is to minimize the cost or time of completing a number of jobs by a number of persons. The
assignment problem in the general form can be stated as follows:
“Given n facilities, n jobs and the effectiveness of each facility for each job, the problem is to
assign each facility to one and only one job in such a way that the measure of effectiveness
is optimized (Maximized or minimized).” Several problems of management have a
structure identical with the assignment problem. For example:
Example I: A manager has four persons (i.e. facilities) available for four separate jobs (i.e.
jobs) and the cost of assigning (i.e. effectiveness) each job to each person is given. His
objective is to assign each person to one and only one job in such a way that the total cost
of assignment is minimized.
Example II: A manager has four operators for four separate jobs and the time of completion
of each job by each operator is given. His objective is to assign each operator to one and
only one job in such a way that the total time of completion is minimized.
Example III: A tourist car operator has four cars in each of the four cities and four
customers in four different cities. The distance between different cities is given. His
objective is to assign each car to one and only one customer in such a way that the total
distance covered is minimized
Assignment problems deal with the question how to assign n objects to m other objects in
an injective fashion in the best possible way. An assignment problem is completely
specified by its two components the assignments, which represent the underlying
combinatorial structure, and the objective function to be optimized, which models "the best
possible way”. The assignment problem refers to another special class of linear
programming problem where the objective is to assign a number of resources to an equal
number of activities on a one to one basis so as to minimize total costs of performing the
tasks at hand or maximize total profit of allocation.
3.2 Structure of assignment problem
Each assignment problem has a table or matrix associated with it. Generally the row
contains the objects or people we wish to assign and the column comprise the jobs or task
we want them assigned to. Consider a problem of assignment of n resources to m activities
so as to minimize the overall cost or time in such a way that each resource can associate
with one and only one job. The cost matrix is given as under:

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3.3 Hungarian Method for solving AP
The Hungarian method was developed by H. Kuhn and is based upon the work of two
Hungarian mathematicians D. Konig and J. Egervary.
For application of the algorithm, it is assumed that all of the ’s of the starting cost matrix
are non- negative and the assignment problem is of minimization case.
Working Rules
1. Subtract the minimum cost from the respective rows then each row containing at
least one zero. Same procedure apply on each column and it is observe that each
column is also having at least one zero. Therefore we get at least one zero in each
row and each column.
2. Examine these rows, which has exactly single zero, give an assignment (means a
square on zero) around it and cross all other zeros in same column. Repeat
same procedure for columns.
3. If passes through above procedure, we get any row/column who does not have any
assignment then follows the below procedure
3.1 Tick the row which does not have any assignment in which also find the
cross .
3.2 Next select the column corresponding to that cross .
3.3 In crossed column find the assignment
3.4 And hence tick the row in assignment lies.
4. Draw lines from unticked rows and ticked columns.
5. Select smallest cost from unlined cost.
6. Add this cost to point of intersection of two lines and subtract it from all unlined cost.
7. Repeat above 1 to 6 steps until each row/column has assignment. And hence we get
optimal assignment cost/time.

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Ex-1: Solve the following AP and find the minimum cost.
2 3 5 3
10 7 13 14
3 2 1 10
3 5 4 6
Solution:
Step-1: Subtract the minimum cost from the respective rows then each row containing at
least one zero.
2 -2 = 0 3-2 = 1 5-2 = 3 3-2 = 1
10-7 = 3 7-7 = 0 13-7 = 6 14-7 = 7
3 -1 = 2 2-1 = 1 1-1 = 0 10-1 = 9
3 -3 = 0 5-3 = 2 4-3 = 1 6-3 = 3
0 1 3 1
3 0 6 7
2 1 0 9
0 2 1 3
Same procedure apply on each column and it is observe that each column is also having at
least one zero.
0 1 3 1-1 = 0
3 0 6 7-1= 6
2 1 0 9-1= 8
0 2 1 3-1= 2

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0 1 3 0
3 0 6 6
2 1 0 8
0 2 1 2
Therefore we get at least one zero in each row and each column.
Step-2: Examine these rows, which has exactly single zero, give an assignment (means a
square on zero) around it and cross all other zeros in same column.
Repeat same procedure for columns.
Since after step – 2 each row/column has an assignment. So optimal assignment are
,

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Therefore the minimum assignment cost/time units.
Ex-2: Solve the following AP and find the minimum cost.
A 26 23 27
B 23 22 24
C 24 20 23
Solution:
Step-1: Subtract the minimum cost from the respective rows then each row containing at
least one zero.
A 26 -23 =23 23-23 =0 27-23 =4
B 23-22 =1 22-22 =0 24-22 =2
C 24-20 =4 20-20 =0 23-20 =3
A 23 0 4
B 1 0 2
C 4 0 3
Same procedure apply on each column and it is observe that each column is also having at
least one zero.
A 23-1=22 0 4-2 =2
B 1-1=0 0 2-2 =0
C 4-1=3 0 3-2 =1

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A 22 0 2
B 0 0 0
C 3 0 1
Therefore we get at least one zero in each row and each column.
Step-2: Examine these rows, which has exactly single zero, give an assignment (means a
square on zero) around it and cross all other zeros in same column.
Repeat same procedure for columns.
Here C does not have any assignment therefore that solution is not optimal then we can
move on step -3 as follows:
Step – 3: Tick the row which does not have any assignment in which also find the
cross .

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Next select the column corresponding to that cross
In crossed column find the assignment
And hence tick the row where assignment lies.

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Step -4: Draw lines from unticked rows and ticked columns.
Step – 5: Select smallest cost from unlined cost.
Here unlined cost are 22, 2, 3 and 1 in which 1 is minimum.
Step -6: Add this cost to point of intersection of two lines and subtract it from all unlined
cost.

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Now according to step – 2 we have each row and column must have at least one assignment
showing in the following table:
As per above table, we say that
, ,
And the minimum cost/time units.
Ex – 3: A computer centre has four expert programmers and needs to develop four
application programs. The head of the computer centre, estimates the computer time (in
minutes) required by the respective experts to develop the application programs as
follows:
Programmers
Programs
A B C D
1 120 100 80 90
2 80 90 110 70
3 110 140 120 100
4 90 90 80 90
Find the assignment pattern that minimizes the time required to develop the application
programs.
A 21 0 1
B 0 1 0
C 2 0 0

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Solution: Let us subtract the minimum element of each row from every element of that
row. Note that the minimum element in the first row is 80. So 80 is to be subtracted from
every element of the first row, i.e., from 120, 100, 80 and 90, respectively. As a result, the
elements of the first row of the resulting matrix would be 40, 20, 0, and 10 respectively.
Similarly, we obtain the elements of the other rows of the resulting matrix. Thus, the
resulting matrix is:
A B C D
1 40 20 0 10
2 10 20 40 0
3 10 40 20 0
4 10 10 0 10
Let us now subtract the minimum element of each column from every element of that
column in the resulting matrix. The minimum element in the first column is 10. So 10 is to
be subtracted from every element of the first column, i.e., from 40, 10, 10, and 10,
respectively.
As a result, the elements of the first column of the resulting matrix are 30, 0, 0, 0,
respectively. Similarly, we obtain the elements of the other columns of the resulting matrix.
Thus, the resulting matrix is:
A B C D
1 30 10 0 10
2 0 10 40 0
3 0 30 20 0
4 0 0 0 10
Now, starting from first row onward, we draw a rectangle around the 0 in each row having
a single zero and cross all other zeroes in the corresponding column. Here, in the very first
row we find a single zero. So, we draw a rectangle around it and cross all other zeroes in
the corresponding column. We get

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In the second, third and fourth row, there is no single zero. Hence, we move column-wise.
In the second column, we have a single zero. Hence, we draw a rectangle around it and
cross all other zeroes in the corresponding row. We get
In the matrix above, there is no row or column, which has a single zero. Therefore, we first
move row-wise to locate the row having more than one zero. The second row has two
zeroes. So, we draw a rectangle arbitrarily around one of these zeroes and cross the other
one. Let us draw a rectangle around the zero in the cell (2, A) and cross the zero in the cell
(2, D). We cross out the other zeroes in the first column. Note that we could just as well
have selected the zero in the cell (2, D), drawn a rectangle around it and crossed all other
zeroes. This would have led to an alternative solution. In this way, we are left with only one
zero in every row and column around which a rectangle has been drawn.
This means that we have assigned only one operation to one operator. Thus, we get the
optimum solution as follows:
A B C D
1 30 10 0 10
2 0 10 40 0
3 0 30 20 0
4 0 0 0 10
Note that the assignment of jobs should be made on the basis of the cells corresponding to
the zeroes around which rectangles have been drawn.
Therefore, the optimum solution for this problem is: 1  C, 2  A, 3  D, 4  B

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This means that programmer 1 is assigned program C, programmer 2 is assigned program
A, and so on.
The minimum time taken in developing the program is = 80 + 80 + 100 + 90 = 350 min.
Ex – 4: A company is producing a single product and selling it through five agencies
situated in different cities. All of a sudden, there is a demand for the product in five more
cities that do not have any agency of the company. The company is faced with the problem
of deciding on how to assign the existing agencies to dispatch the product to the additional
cities in such a way that the travelling distance is minimized. The distances (in km)
between the surplus and deficit cities are given in the following distance matrix
Deficit
City
Surplus
City
I II III IV V
A 160 130 175 190 200
B 135 120 130 160 175
C 140 110 155 170 185
D 50 50 80 80 110
E 55 35 70 80 105
Determine the optimum assignment schedule.
Solution: Subtracting the minimum element of each row from every element of that row,
we have
I II III IV V
A 30 0 45 60 70
B 15 0 10 40 55
C 30 0 45 60 75
D 0 0 30 30 60
E 20 0 35 45 70
Subtracting the minimum element of each column from every element of that column, we
have

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I II III IV V
A 30 0 35 30 15
B 15 0 0 10 0
C 30 0 35 30 20
D 0 0 20 0 5
E 20 0 25 15 15
We now assign zeroes by drawing rectangles around them as explained in Example 1. Thus,
we get
I II III IV V
A 30 0 35 30 15
B 15 0 0 10 0
C 30 0 35 30 20
D 0 0 20 0 5
E 20 0 25 15 15
Since the number of assignments is less than the number of rows (or columns), we proceed
from Step 5 onwards of the Hungarian method as follows:
 We tick mark (  ) the rows in which the assignment has not been made. These are
the 3rd and 5th rows.
 We tick mark (  ) the columns which have zeroes in the marked rows. This is the
2nd column.
 We tick mark (  ) the rows which have assignments in marked columns. This is the
1st row.
 Again we tick mark (  ) the column(s) which have zeroes in the newly marked row.
This is the 2nd column, which has already been marked. There is no other such
column. So, we have

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I II III IV V
A
30
0 35 30 15 
B 15 0 0 10 0
C 30 0 35 30 20 
D 0 0 20 0 5
E 20 0 25 15 15 

We draw straight lines through unmarked rows and marked columns as follows:
I
II
III IV V
A
30
0 35 30 15 
B 15 0 0 10 0
C 30 0 35 30 20 
D 0 0 20 0 5
E 20 0 25 15 15 

We proceed as follows, as explained in step 6 of the Hungarian method:
i) We find the smallest element in the matrix not covered by any of the lines. It is
15 in this case.

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ii) We subtract the number ‘15’ from all the uncovered elements and add it to the
elements at the intersection of the two lines.
iii) Other elements covered by the lines remain unchanged.
Thus, we have
We repeat Steps 1 to 4 of the Hungarian method and obtain the following matrix:
Since each row and each column of this matrix has one and only one assigned 0, we obtain
the optimum assignment schedule as follows: A  V, B  III, C  II, D  I, E  IV Thus,
the minimum distance is 200 + 130 + 110 + 50 + 80 = 570 km.

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3.4 Exercise
1. Find the optimal assignment schedule of the following cost matrix.
Marketing
Executive
N E W S
A 14 20 11 19
B 12 10 15 9
C 16 19 18 15
D 17 13 15 14
2. Solve the assignment problem represented by the following effective matrix.
a b c d e f
A 9 22 58 11 19 27
B 43 78 72 50 63 48
C 41 28 91 37 45 33
D 74 42 27 49 39 32
E 36 11 57 22 25 18
F 3 56 53 31 17 28

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Replacement problems involve items that degenerate with use or with the passage of time
and those that fail after a certain amount of use or time. Items that deteriorate are likely to
be large and costly (e.g., machine tools, trucks, ships, and home appliances). Non
deteriorating items tend to be small and relatively inexpensive (e.g., light bulbs, vacuum
tubes, ink cartridges). The longer a deteriorating item is operated the more maintenance it
requires to maintain efficiency. Furthermore, the longer such an item is kept the less is its
resale value and the more likely it is to be made obsolete by new equipment. If the item is
replaced frequently, however, investment costs increase. Thus the problem is to determine
when to replace such items and how much maintenance (particularly preventive) to
perform so that the sum of the operating, maintenance, and investment costs is minimized.
In the case of non deteriorating items the problem involves determining whether to replace
them as a group or to replace individuals as they fail. Though group replacement is
wasteful, labor cost of replacements is greater when done singly; for example, the light
bulbs in a large subway system may be replaced in groups to save labor. Replacement
problems that involve minimizing the costs of items, failures, and the replacement labor are
solvable either by numerical analysis or simulation.
The “items” involved in replacement problems may be people. If so, maintenance can be
interpreted as training or improvements in salary, status, or fringe benefits. Failure can be
interpreted as departure and investment as recruiting, hiring, and initial training costs.
There are many additional complexities in such cases; for example, the effect of one
person’s resigning or being promoted on the behavior of others. Such controllable aspects
of the environment as location of work and working hours can have a considerable effect
on productivity and failure rates. In problems of this type, the inputs of the behavioral
sciences are particularly useful.
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Replacement Theory

Module – 4: Replacement Theory
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4.1 Introduction
The replacement problems are concerned with the situations that arise when some items
such as men, machines, and electric-light bulbs, etc. need replacement due to their
decreased efficiency, failure or breakdown. Such decreased efficiency or complete
Breakdown may either be gradual or all of a sudden. The replacement problem arises
because of the following factors:
1. The old item has become in worse condition and work badly or requires expensive
maintenance.
2. The old item has failed due to accident or otherwise and does not work at alt, or the old
item is Expected to fail shortly.
3. A better or more efficient design of machine or equipment has become available in the
market.
In the case of items whose efficiency go on decreasing according to their age, it requires to
spend more money on account of increased operating cost, increased repair cost, increased
scrap, etc. So in such cases, the replacement of an old item with new one is the only
alternative to prevent such increased expenses. Thus the problem of replacement is to
decide best policy to determine an age at which the replacement is most economical
instead of continuing at increased cost. The need for replacement arises in many situations
so that different type of decisions may have to be taken.
For example,
i. We may decide whether to wait for complete failure of the item (which might cause
some loss), or to replace earlier at the expense of higher cost of the item.
ii. The expensive items may be considered individually to decide whether we should
replace now or, if not, when it should be reconsidered for replacement.
iii. It may be decided whether we should replace by the same type of item or by
different type (latest model) of item.
The problem of replacement is encountered in the case of both men and machines. Using
probability it is possible to estimate the chance of death (or failure) at various ages.
The main objective of replacement is to direct the organization or maximizing its profit (or
minimizing the cost).

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4.2 Failure Mechanism of Items
The term ‘failure’ has a wider meaning in business than what it has in our daily life. There
are two kinds of failure.
1. Gradual Failure: The mechanism under this category is progressive. That is, as the life
of an item increases, its efficiency deteriorates, causing:
i. Increased expenditure for operating costs,
ii. decreased productivity of the equipments,
iii. Decrease in the value of the equipment, i.e., the resale of saving value decreases.
For example, mechanical items like pistons, bearings, rings etc. Another example is
Automobile tires.
2. Sudden Failure: This type of failure is applicable to those items that do not deteriorate
markedly with service but which ultimately fail after some period of using. The period
between installation and failure is not constant for any particular type of equipment but
will follow some frequency distribution, which may be progressive, retrogressive or
random in nature.
 Progressive failure: Under this mechanism, probability of failure increases with the
increase in the life of an item. For example, electric light bulbs, automobile tubes,
etc.
 Retrogressive failure: Certain items have more probability of failure in the
beginning of their life, and as the time passes the chances of failure become less.
That is, the ability of the unit to survive in the initial period of life increases its
expected life.
 Industrial equipments with this type of distribution of life span are exemplified by
aircraft engines.
 Random failure: Under this failure, constant probability of failure is associated with
items that fail from random causes such as physical shocks, not related to age.
 In such a case, virtually all items fail before aging has any effect. For example,
vacuum tubes in air-borne equipment have been shown to fail at a rate independent
of the age of the tube.

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The replacement situations may be placed into four categories:
1. Replacement of capital equipment that becomes worse with time, e.g. machines
tools, buses in a transport organization, planes, etc.
2. Group replacement of items that fail completely, e.g., light bulbs, radio tubes, etc.
3. Problems of mortality and staffing.
4. Miscellaneous Problems.
4.3 Costs to be considered
In general, the costs to be included in considering replacement decisions are all those costs
that depend upon the choice or age of machine. In some special problems, certain costs
need not be included in the calculations. For example, in considering the optimum decision
of replacement for a particular machine, the costs that do not change with the age of the
machine need not be considered.
4.4 When The Replacement Is Justified?
This question can easily be answered by considering a case of truck owner whose problem
is to find the ‘best’ time at which he should replace the old truck by new one. The truck
owner wants to transport goods as cheaply as possible. The associated costs are:
(i) The running costs, and
(ii) The capital costs of purchasing a truck.
These associated costs can be expressed as average cost per month. Now the truck owner
will observe that the average monthly cost will go on decreasing, longer the replacement is
postponed.
However, there will come an age at which the rate of increase of running costs more than
compensates the saving in average capital costs. Thus, at this age the replacement is
justified.
4.5 Replacement model
Replacement model is used in the decision making process replacing the used asset or
equipment with a substitute mostly the new asset or equipment is best for uses this is the
meaning of replacement.

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Why we need to replace an asset?
The reason is the asset value or the efficiency of asset is gradually decreases with the
passage of time at the same time the maintenance cost of the asset is gradually increasing.
Efficiency Maintenance cost
At some period of time the Maintenance cost for the asset is very high then it is necessary
to replace the asset by new one but it is require finding a right time means period of time
when the asset is resale in the market that is called Optimum replacement period.
4.6 Replacement policy
If the running and maintenance cost of the asset or machine for the next year is more than
the average annual cost of selected year then replace at the end of the selected year.
1. Replacement of items that deteriorate
Whose maintenance costs increase with time; ignoring changes in the value of money
during the period.
2. Replacement of items whose maintenance costs increase with time and value
of the money also changes with time.
3. Group replacement model
Ex-1: The cost of a machine is Rs. 10500 and its scrap value (Resale value) is Rs.500. The
maintenance costs found from experience are as follows:
Year 1 2 3 4 5 6 7 8
Maintenance
cost (Rs.)
300 500 700 1000 1400 1900 2400 3000
When should the machine be replaced?
Solution:

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1 2 3 4 5 6 7
Years
of
Service
Resale
value
Depreciatio
n cost
[Purchase
price –
Resale
value]
[10500-500]
Annual
mainten
ance
cost
Cumulative of
maintenance
cost
Total
cost
[(3)+(5)
]
Average
Annual
cost
1 500 10000 300 300 10300
10300/1
= 10300
2 500 10000 500 800 10800
10800/2
= 5400
3 500 10000 700 1500 11500
11500/3
= 3833
4 500 10000 1000 2500 12500
12500/4
= 3125
5 500 10000 1400 3900 13900
13900/5
= 2780
6 500 10000 1900 5800 15800
15800/6
= 2633.3
7 500 10000 2400 8200 18200
18200/7
= 2600
8 500 10000 3000 11200 21200
21200/8
= 2650
Find minimum cost from the Average Annual cost that opposite year will be final optimum
year for resale the asset or replace it by new one.
Here it is observed that the maintenance cost in the 8th year becomes greater than the
average cost for 7 years. Hence the machine should be replaced at the end of 7th year.
Alternatively, last column of above table shows that the average cost starts increasing in
the 8th year, so the machine should be replaced before the beginning of 8th year, i.e. at the
end of 7th year.

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Ex – 2: The cost of a machine is Rs.6100 and its scrap value is only Rs. 100. The
maintenance costs are found experience to be:
Year 1 2 3 4 5 6 7 8
Maintenance cost in
Rs.
100 250 400 600 900 1250 1600 2000
When should machine be replaced?
Solution:
1 2 3 4 5 6 7
Years
of
Service
Resale
value
Depreciation
cost
[Purchase
price –
Resale value]
[6100-100]
Annual
mainten
ance
cost
Cumulative
of
maintenanc
e cost
Total
cost
[(3)+(5)]
Average
Annual cost
1 100 6000 100 100 6100
2 100 6000 250 350 6350
3 100 6000 400 750 6750
4 100 6000 600 1350 7350
5 100 6000 900 2250 8250
6 100 6000 1250 3500 9500
7 100 6000 1600 5100 11100
8 100 6000 2000 7100 13100
average cost for 6 years. Hence the machine should be replaced at the end of 6th year.

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the 7th year, so the machine should be replaced before the beginning of 7th year, i.e. at the
end of 6th year.
Ex - 3: A new automobile vehicle costs Rs. 10000 and it can be sold at the end of any year
with the selling price as shown. The operating and maintenance cost table. Find when the
automobile vehicle needs to be replacing because of wear and tear.
Year 1 2 3 4 5 6
Scrap value 7000 5000 3000 2000 1000 500
Maintenance
cost
1000 1600 1800 2500 3000 3500
Solution:
1 2 3 4 5 6 7
Year
Resale/
Scrap
value
Depreciation
cost
[Purchase
price –
Resale value]
[10000-scrap
value]
Annual
mainten
ance
cost
Cumulative
of
maintenanc
e cost
Total
cost
[(3)+(5)
]
Average
Annual cost
1 7000 3000 1000 1000 4000
2 5000 5000 1600 2600 7600
3 3000 7000 1800 4400 11400
4 2000 8000 2500 6900 14900
5 1000 9000 3000 9900 18900
6 500 9500 3500 13400 22900
average cost for 4th year. Hence the vehicle should be replaced at the end of 4th year.
the 5th year, so the vehicle should be replaced before the beginning of 5th year, i.e. at the
end of 4th year.

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Ex – 4: Following failure rates have been observed for certain type of light bulbs
Month 1 2 3 4 5
Percentage/
probability of
bulbs failing
by end of
month
10 25 50 80 100
There are total 1000 bulbs in use and it cost Rs. 10 to replace an individual bulb which has
fused out. If all bulbs are replaced simultaneously. It would cost Rs 4. Per bulb. Two
polices are being considered for replacement of bulbs; first replace all bulbs simultaneously
at fixed interval whether fails or not and do individual replacement in immediate periods
secondly individual replacement of bulbs as and when it fails. Determine the optimum
policy for replacement of bulbs based above failure data and costs.
Solution:
Here we are work on both policies individual and group replacement in which those are
having minimum cost for replacement and then we are follow that policy.
PART – I: For individual replacement
Here probability of failure is given and that is always a cumulative probability so first we
calculate probability of failure for each month.
Step – 1: Find probability of failure for each month
Probability of bulbs fails during 1st month
Probability of bulbs fails during 2nd month
Probability of bulbs fails during 3rd month
Probability of bulbs fails during 4th month
Probability of bulbs fails during 5th month
Step – 2: Find average life of the bulb
Where indicate number of month and
indicates the total number of months.

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Step – 3: Find average number of failure (fail bulbs) per month
Step – 4: Find average cost of individual replacement per month
PART – II: For group replacement
Here given that total number of bulbs used
Step – 1: Calculate number of failure during each month
Number of bulbs fails during 1st month

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Number of bulbs fails during 2nd month
Number of bulbs fails during 3rd month
Number of bulbs fails during 4th month
Number of bulbs fails during 5th month
Step – 2: Calculate cost for group replacement
And

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Month (n) Total Cost (C )
Average Cost
1
1000(4)+100(10)
=5000
2
1000(4)+[100+160](10)
=6600
3
1000(4)+[100+160+281](10)
=9410
4
1000(4)+[100+160+281+377](10)
=13180
5
1000(4)+[100+160+281+377+350](10)
=16680
According to above tabular data it is clear that after 3rd month the average cost would be
increase therefore it is required to replace bulbs in a group at end of the 3rd month of
starting of 4th month because at that time duration the average cost is minimum like
So the average cost of group replacement is
Now compare both the polices together
Average cost for individual replacement
Average cost for group replacement after 3 months.
So it is advisable to follow the group replacement policy for replacing the bulbs.

OPERATION RESEARCH FOR BEGINNERS.pdf

OPERATION RESEARCH FOR BEGINNERS.pdf

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OPERATION RESEARCH FOR BEGINNERS.pdf