Introduction of Or

8/3/2019 Introduction of Or

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CHARACTERISTICS CONSTITUING THE NATURE OF OR

Interdisciplinary team approach

Operation research is done by a team of scientists

drawn from various discipline such as mathematics,

statistics, economics, engineering, physics etc. Each

member of the OR team is benefited from the

viewpoint of other and collaborative study gives a

solution with great chance of acceptance by

management.


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System Approach OR emphasizes on the overall approach to the system.

This characteristics of OR is often referred to as

system orientation. The orientation is based on the

observation that in the organized systems thebehavior of any part ultimately has some effect on

every other part. In OR., an attempt is made to take

account of all the significant effects and to evaluate

them as a whole. OR thus considers the total systemfor getting the optimum decisions


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Helpful in improving the quality of solution Operation research cannot give perfect answers or

solutions to the problem. OR simply helps in improving

the quality of the solution but does not result in

perfect solution.


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Scientific method: Operation research involves scientific and systematic

approach of complex problems to arrive at the

optimum solution. It uses techniques of scientific

research.


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Goal oriented optimum solution Operation research tries to optimize a well defined

function subject to given constraints and as such is

concerned with the optimization theory.


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Use of models: Operation research uses models built by quantitative

measurement of the variables concerning a given

problem and also derives a solution from the model

using one or more of the diversified solutiontechniques. A solution may be extracted from a model

either by conducting experiments on it or by

mathematical analysis.


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Requires willing executives: OR does require the willingness on the part of the

executive for experimentation to evaluate the costs

and the consequences of the alternative solutions of

the problem. It enables the decision maker to beobjective in choosing an alternative from among many

possible alternatives.


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Reduce complexity: OR tries to reduce the complexity of business

operations and does help the executive in correcting a

troublesome function and to consider innovations

which are too costly and complicated to experimentwith the actual practice. In view of this above, OR

must be viewed both as a science and as an art.


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MODELS AND MODELING IN OPERATION

RESEARCHES

A model in OR is a simplified representation of anoperation or is a process in which only the basic

aspects or the most important features of a typical

problem under investigation are considered. The

objective of a model is to identify significant factorsand interrelationships.


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A good model possess the following characteristics: It should be capable of taking into account, new

formulation without having any changes in its frame.

Assumption made in the model should be as small as

possible.

Variable used in the model must be less in number

ensuring that it is simple and coherent.

It should not take much time in its construction for anyproblem.


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ADVANTAGES OF A MODEL

Problems under consideration become controllable. It provides a logical and systematic approach to the

problem

It provides the limitations and scope of an activity.

It helps in finding useful tools that eliminate

duplication of methods applied to solve problems.

It helps in finding solution for research and

improvements in a system.


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CLASSIFICATION OF MODELS

The classification of models is done on the followingbasis:

Models by function

Models by structure

Models by nature of an environment

Models by extent of generality


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MODELS BY FUNCTION

These models consist of Descriptive models

Predictive models and

Normative models


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Descriptive models: They describe facts and relationships among the

various activities of problem. These models do not

have an objective function as part of the model to

evaluate decision alternatives. In these models, it ispossible to get information as to how one or more

factors change as a result of changes in other factors.


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Predictive models: These are used predictive analytics to create

statistical model of future behavior.

A predictive model is made up of a number of

predictors, which are variable factors that are likely to

influence future behavior or results. In marketing, for

example, a customer's gender, age, and purchase

history might predict the likelihood of a future sale.


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Normative models: Prescriptive model which evaluates alternative

solutions to answer the question, "What is going

on?" and suggests what ought to be done or how

things should work according to an assumption or

standard. In comparison, a descriptive model

merely describes the solutions without evaluating

them.U

sed mainly as a standard for measuringchange or performance.


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MODELS BY STRUCTURE

Iconic or physical models: They are pictorial representations af real systems and

have the appearance of the real thing. Eg: city maps,

blue prints, globe etc. These models are easy to

observe and describe, but are difficult to manipulateand are not very useful for the purpose of prediction.


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Analog Models: The model in which one set of properties is used to

represent another set of properties are called analog

models. After the problem is solved, the solution is

reinterpreted in terms of the original system. Thesemodels are less specific, less concrete, but easier to

manipulate than ionic models.


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Mathematical or symbolic models: They are most abstract in nature. They employ a set of

mathematical symbols to represent the components

of real system. These variables are related together by

means of mathematical equations to describe thebehavior of system.


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MODELS BY NATURE OF AN

ENVIRONMENT

Deterministic models: They are those in which all parameters and functional

relationships are assumed to be known with the

certainty when the decision is to be made. Linear

programming and break-even models are the exampleof deterministic models.


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Probabilistic or stochastic models: These models are those in which atleast one

parameter or decision variable is random variable.

These models reflect to some extent the complexity of

the real world and the uncertainty surrounding it.


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MODELS BY THE EXTENT OF GENERALITY

Specific models: When a model presents a system at some specific

time, it is known as specific model, if the time factor is

not considered, they are termed as static models..

General models:

Simulation and Heuristic models fall under the

category of general models. These models are used to

explore alternative strategies.

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Introduction of Or