The document presents an introduction to operations research, defining it as applying mathematical modeling to complex problems in business, industry, and government. It discusses the history and development of operations research, its objectives like improving efficiency and decision making, and the scope and methods used in operations research modeling including analytical, trial and error, and simulation approaches. The presentation provides an overview of operations research including its definition, objectives, modeling approaches, and applications in various fields.

1. PRESENTATION ON
“ INTRODUCTION
TO
OPERATIONS RESEARCH”

2. Presented by :
MD SARWAR KAMAL
Student of IIUC ,BBA
+8801815806291

3. Introduction
The term Operations Research was first introduced in 1940 by McClosky and
Trefthen
Defination
Operations Research is the applications of the methods of mathematical
science to complex problems arising in the direction and management of
large systems of men, machine, materials and money in industry, business,
government and defence. The distinctive approach is to develop a scientific
model of systems incorporating measurements of factors such as chance and
risk with which to predict and compare the outcomes of alternative
decisions, strategies or controls. The purpose is to help management
determine its policy and actions scientifically.

4. History of OR
It can be divided into 3 parts
• PRE- world war II development
• During world war II development
• Post world war II development

5. Objectives of OR
• To improve productivity and efficiency
• To maximize profit and minimize cost
• To help decision making and improve its quality
• To Improve objectivity of analysis and clarity of
thought
• To Identify the most suitable optimum solution
• To Integrate the system as a whole
• To be successful in competitions and market
leadership

6. Scope of OR
area application
finance Dividend policy making, investment and budgeting portfolio
analysis, cash flow analysis, credit risk etc
accounting Cash flow planning, credit policy analysis, planning of
delinquent account strategy
marketing Advertising and media planning; product selection, timing,
competitive action; recruitment of salesmen
construction Allocation of resources to projects; determination of proper
workforce; deployment of workforce
Research and
development
Control of R & D projects; product introduction planning;
organizational design and control etc
Purchasing and
procurement
Bidding polices, vendor analysis, replacement polices,
optimal buying and reordering, material transfer
manufacturing Employment, training, layoffs, quality control, assembly line,
blending etc

7. Models of OR
By structure it is of 3 types
1. Physical or Ionic model : it looks like what it
represents. Example- A photograph and painting are
ionic models of persons or objects
2. Analogue or schematic model: it shows
interrelationship between two or more parameters.
Example- histogram, frequency table, flow chart etc
3.Symbolic or Mathematical model : it shows a set of
mathematical symbols like as letters, numbers etc.
Example – equations or inequalities

8. By nature of environment it is of 2
types
1. Deterministic model : it assumes conditions
of complete certainty and perfect
knowledge. Example- Linear programming,
transportation and assignment models
2. Probabilistic model : it handles situations in
which the payoff of managerial actions
cannot be predicted with certainty. Example
– insurance companies

9. Methods of OR
There are 3 methods
1. Analytical method : it depends on classical steps and
techniques in mathematics like use of differential calculus,
integration , set, matrix and coordinate geometry.
Examples – EOQ and graphical solution for the product
mix through linear programming.
2. Trial and error method: when analytical method
fails then trial and error method is used. Here certain
algorithm is developed. One starting point is the initial
solution, which is first approximation . it is repeated with
a certain set of rules until optimum solution is reached.
Example- simplex method of linear programming

10. 3.Simulation method : solutions of problems solved using
principles of statistics, sampling and probability is called
simulation method
Example – Monte Carlo Simulation

11. Phases/steps of OR• Problem identification :
problem must be precisely and concisely defined.
Not only the problem is defined but also uses, objectives
and limitations of study are stressed in the light of the
problem
• Model building :
In this step Parameters or uncontrollable inputs of problem
must be recognized
Decision variables or controllable inputs specified
And the objective and constraints expressed quantitatively
• Solution of the model : mathematical
model developed at earlier stage is solved through
scientific methods
solutions must be the objective functions as well as
constraints

12. • Validation of the model :
Valid if it gives a reliable for a set of inputs under the given
conditions.
Validation is possible for a limited period of time.
Essential to check validity of model time to time
• Report generation : report must
be generated to convey the model’s solution
Contain a statement of the problem, the assumptions
made and indication of general approach to solution
summary of recommendation based on the model’s results
should be stated

13. Advantages of OR
• It provides a logical and systematic approach
to the problem
• Indicates the scope as well as limitations of
the problem
• Helps in finding avenues for new research and
improvements in a system
• Makes overall structure of the problem more
comprehensive.

14. Limitations of OR
• Should never be considered in absolute terms
in any case
• Provides solutions only when all elements
related to problem can be quantified
• Information gap between 2 parties
• Basic data subject to frequent changes.
Incorporating them into the OR is a costly
affair

15. Conclusion