mailto:mkbhambu@hotmail.com

OPERATIONS RESEARCH : NAMES

Operations Research is also known as:

Decision Science

Management Science

Operations Management

Quantitative Techniques

OPERATIONS RESEARCH: HISTORY

The roots of OR can be traced back many

decades, when early attempts were made to

use a scientific approach in the management

of organizations.

However, the beginning of the activity called

operations research has generally been

attributed to the military services early in World

War II.

Because of the war effort, there was an urgent

need to allocate scarce resources to the

various military operations and to the activities

within each operation in an effective manner.

Therefore, the British and then the U.S. military

management called upon a large number of

scientists to apply a scientific approach to

dealing with this and other strategic and tactical

problems.

In effect, they were asked to do research on

(military) operations. These teams of scientists

were the first OR teams.

By developing effective methods of using the

new tool of radar, these teams were instrumental

in winning the Air Battle of Britain.

Through their research on how to better manage convoy

and antisubmarine operations, they also played a major

role in winning the Battle of the North Atlantic.

Similar efforts assisted the Island Campaign in the

Pacific.

A key person in the post-war development of OR was

George B Dantzig. In 1947, he developed linear

programming and its solution method known as simplex

method.

Besides linear programming, many other tools of OR

such as statistical control, dynamic programming queuing theory and inventory theory were well

developed before the end of the 1950s.

O.R. as a formal subject is about fifty five years

old, origins may be traced to the latter half of

World War II. The impetus for its origin was the

development of radar defense systems for the

Royal Air Force, and the first recorded use of

the term Operations Research is attributed to a

British Air Ministry official named A. P. Rowe

who constituted teams to do “operational

researches” on the communication system and

the control room at a British radar station.

The studies had to do with improving the operational efficiency of systems (an objective which is still one of the

cornerstones of modern O.R.). This new approach of picking

an “operational’’ system and conducting “research” on how

to make it run more efficiently soon started to expand into

other arenas of the war.

Perhaps the most famous of the groups involved in this effort

was the one led by a physicist named P. M. S. Blackett which

included physiologists, mathematicians, astrophysicists, and

even a surveyor. This multifunctional team focus of an

operations research project group is one carried forward to this day. Blackett’s biggest contribution was in convincing the

authorities of the need for a scientific approach to manage

complex operations, and indeed he is regarded in many

circles as the original operations research analyst.

OPERATIONS RESEARCH : DEFINITIONS

Operations Research (OR) – The science that

applies mathematical and computer science

tools to support decision making.

Operations Research is concerned with

scientifically deciding how to best design and

operate man-machine systems usually

requiring the allocating of scarce resources.

-Operations Research Society, America

OR is the art of winning wars without actually fighting.

-Arthur Clarke

OR is a scientific method of providing executive

departments with a quantitative basis for decision

regarding the operations under their control.

-Morse and Kimbal

OR is the art of giving bad answers to problems where

otherwise worse answers are given.

-T.L. Satty

CHARACTERISTICS OF OPERATIONS RESEARCH

OR is a system approach

OR is an Inter-disciplinary team approach.

OR increases creative ability of the decision maker

OR is Scientific approach

(i) Defining

(ii) Observing

(iii) Formulating

(iv) Testing

(v) Analyzing

OR is Objectivistic approach

Digital computer

Quantitative solution

OR is a continuing process

Optimizing nature

Human judgment

CHARACTERISTICS OPERATIONS RESEARCH

Operation Research is the applications of scientific methods,

techniques and tools to problems involving the operations of a

system so as to provide those in control of the system with

optimum solutions to the problems. The significant features of

operation research are as below :

1. OR is a system approach: The essence of systems

approach is to find all significant and indirect effects on all

parts of a system and to evaluate each action in terms of the

effects for the system as a whole. e.g., a new strategy of

marketing department can effect all the other departments of

the organisation and so in evaluating the strategy, not only its

effects on the marketing department should be considered but also the effects of the proposal on other departments as well.

2. OR is an Inter-disciplinary team approach: OR

is interdisciplinary in nature and needs a team

approach solving economic, physical,

psychological, biological, sociological and

engineering aspects of any problem by the

assistance of mathematicians statisticians,

engineers, economists, management and

computer experts, this team for a given

problem tries to analyse the cause and effect

relationship between various parameters and

evaluates the outcome of various alternative

strategies.

3. OR increases creative ability of the decision

maker: OR is a powerful tool in increasing the

effectiveness of managerial decision. OR

techniques help the decision maker to

improve his creative and judicious

capabilities, analyse and understand the

problem situation leading to better control,

co-ordination, system finally better decisions.

4. OR is Scientific approach : OR gives scientific methods for the purpose of solving problems, and there is no place of whims a guesswork in it. It is a formulized process of reasoning and consists of the following steps:

(i) Defining: The problem to be analyzed clearly and defining the conditions for observations.

(ii) Observing: Observations are made under different conditions to determine the behaviour of the system.

(iii) Formulating: A hypothesis describing how the various factors involved are believed to interact and the best solution to the problem is formulated on the basis of above observations.

(iv) Testing: Finally the result of experiment is design and executed, observations are made and measurements are recorded.

(v) Analysing: Finally the result of experiment are analysis and check weather hypothesis is accepted or not. Of the hypothesis is accepted it means the solution obtained is optimum.

5. OR is Objectivistic approach : OR attempts to find out the

strategic or optimal solution to the problem under consideration. For this purpose, it is required that a measure of

effectiveness be defined which is based on the objectives of

the organisation. This measure is then used as the basis to

compare the alternative courses of action.

6. Digital computer : Use of digital computer has become an integral part of the operations research approach to decision-

making. The computer may be required due to the complexity

of the model, volume of data required or the computations to

be made. Many quantitative techniques are available in the

form of ‘canned’ programmes.

7. Quantitative solution. Operation research assists the management with a quantitative basis for decision making. OR attempts to provide a systematic and scientific rational approach for quantitative solutions to the various managerial problems.

8. OR is a continuing process : OR is a continuing process. It continues with the emergence of new problems, finding and implementing solutions and interpreting the results of such implementation. Problems continue to arise in the modern dynamic environment. As such OR becomes a continuing process.

9. Optimizing Nature : OR ties to optimize total

return by maximizing the profit and minimizing

the cost or loss.

10. Human judgment : In deriving quantitative

solution, sometimes human factors, play

significant role, in the problems, are ignored.

So, study of the OR is incomplete without a

study of human factors.

5. OR is Objectivistic approach : OR attempts to find out the

strategic or optimal solution to the problem under consideration. For this purpose, it is required that a measure of

effectiveness be defined which is based on the objectives of

the organisation. This measure is then used as the basis to

compare the alternative courses of action.

6. Digital computer : Use of digital computer has become an integral part of the operations research approach to decision-

making. The computer may be required due to the complexity

of the model, volume of data required or the computations to

be made. Many quantitative techniques are available in the

form of ‘canned’ programmes.

7. Quantitative solution. Operation research assists the management with a quantitative basis for decision making. OR attempts to provide a systematic and scientific rational approach for quantitative solutions to the various managerial problems.

8. OR is a continuing process : OR is a continuing process. It continues with the emergence of new problems, finding and implementing solutions and interpreting the results of such implementation. Problems continue to arise in the modern dynamic environment. As such OR becomes a continuing process.

9. Optimizing Nature : OR ties to optimize total

return by maximizing the profit and minimizing

the cost or loss.

10. Human judgment : In deriving quantitative

solution, sometimes human factors, play

significant role, in the problems, are ignored.

So, study of the OR is incomplete without a

study of human factors.

WHY OPERATIONS RESEARCH

You may ask, “Why must we learn the Operations

Research techniques?” Here are a few motivating

reasons:

Organizations are becoming more complex, Huge

numbers of choices and relentless time pressures and

margin pressures make the decisions you face more

daunting and more difficult.

Environments are changing so rapidly that past practices

are no longer adequate. Meanwhile, new enterprise

applications and software are generating massive

amounts of data – and it can see like an overwhelming

task to turn that data into insight and answers.

The costs of making bad decisions have increased.

OPERATIONS RESEARCH HELPS

Deciding where to invest capital in order to grow

Getting more value out of ERP(Enterprise Resource

Planning), CRM (Customer Relationship

Management), and other software systems

Figuring out the best way to run a call center

Locating a warehouse or depot to deliver material

s over shorter distances at reduced cost

Forecasting sales for a new kind of product that has

never marketed before

Solving complex scheduling problems

Planning for a potential terrorist attack

Deciding when to discount, and how much

Getting more cycles out of manufacturing

equipment

Optimizing a portfolio of investments, whether it

contains financial securities or pharmaceutical

product inventory

Deciding how large a budget to devote to Internet

vs. traditional sales

Planting crops in the face of uncertainty about

weather and consumer demand

SCOPE OF OPERATION RESEARCH

(The Multidisciplinary and Interdisciplinary Nature

of Operations Research)

I. IN DEFENCE OPERATIONS

Administration

Intelligence

Operations, and

Training and supply.

II.IN INDUSTRY

Applications of operations research in the area of management

1. Production Management : The production manager can

apply OR methods for

The remunerative policy with regard to time and piece rate.

Determination of optimum product mix.

Production, scheduling and sequencing the production run by

allocation of machines.

Work study operation including time study.

Selecting plant location and design of the sites.

Distribution policy

Loading and unloading facility for road transportation.

Maintenance crew sizes.

2. MARKETING MANAGEMENT

The marketing manager can apply OR method for

Product selection, timing and formulation of

competitive strategies.

Marketing research.

Distribution strategies.

Sales forecasting.

Sales promotion.

Selection of advertising media and terms of cost

and time factor

To find optimum number of Salesmen.

3. FINANCIAL MANAGEMENT

The financial manager can apply OR method for

Apply cash flow analysis for capital budgeting

Formulate credit policies, evaluate credit risks

Determine optimum replacement strategies.

Frame claim and complaint procedures.

Frame policies regarding capital structure.

Long range capital requirement.

Investments portfolio.

Dividend policies.

4. PERSONAL MANAGEMENT

The personal manager can apply OR method for

Forecasting the manpower requirement, framing of

recruitment policies, assignment of jobs to machines or

workers etc.

Selection of suitable personnel with due consideration for

age, education skills training etc.

Determination of optimum number of persons for each

service centre.

The promotional policies.

Mixes of age and skills.

5. PURCHASE DEPARTMENT

The purchased department can apply OR method for

Determining the quantity and timing of purchase of

raw materials, machinery etc.

Bidding policies.

Rules for buying and supplies under varying pries.

Equipment replacement policies.

Determination of quantities and timing of purchases.

6. RESEARCH AND DEVELOPMENT DEPARTMENT

The research and development department can

apply OR method for

Determining the areas for research and

development.

Scheduling and control of R and D projects.

Resource allocation and crashing in projects.

Project selection.

Reliability and alternative design.

7. MANUFACTURING DEPARTMENT

The manufacturing department can apply OR

method for :

Inventory control

Projection marketing balance.

Production scheduling

Production smoothing.

8. ORGANIZATION BEHAVIOUR DEPARTMENT

The OB department can apply OR method

for

Personnel selection and planning.

Scheduling of training programs.

Skills balancing.

Recruitment of Employees.

9. ACCOUNTING DEPARTMENT

The accounting department can apply OR

method for

Cash flow and fund flow planning.

Credit policy analysis.

Planning of delinquent account strategy.

10. TECHNIQUES AND GENERAL MANAGEMENT

The Techniques & General Management can

apply OR method for

Decision support systems and MIS;

forecasting.

Organizational design and control

Projection management,

strategic planning.

III. IN GOVERNMENT PLANNING

IV. AGRICULTURE: With the explosion of population and consequent

shortage of food, every country is facing the problem of :

Optimum allocation of land and various crops in accordance with the

climatic conditions;

Optimum distribution of water from various resources like canal for

irrigation purposes.

Thus there is a need of determining best policies under the prescribed

restrictions. Hence a good amount of work can be done in this direction.

V. IN HOSPITALS

VI. IN LIFE INSURANCE CORPORATION

VII. IN CONSTRUCTION PROJECTS

VIII. OPERATIONS RESEARCH MANAGEMENT INFORMATION SYSTEMS

IX. OPERATIONS RESEARCH AS SYSTEM SCIENCE:

Extensions

Unification

The Needs: Explication, Understanding, Prediction

Observation of the phenomenon

Modeling New Theories

Using Existing Models

Constructing Hypothesis

Obtaining Experimental Data

Testing for Confirmation Or

Attempt of Refutation

METHODOLOTY OF OR METHODS

Orientation

Problem Definition

Validation and Output Analysis

Solution

Implementation and Monitoring

Data Collection

Model Formulation

Basis of Classification

STRUCTURE PURPOSE TIME/BEHAVIOUR DEGREE OF SOLUTION CERTAINITY PROCEDURE

Descriptive Normative Model Model Predictive Model Static Dynamic Analytical Simulation Physical Symbolic Model Model Model Model Model Model Model

Probabilistic Non- Probabilistic Iconic Analogue Verbal Mathematic Model Model Model Model Model Model

CLASSIFICATION OF OR

MODEL

(A) Classification Based on Structure

1. Physical Model : These models provide a physical appearance of the real object under study either reduced in size or scaled up. These models cannot be manipulated and not very useful for prediction, therefore, problems such as portfolio section, media selection, production scheduling, etc. cannot be analysed with a physical model. Physical models are classified into the following two categories.

Iconic Models : Iconic models retain some of the physical and characteristics of the system they represent. An iconic model is either in an idealized form or a scaled version of the system. It is said to be scaled down when the dimensions of the model are smaller than those of the real object and model said to be scaled up when it is bigger than the real object. In other words, it is an image.

Examples :

A globe representing the earth.

Blue prints of a home.

Model of a cell in biology.

A baby toy car as a model of an automobile.

PHYSICAL MODELS

Analogue Model: These models represent a system or object by using set of properties different from the ones, held by the original object or system. There is no ‘look-alike’ relation between the model and the original. i.e. These models represent a system by the set of properties different from that of the original system and does not resemble physically. After the problem is solved, the solution is re-interpreted in terms of the original system.

Example : Organizational chart represent the state of formal relationships existing between

members of the organization.

Maps in different colours may represent water, desert, mountains etc.

Graphs of time series, stock market etc. may be used to represent quantitative relationship between any two properties.

Both models are easier to manipulate and can represent dynamic situations; so analogue model is more popular than iconic models.

SYMBOLIC MODELS These models use symbols like letters, numbers etc.to represent the

properties of the system. These models are also used to represent relationships which can be represented in a physical form. Symbolic models can be classified into two categories:

Verbal Models: These models describe a situation in written or spoken language.

Example: Written sentences, books, newspapers, journals etc.

Mathematical Models: These models represent the characteristics of a situation or reality by using a set of mathematical symbols and relationships. These models are widely used in OR due to their capacity to depict the complex relationship among the variables of a problem. Example : ‘+’, ‘–‘, ‘×’, ‘÷’.

CLASSIFICATION BASED ON PURPOSE

The models based on the purpose of their utility include :

Descriptive Models: Descriptive models simply describe some features of a situation based on observation

survey or other available data of a situation and do not predict or recommend.

Example :

Result of a n opinion poll.

Block diagram representing an algorithm or method for solving a problem.

Predictive Models: These models indicate that ‘if this occurs then that will follow’. They related dependent

and independent variables and permit trying out, ‘what if’ questions. In other words, these models are

used to predict the outcomes due to a given set of alternatives for the problem.

Example :

Television network try to predict the election results before the counting of all the votes.

Rain forecast before actual rainfall.

Normative Models : When a model has been repeatedly successful, it can be used to develop objective

decision rules or criteria for optimal solutions. These models are applicable to repetitive problems.

Example :

Linear programming is a normative or prescriptive model, because it prescribes what the managers

should do.

CLASSIFICATION BASED ON BEHAVIOUR

Static models : these models are considered independent of time. They

do not take into account the effect of changes taking place during a

particular time period. It involve only one decision for duration of a

given time period.

Example :an inventory models can be developed and solved to

determine economic order quantity for the next period assuming that

the demand in planning period would remain the same as that for

today.

Dynamic models : these models consider time as one of the important

variables and taken into account the effect of changes generated by

time. This involves not only one, but a series of interdependent

decisions are required.

Example : dynamic programming in which all possible results are

analyzed and best solution is selected.

CLASSIFICATION BASED ON DEGREE OF

CERTAINTY Deterministic Models : These models make assumption of certainty

and perfect knowledge. In this model the parameters are

completely defined. Examples: Linear Programming Problems,

Assignment Problems, Transportation Problems , Break even models

etc.

Probabilistic Models : Models in which at least one parameter or

decision variable is a random variable are called probabilistic

models. Variables are independent which is the function of

dependent variable(s). This means payoff due to certain changes in the independent variable cannot be predicted with certainty.

However, it is possible to predict a pattern of values of both the

variables by their probability distribution. Example : Probabilistic

inventory models are used the conditions of uncertain demand to

decide the economic ordering quantity (EOQ). A game theory

where saddle points or equilibrium points of the player does not

exists, we apply probabilistic model.

CLASSIFICATION BASED ON SOLUTION PROCEDURE

Analytical Models : These models have a specific

mathematical structure and problems can be solved by

running specific solution procedures. Any optimization

model (which requires maximization or minimization of an

objective function) is an analytical model.

Example :

A general linear programming problem.

Special structured transportation and assignment problem.

Simulation Models : These models also have a mathematical

structure but are not solved by applying mathematical

techniques to get a solution. Instead, a simulation model is

essentially a computer assisted experimentation on a

mathematical structure of a real life problem in order to

describe and evaluate its behaviour under certain

assumptions over a period of time.

LIMITATIONS OF OPERATIONS RESEARCH

Operation Research has certain limitations. However, these

limitations are mostly related to the problems of model

building and the time and money factors involved in its

application rather than its practical utility.

Some of them are as follows:

MAGNITUDE OF COMPUTATIONS

O.R tries to find out optimal solution taking into account all the

factors. In the modern society these factors are enormous

and expressing them in quantity and establishing

relationships among these are required complicated

calculations which can only be handled by machines.

NON-QUANTIFIABLE FACTORS

O.R provides solution only when all elements related to a

problem can be qualified. All relevant variables do not

lend themselves to quantification. Factors which

cannot be quantified, find no place in O.R.

GAP BETWEEN MANAGER AND OPERATIONS RESEARCHER

O.R being specialist’s job requires a mathematician or a

statistician, who might not be aware of the business

problems. Similarly, a manager fails to understand the complex working of O.R. Management itself may offer

a lot of resistance due to conventional thinking.

MONEY AND TIME COSTS

When the basic data are subjected to frequent changes,

incorporation them into the O.R models is a costly affair.

Moreover, a fairly good solution at present may be

more desirable than a perfect O.R solution available

after sometime.

IMPLEMENTATION

Implementation of decisions is a delicate task. It must take

into account the complexities of human relations and

behaviour. Sometimes resistance is offered only due to

psychological factors.

SELECTION OF TECHNIQUE

Operations Research techniques are very useful but they

cannot be used indiscriminately. Choice of technique

depends upon the nature of problem, operating

conditions, assumptions, objectives, etc. Thus,

identification and use of an appropriate technique is

essential.

NOT A SUBSTITUTE OF MANAGEMENT

Operations Research only provides the tools and cannot be

a substitute of management. It only examines the results

of alternative courses of action and final decision is made

by management within its authority and judgment.

SUB- OPTIMISATION

Sub- optimisation is deciding in respect of a relatively

narrow aspect of the whole business situation or

optimisation of a sub- section of the whole.

Functional heads some times, without taking care of

wider implications, sub- optimise their functions. This

may cause loss in that part of the organisation which

is left out of the exercise and as such should be

avoided.

THANK YOU