The document outlines the fundamentals of operations research (OR), including its history, definition, features, and techniques. It emphasizes the application of scientific methods to optimize decision-making in various fields such as management, production, and healthcare. Additionally, it discusses the decision-making process, highlighting both quantitative and qualitative approaches and the importance of modeling in problem-solving.

1. Jimma University
College of Business and Economics
Department of Management
Tesfaye H. (MBA)
2013
Operations Research (OR)

2. Chapter One: Introduction
Chapter objectives
After completing this unit, you will be able to:
• Understand the history of operations
research.
• Meaning and definition of OR
• Features of OR
• Identify application areas of
operations research
• Significance of operations research.
• OR techniques
• Quantitative Analysis and Decision Making
• Models and Model Building
• Limitations of OR
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3. Introduction To OR/MS
• Operations Research (OR): is a science which deals with problem,
formulation, solution and finally appreciate decision making.
• This science is basically concerned with optimizing maxima and minima of
objective functions involved.
• E.g. maxima could be profit, performance and yield
minima could be loss and risk
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4. History of OR
• Started in Great Britain during WWII
• Failure of mission was very high.
• scientists and technocrats formed a team to study the problems arising out of
different situations.
• 1940s: the term of OR get more prominence when research was carried out on the
design and analysis of mathematical models for military operations.
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5. Contd…
• Till 1950s OR confined to military operations,
• 1950: OR began to develop in Industrial filed in USA.
• 1951: the first book “Methods of Operations Research” published.
• 1952: the Operations Research Society of America came into being.
• 1957: IFORS established at Oxford
• Since then the OR/MS/DS/SS has become more applicable in all
management aspects of a system, product and service.
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6. What is OR?
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Operations
• The activities carried out in an organization.
Research
• The process of observation and testing characterized by the scientific method.
Situation, problem statement, model construction, validation, experimentation,
candidate solutions.
Model
• An abstract representation of reality. Mathematical, physical, narrative, set of
rules in computer program.

7. Meaning and Definition of OR
• OR is the application of a scientific approach to solving management problems
in order to help managers make better decision (Bernard W. Taylor III, 2006).
• OR: is the application of scientific methods by inter-disciplinary teams to solve
problems involving the control of organized (man-machine systems) so as to
provide solutions which best serve the purposes of the organization as a whole
(Ackoff and Sasieni 1968).
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8. • From the concepts and definition given above, Operations Research is:
1. The application of scientific methods, techniques and tools to solve a problem
2. A management tool in the hands of a manager to take a decision.
3. A scientific approach to decision-making process.
4. An “applied research” aiming at finding a solution for an immediate problem facing a
society, industry or a business enterprise. This is not “fundamental research”.
5. A decision-oriented research, using scientific methods, for providing management a
quantitative basis for taking decision regarding operations under his/her control.
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9. FEATURES OF OR
(i) Decision-making
(ii) Scientific Approach
(iii) Inter-disciplinary Team Approach
(iv) System Approach
(v) Reduces complexity by use of computers
(vi) Helpful in improving the quality of solution
(vii) Use of models
(viii) Require willing executives
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10. 10
The procedure to be followed in the study of OR, generally involves the following
major phases.
1. Formulating the problem
2. Constructing a mathematical model
3. Deriving the solution from the model
4. Testing the model and its solution ( updating the model)
5. Controlling the solution
6. Implementation

11. Application Areas of Operations Research
• Forecasting
• Production Scheduling
• Inventory Control
• Capital Budgeting
• Transportation
• Plant location
• Human Resource Management
• Advertising and sales research
• Maintenance and Repair
• Accounting procedures
• Packaging
• Natural Resource Management
• Research and Development
• Health Care
• Quality Control
• Equipment Replacement, etc.
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There are so many application areas of operations research; to mention some of the
most widely known areas:

12. Significance of Operations Research
• Provides a tool for scientific analysis
• Provides solution for various business problems
• Enables proper deployment of resources
• Helps in minimizing waiting and servicing costs
• Enables the management to decide when to buy and how much to buy?
• Assists in choosing an optimum strategy
• Renders great help in optimum resource allocation
• Facilitates the process of decision making
• Helps a lot in the preparation of future managers
• Optimizing plant revenues
• Improving the efficiency of a production line
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13. The commonly used techniques include
1. Allocation models :
• Linear programming
• Non-linear programming
• Transportation models
• Assignment models
• Integer programming
• Goal programming
• Dynamic programming
2. Inventory models
3. Replacement models
4. Network models
5. Waiting- line models(Queuing theory)
6. Simulation
7. Sequencing models
8. Decision theory
9. Game theory
10. Markov models
11. Regression and correlation 13
Techniques of OR

14. Quantitative Analysis & the Decision Making Process
• In order to understand the role of quantitative analysis in managerial type of
problems, it is better to have a look at the decision making process.
• Decision Making: is the process of selecting a feasible course of action from a set
of alternative, so as to solve problems.
• The decision making process is initiated by a problem.
• The intention of the manager, when making a decision, is to solve that problem.
• In doing so, the manager first makes an analysis of the alternatives.
• There are two forms of analysis— qualitative and quantitative.
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15. Managerial
Problem
Quantitative analysis based
upon mathematical
techniques
Summary &
evaluation Decision
Qualitative analysis
based upon managerial
experience and judgment
Figure 1.1 The Decision Making Process
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Decision Making Process

16. Steps of decision making
1. Identify and define the problem;
2. Determine the set of alternative solutions;
3. Determine the criteria to evaluate alternatives;
4. Analyze the alternatives;
5. Select the best alternative/make the decision;
6. Implementing the solution;
7. Establishing a control, follow up and evaluation system;
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17. Cont…
• In qualitative analysis, intuition and the manager’s subjective judgment and
experience are used.
• This type of problem solving is more an art than a science.
The qualitative approach is usually used when:
• The problem is simple
• The problem is familiar
• The costs involved are not so great
• Immediate decisions are needed 17

18. Cont…
The quantitative approach is used when:
• The problem is complex
• The problem is not familiar
• The costs involved are substantial
• Enough time is available to analyze the problem
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19. Cont….
 Both the quantitative and qualitative analyses of a problem provide
important information for the decision maker.
 quantitative analysis tend to be more objective than those based on
a purely qualitative analysis.
 For this reason OR makes use of quantitative models.
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20. Problem Definition
Model Construction
Analysis (Model
Solution)
Implementation &
Follow-up
The management science approach to analyze and solve the problem involves:
Figure 1.2 The management science approach
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21. Models and model building
• Model is a theoretical abstraction(approximation) of a real-life problem.
• In OR, the problem is expressed in the form of a model.
• A management science model is an abstract representation of an
existing problem situation.
• It can be in the form of a graph or chart, but most frequently a
management science model consists of a set of mathematical
relationships.
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22. Cont…..
There are certain significant advantages gained when using a model.
 Problems under consideration become controllable through a model
It provides a logical and systematic approach to the problem
It provides the limitations and scope of an activity
It helps to eliminate duplications
It helps in finding solutions for research and improvements in a system.
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23. Classification of Models
• The classification of models is a subjective problem. They may be
distinguished as follows:
Models by function
Models by degree of abstraction
Models by structure
Models by nature of an environment
Models by the extent of generality
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24. 1. Models based on Function/purpose:
A. Descriptive Models: These models use surveys , questionnaire results, inference of
observations to describe the situation. Ex. Plant Layout diagram. Block diagram of an
algorithm.
B. Predictive Models: These models are the results of query: “ What will follow if
this occurs or does not occur?”. Ex. Preventive Maintenance Trouble Shooting chart or
procedures.
C. Normative or Optimization Models: These models are designed to provide optimal
solution to the problem subject to a certain limitations or constraints on use of
resources. Ex. LP Problem
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25. 2. Models based on Structure and Abstraction :
A. Iconic or Physical Models (It is also called Static Model): are pictorial representations of real systems
 These models are easy to observe and describe but are difficult to manipulate.
 E.g. the structure of an atom, layout drawing of factory, model of an airplane etc.
B. Analog Models:
 They are more abstract than iconic models.
 These models are less specific, less concrete but easier to manipulate than iconic models.
 Abstract models mostly showing inter and intra relationships between two or more parameters.
 For example It may show the relationship between an input with that of an output.
 For instance; histogram, frequency table, cause-effect diagram, flow charts, Gantt charts etc.
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26. Cont…..
C. Mathematical or Symbolic Models:
• They are most abstract in nature.
• Here, a set of relations is represented in the form of mathematical equations
• Its function is more explanatory than descriptive.
Example:
1. (x + y) 2 = x2+2xy+y2
2. Max. Z=3000x1 +2500x2
Subject to: 2x1+x2 < 40
x1+3x2 < 45 x1 and x2 are decision variables.
x1 < 12
x1 , x2 > 0 26

27. 3. Models based on certainty/ Nature of an Environment :
(1)Deterministic Models: all the parameters of decision variables are constants
and their functional relationship are known with certainty.
Eg. LP, Integer programming etc.
(2) Probabilistic or Stochastic Models: This is the model in which at least one
of the decision variable or parameter is random in nature.
Eg. Queuing theory, decision analysis etc.
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28. 4. Models by Extent of Generality
• These models can be categorized in to:
A. Specific Models: when a model presents a system at some specific time
B. General Models: are models applicable to all situations without time
bound. Simulation and Heuristic models fall under this category.
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29. Limitations of OR
• The inherent limitations concerning mathematical expressions
• High costs are involved in the use of OR techniques
• OR does not take into consideration the intangible factors
• OR is only a tool of analysis and not the complete decision-making process
• Other limitations
– Bias
– Internal resistance
– Competence
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30. Thank you for you
Attention!!!!!!!!!!!!
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