Classification of Models in Operations Research
Introduction
Mathematical models are the foundation of Operations Research (OR). Every Operations Research study begins with the development of a model that represents a real-world system or decision problem. Since actual business, industrial, transportation, healthcare, and engineering systems are often too complex, costly, or risky to study directly, analysts use models to simplify reality while preserving its essential features.
Different decision-making problems require different types of models. Some models are used to describe a system, some predict future outcomes, while others recommend the best course of action. Similarly, certain models assume complete certainty, whereas others explicitly consider uncertainty and risk.
For this reason, Operations Research models are classified into different categories based on their structure, purpose, environment, behaviour, and method of solution. Understanding these classifications helps students and managers select the most appropriate model for a particular decision problem.
In this article, we discuss the Classification of Models in Operations Research in simple, student-friendly language with practical examples based on standard Operations Research textbooks.
What is the Classification of Models in Operations Research?
The classification of models in Operations Research refers to the systematic grouping of mathematical models according to their characteristics, purpose, behaviour, assumptions, or solution methods.
Since no single model is suitable for every decision-making problem, different classifications help analysts understand the nature of a model and determine where it can be applied effectively.
A clear understanding of model classification is essential for selecting the appropriate modelling technique and obtaining meaningful solutions.
Why are Models Classified?
Models are classified because different real-world problems have different characteristics. A suitable classification helps analysts:
- Select the appropriate model for a particular problem.
- Understand the assumptions of each model.
- Choose a suitable solution technique.
- Improve the accuracy and reliability of decisions.
- Compare different modelling approaches easily.
Thus, model classification forms an important part of the Operations Research modelling process.
Classification of Models in Operations Research
Operations Research models are commonly classified into the following categories:
- Classification by Structure
- Classification by Purpose
- Classification by Nature of Environment
- Classification by Behaviour
- Classification by Method of Solution
- Classification by use of Digital Computers
Each classification highlights a different characteristic of an Operations Research model.
1. Classification of Models by Structure
Based on their structure, Operations Research models are classified into:
- Iconic Models
- Analogue Models
- Symbolic (Mathematical) Models
§1.1 Iconic Models
Definition
What are iconic models? An Iconic model represents the system as it is by scaling it up or down (i.e., by enlarging or reducing the size). In other words, it is an image.
An Iconic Model is a physical representation of a real object or system in which the appearance is preserved while the size may be enlarged or reduced.
In other words, an iconic model looks similar to the real object but is constructed on a different scale.
For example, a toy aeroplane is an iconic model of a real one. Other common examples of iconic models are photographs, drawings, maps etc.
A model of an atom is scaled up so as to make it visible to the naked eye. In a globe, the diameter of the earth is scaled down, but the globe has approximately the same shape as the earth, and the relative sizes of continents, seas, etc., are approximately correct.
Explanation
Iconic models are the simplest and most concrete type of model. They help people visualize the physical characteristics of a system without studying the actual object.
These models mainly represent the shape, appearance, or physical structure of the original system rather than its mathematical or functional behaviour.
Since iconic models do not describe complex relationships among variables, they are generally used for illustration, communication, and visualization instead of optimization or prediction.
In short,
The iconic model is usually the simplest to conceive and the most specific and concrete. Its function is generally descriptive rather than explanatory. Accordingly, it cannot be easily used to determine or predict what effects many important changes on the actual system.
Examples
- A globe representing the Earth.
- A toy aeroplane representing a real aircraft.
- An architectural model of a building.
- A scale model of a bridge.
- Maps, engineering drawings, and photographs.
Advantages
- Easy to understand.
- Simple to construct.
- Helps visualize physical objects.
- Useful for demonstrations and presentations.
Limitations
- Cannot analyse mathematical relationships.
- Not suitable for optimization.
- Limited ability to predict system behaviour.
Exam Tip: Remember that an Iconic Model preserves appearance, not mathematical relationships.
§1.2 Analogue Models
Definition
What is the analog model in operation research? The models, in which one set of properties is used to represent another set of properties, are called analogue models. After the problem is solved, the solution is reinterpreted in terms of the original system.
An Analogue Model represents one system by another system that behaves in a similar manner, even though the two systems may look completely different.
Instead of preserving physical appearance, an analogue model preserves the functional or behavioural relationship between variables.
For example, Graphs, are very simple analogues because the distance is used to represent the properties such as time, number, percent, age, weight, and many other properties.
Contour lines on a map represent the rise and fall of the heights. In general, analogues are less specific, less concrete but easier to manipulate than our iconic models.
Explanation
Analogue models are more abstract than iconic models. They use one measurable property to represent another property of the real system.
Unlike iconic models, analogue models need not resemble the original object physically. Their usefulness lies in demonstrating similar behaviour or relationships.
Because analogue models are easier to manipulate than physical models, they are widely used to study trends, relationships, and system behaviour.
Examples
- A graph representing the relationship between sales and time.
- Contour lines on a topographic map representing land elevation.
- A thermometer representing temperature.
- A pressure gauge representing fluid pressure.
- An electrical circuit used to represent another physical system.
Advantages
- Easier to analyse than physical models.
- Clearly shows relationships among variables.
- Useful for studying system behaviour.
Limitations
- Does not physically resemble the original system.
- May require careful interpretation.
- Accuracy depends on how well the analogue represents the actual system.
Exam Tip: Students often confuse Iconic and Analogue models. An iconic model resembles the object, whereas an analogue model resembles the behaviour.
§1.3 Symbolic (Mathematical) Models
Definition
A Symbolic (Mathematical) Model is a model that represents a real-world system using mathematical symbols, variables, equations, inequalities, and functions.
It describes the relationships among decision variables mathematically so that the problem can be analysed and solved using appropriate mathematical techniques.
In other words,
The symbolic or mathematical model is one that employs a set of mathematical symbols (i.e., letters, numbers, etc.) to represent the decision variables of the system. These variables are related together by means of a mathematical equation or a set of equations to describe the behaviour (or properties) of the system. The solution to the problem is then obtained by applying well-developed mathematical techniques to the model.
The symbolic model is usually the easiest to manipulate experimentally and it is most general and abstract. Its function is more often explanatory rather than descriptive.
Explanation
The symbolic or mathematical model is the most important type of model in Operations Research. Unlike iconic and analogue models, it does not attempt to represent the physical appearance of a system. Instead, it represents the logical and mathematical relationships among the variables involved in the problem.
In a mathematical model, the decision variables, objective function, and constraints are expressed using mathematical expressions. Appropriate Operations Research techniques are then applied to obtain the best possible solution.
Most optimization problems in Operations Research are solved using symbolic models because they are flexible, precise, and suitable for computer-based analysis.
Examples
- Linear Programming Model
- Transportation Model
- Assignment Model
- Inventory Model
- Queuing Model
- Network Model
- Dynamic Programming Model
Advantages
- Represents complex systems accurately.
- Suitable for mathematical analysis and optimization.
- Easy to modify when assumptions or data change.
- Can be solved efficiently using computer software.
- Widely used in scientific decision-making.
Limitations
- Requires reliable quantitative data.
- May oversimplify certain real-life situations.
- Qualitative human factors are often difficult to represent mathematically.
Exam Tip: Most Operations Research techniques, such as Linear Programming, Transportation, Assignment, Inventory, and Queuing Theory, are based on symbolic (mathematical) models.
§ Comparison of Structural Models
| Feature | Iconic Model | Analogue Model | Symbolic (Mathematical) Model |
|---|---|---|---|
| Representation | Physical appearance | Functional behaviour | Mathematical relationships |
| Nature | Concrete | Semi-abstract | Highly abstract |
| Main Purpose | Visualization | Behaviour analysis | Decision-making and optimization |
| Easy to Understand | Very High | High | Moderate |
| Mathematical Analysis | No | Limited | Extensive |
| Computer Applications | Rare | Limited | Very Common |
| Typical Examples | Globe, Toy Aircraft | Graphs, Maps | Linear Programming, Inventory Models |
2. Classification of Models by Purpose
Models can also be classified according to the purpose for which they are developed.
Based on their purpose, Operations Research models are classified into:
- Descriptive Models
- Predictive Models
- Prescriptive Models
§2.1 Descriptive Models
Definition
A Descriptive Model explains or describes the characteristics of an existing system without attempting to predict future events or recommend decisions.
In other words,
A descriptive model simply describes some aspects of a situation based on observations, surveys, questionnaire results or other available data.
For example, the result of an opinion poll represents a descriptive model.
Explanation
Descriptive models summarize observed data and help managers understand the current situation. These models focus on presenting facts, relationships, or patterns rather than identifying the best course of action.
They are commonly used for reporting, data analysis, surveys, and performance measurement.
Examples
- Population census reports
- Customer satisfaction surveys
- Sales reports
- Opinion polls
- Statistical summaries
Advantages
- Simple to understand.
- Clearly presents existing information.
- Supports further analysis.
Limitations
- Does not predict future outcomes.
- Does not recommend decisions.
- Limited usefulness for optimization.
Exam Tip: A descriptive model answers the question "What is happening?" rather than "What should be done?"
§2.2 Predictive Models
Definition
A Predictive Model estimates future outcomes by analysing historical data and relationships among variables.
Such models can answer ‘what if’ type of questions, i.e. they can make predictions regarding certain events.
For example, based on the survey results, television networks such models attempt to explain and predict the election results before all the votes are actually counted.
Explanation
Predictive models help managers estimate what is likely to happen under specified conditions. They use existing information, statistical methods, probability, or mathematical relationships to forecast future events.
Although predictive models reduce uncertainty, they cannot eliminate it completely because future events are never known with absolute certainty.
Examples
- Demand forecasting
- Sales forecasting
- Weather forecasting
- Population growth prediction
- Forecasting inventory requirements
Advantages
- Supports future planning.
- Reduces uncertainty.
- Helps managers prepare for expected situations.
Limitations
- Depends heavily on data quality.
- Forecasts may become inaccurate when conditions change.
- Predictions are estimates rather than guarantees.
Exam Tip: Predictive models answer "What is likely to happen?"
§2.3 Prescriptive Models
Definition
Finally, when a predictive model has been repeatedly successful, it can be used to prescribe a source of action. For example, linear programming is a prescriptive (or normative) model because it prescribes what the managers ought to do.
A Prescriptive Model recommends the best course of action from among the available alternatives in order to achieve a specific objective.
Explanation
Prescriptive models are the most important category of models in Operations Research because they not only analyse a problem but also identify the optimal or most appropriate decision.
These models use mathematical optimization techniques to determine the best solution while satisfying the given constraints. Depending on the nature of the problem, the objective may be to maximize profit, minimize cost, reduce time, improve efficiency, or utilize resources effectively.
Unlike descriptive models, which explain existing situations, and predictive models, which estimate future outcomes, prescriptive models answer the question of what decision should be made.
Most optimization techniques in Operations Research belong to this category.
Examples
- Linear Programming
- Integer Programming
- Transportation Model
- Assignment Model
- Goal Programming
- Network Optimization Models
Advantages
- Identifies the best possible decision.
- Supports scientific and objective decision-making.
- Improves resource utilization.
- Helps achieve organizational objectives efficiently.
Limitations
- Requires accurate data and realistic assumptions.
- Results depend on the validity of the mathematical model.
- Some qualitative factors cannot always be included.
Exam Tip: A prescriptive model answers the question "What should be done?"
§ Comparison of Models by Purpose
| Feature | Descriptive | Predictive | Prescriptive |
|---|---|---|---|
| Main Objective | Describe the current situation | Predict future outcomes | Recommend the best decision |
| Decision Support | Very Limited | Moderate | High |
| Future Forecasting | No | Yes | Yes, along with optimization |
| Optimization | No | No | Yes |
| Typical Examples | Surveys, Reports | Forecasting Models | Linear Programming, Transportation Models |
3. Classification of Models by Nature of Environment
According to the nature of the operating environment, Operations Research models are classified into:
- Deterministic Models
- Probabilistic (Stochastic) Models
The classification depends on whether the problem involves certainty or uncertainty.
§ 3.1 Deterministic Models
Definition
A Deterministic Model is a model in which all input data, parameters, and relationships are assumed to be known with certainty.
Such models assume conditions of complete certainty and perfect knowledge. For example, linear programming, transportation and assignment models are the deterministic types of models.
Explanation
In deterministic models, every parameter has a fixed value. There is no randomness or uncertainty in the system. Therefore, if the same input data are used repeatedly, the model always produces the same result.
These models are appropriate when all relevant information is available and remains unchanged during the analysis.
Many classical Operations Research techniques are deterministic because they assume complete knowledge of costs, resource availability, capacities, and other parameters.
Examples
- Linear Programming
- Transportation Problem
- Assignment Problem
- Classical Network Models
- Deterministic Inventory Models
Advantages
- Simple to formulate and solve.
- Produces consistent results.
- Suitable when accurate data are available.
Limitations
- Ignores uncertainty.
- May not accurately represent real-life situations where conditions change unexpectedly.
Exam Tip: In deterministic models, all parameters are known with certainty before the analysis begins.
§ 3.2 Probabilistic (Stochastic) Models
Definition
A Probabilistic (Stochastic) Model is a model in which one or more parameters are uncertain and are represented using probability distributions.
These types of models usually handle such situations in which the consequences or payoff of managerial actions cannot be predicted with certainty. However, it is possible to forecast a pattern of events, based on which managerial decisions can be made.
For example, insurance companies are willing to insure against the risk of fire, accidents, sickness and so on, because the pattern of events has been compiled in the form of probability distributions.
Explanation
Many real-world systems involve uncertainty. Customer arrivals, machine failures, demand, service time, and market conditions often vary randomly and cannot be predicted with complete certainty.
Probabilistic models explicitly incorporate this uncertainty by using probability theory and statistical methods. Instead of providing a single certain outcome, these models estimate the likelihood of different possible outcomes.
Such models help managers make informed decisions even when future events are uncertain.
Examples
- Queuing Models
- Markov Models
- Probabilistic Inventory Models
- Reliability Models
- Decision Analysis under Risk
Advantages
- Represents real-world uncertainty more realistically.
- Improves decision-making under risk.
- Supports probability-based planning.
Limitations
- Requires probability distributions and historical data.
- More complex than deterministic models.
- Results are probabilistic estimates rather than exact values.
Exam Tip: A stochastic model does not eliminate uncertainty, it incorporates uncertainty into the analysis using probability.
§ Comparison of Deterministic and Probabilistic Models
| Feature | Deterministic Model | Probabilistic (Stochastic) Model |
|---|---|---|
| Nature of Data | Certain | Uncertain |
| Random Variables | Not Included | Included |
| Probability Theory | Not Required | Required |
| Output | Fixed Solution | Probable Outcomes |
| Complexity | Lower | Higher |
| Examples | Linear Programming, Transportation | Queuing, Markov, Reliability Models |
4. Classification of Models by Behaviour
Based on the behaviour of the system over time, Operations Research models are classified into:
- Static Models
- Dynamic Models
This classification depends on whether time is considered in the model.
§4.1 Static Models
Definition
A Static Model is a model that represents a system at a particular point in time. It assumes that the system does not change during the period of analysis.
These models do not consider the impact of changes that takes place during the planning horizon, i.e. they are independent of time. Also, in a static model, only one decision is needed for the duration of a given time period.
Explanation
In a static model, all variables and parameters are assumed to remain constant while the decision is being made. Time is not treated as an explicit variable.
Static models are suitable for problems where only a single decision is required or where changes over time have little effect on the final solution.
Many optimization problems in Operations Research are static because they analyse the system under fixed conditions.
Examples
- Linear Programming
- Transportation Problem
- Assignment Problem
- Simple Product Mix Problems
Advantages
- Easy to formulate and solve.
- Requires fewer assumptions.
- Suitable for one-time decision-making problems.
Limitations
- Ignores changes over time.
- Less suitable for long-term planning.
- Cannot represent continuously changing systems.
Exam Tip: A static model studies the system at one point in time.
§4.2 Dynamic Models
Definition
A Dynamic Model is a model that explicitly considers the effect of time and describes how a system changes over different time periods.
In these models, time is considered as one of the important variables and admits the impact of changes generated by time. Also, in dynamic models, not only one but a series of ‘interdependent’ decisions is required during the planning horizon.
Explanation
In dynamic models, time is an important variable. The decisions made at one stage influence future decisions and future system performance.
Dynamic models are useful when the system evolves over time and a sequence of interrelated decisions is required.
Such models are widely used in long-term planning, inventory control, production scheduling, financial planning, and resource allocation.
Examples
- Dynamic Programming Models
- Multi-period Inventory Models
- Project Planning Models
- Equipment Replacement Models
Advantages
- Represents real-life systems more realistically.
- Supports long-term planning.
- Captures the effect of changing conditions over time.
Limitations
- More difficult to formulate.
- Requires more data.
- Computationally more complex than static models.
Exam Tip: A dynamic model studies how a system changes over time, not just its current state.
§ Comparison of Static and Dynamic Models
| Feature | Static Model | Dynamic Model |
|---|---|---|
| Time Considered | No | Yes |
| Number of Decisions | Usually one | Multiple, interrelated |
| Complexity | Lower | Higher |
| Suitable For | One-time decisions | Sequential decision-making |
| Examples | Linear Programming, Transportation | Dynamic Programming, Multi-period Inventory |
5. Classification of Models by Method of Solution
Based on the technique used to obtain the solution, Operations Research models are generally classified into:
- Analytical Models
- Simulation Models
§5.1 Analytical Models
Definition
An Analytical Model is a mathematical model that can be solved directly using established mathematical or optimization techniques.
These models have a specific mathematical structure and thus can be solved by known analytical or mathematical techniques.
For example, a general linear programming model, as well as the specially structured transportation and assignment models are analytical models.
Explanation
Analytical models have a well-defined mathematical structure. Once the objective function, constraints, and variables are formulated, the solution is obtained using mathematical procedures or optimization algorithms.
Most classical Operations Research techniques belong to this category because they produce an exact or optimal solution whenever the assumptions of the model are satisfied.
Examples
- Linear Programming
- Transportation Model
- Assignment Model
- Network Optimization Models
- Integer Programming
Advantages
- Produces exact or optimal solutions.
- Well-established mathematical procedures are available.
- Efficient for structured decision problems.
Limitations
- Requires a clearly defined mathematical model.
- May become difficult for highly complex systems.
- Some real-world problems cannot be represented analytically.
Exam Tip: Analytical models are preferred whenever an exact mathematical solution is possible.
§5.2 Simulation Models
Definition
A Simulation Model is a mathematical or logical model that imitates the behaviour of a real-world system over time in order to study its performance under different conditions.
They also have a mathematical structure but they cannot be solved by purely using the ‘tools’ and ‘techniques’ of mathematics.
A simulation model is essentially computer-assisted experimentation on a mathematical structure of a real-time structure in order to study the system under a variety of assumptions.
Simulation modelling has the advantage of being more flexible than mathematical modelling and hence can be used to represent complex systems that otherwise cannot be formulated mathematically. On the other hand, simulation has the disadvantage of not providing general solutions like those obtained from successful mathematical models.
Explanation
Simulation does not directly determine the optimal solution. Instead, it reproduces the operation of a system and allows analysts to observe how the system behaves under different assumptions or scenarios.
Simulation is particularly useful when the system is too complex to be analysed using traditional mathematical optimization techniques.
With the help of computers, simulation models can evaluate alternative policies, estimate system performance, and support managerial decision-making.
Examples
- Hospital patient-flow simulation
- Airport passenger-flow simulation
- Manufacturing system simulation
- Supply chain simulation
- Traffic simulation
Advantages
- Can represent highly complex systems.
- Handles uncertainty effectively.
- Allows experimentation without disturbing the real system.
- Useful when analytical solutions are impractical.
Limitations
- Does not necessarily provide an optimal solution.
- Results depend on the quality of the model and input data.
- May require significant computational resources.
Exam Tip: Simulation evaluates alternatives, whereas analytical models directly solve mathematical problems.
§ Comparison of Analytical and Simulation Models
| Feature | Analytical Model | Simulation Model |
|---|---|---|
| Solution Method | Mathematical optimization | Computer-based experimentation |
| Result | Exact or optimal solution (when applicable) | Approximate performance evaluation |
| Complexity Handling | Moderate | Very High |
| Computer Dependence | Moderate | High |
| Best Used When | Exact mathematical formulation is possible | System is too complex for analytical methods |
| Examples | Linear Programming, Transportation | Manufacturing, Hospital, Traffic Simulation |
6. Classification of Models by Use of Digital Computers
The development of the digital computer has led to the introduction of the following types of modelling in OR. Based on the digital computers used to obtain the solution, Operations Research models are generally classified into:
- Analogue and Mathematical models combine
- Function models
- Quantitative models
- Heuristic models
Important Note on this Classification of Models:
§6.1 Analogue and Mathematical models combine
Sometimes analogue models are also expressed in terms of mathematical symbols. Such models may belong to both the types (1.2) and (1.3) in classification 1 above.
For example, a Simulation model is of analogue type but mathematical formulae are also used in it. Managers very frequently use this model to ‘simulate’ their decisions by summarizing the activities of the industry in a scale-down period.
§6.2 Function models
Such models are grouped on the basis of the function being performed.
For example, a function may serve to acquaint scientists with such things as tables, carrying data, a blueprint of layouts, a program representing a sequence of operations (like in computer programming).
§6.3 Quantitative models
Such models are used to measure the observations.
For example, degree of temperature, yardstick, a unit of measurement of length value, etc. Other examples of quantitative models are,
(i) transformation models which are useful in converting a measurement of one scale to another (e.g., Centigrade vs. Fahrenheit conversion scale), and
(ii) the test models that act as ‘standards’ against which measurements are compared (e.g., business dealings, a specified standard production control, the quality of a medicine).
§6.4 Heuristic models
These models are mainly used to explore alternative strategies (courses of action) that were overlooked previously, whereas mathematical models are used to represent systems possessing well-defined strategies.
Heuristic models do not claim to find the best solution to the problem.
In modern books, Heuristic models is also known as Heuristic methods, Heuristic algorithms, or Approximation methods.
Complete Comparison of Model Classifications in Operations Research
The following table summarizes all the major classifications of Operations Research models discussed in this article.
| Basis of Classification | Types | Main Difference |
|---|---|---|
| Structure | Iconic, Analogue, Symbolic (Mathematical) | Based on the way a real system is represented. |
| Purpose | Descriptive, Predictive, Prescriptive | Based on the objective of the model. |
| Nature of Environment | Deterministic, Probabilistic (Stochastic) | Based on certainty or uncertainty in the system. |
| Behaviour | Static, Dynamic | Based on whether time is considered. |
| Method of Solution | Analytical, Simulation | Based on the technique used to analyse or solve the model. |
Common Mistakes Students Make
1. Many students believe that graphs are symbolic models.
This is not correct.
In classical Operations Research, graphs are generally considered analogue models because they represent one quantity through another while preserving the relationship between variables.
On the other hand, Linear Programming equations and Transportation models are symbolic (mathematical) models because they express relationships through mathematical symbols.
2. Students often confuse Predictive and Prescriptive models.
Remember the difference:
- Predictive models estimate what is likely to happen.
- Prescriptive models recommend what should be done.
Prediction alone does not identify the best decision. Prescriptive models go one step further by determining the most appropriate course of action.
3. Students often believe that a dynamic model simply means a difficult model.
This is incorrect.
The defining characteristic of a dynamic model is the explicit consideration of time, not its mathematical complexity.
4. Many students assume that simulation always finds the optimal solution.
This is incorrect.
Simulation is primarily used to study and compare the performance of different alternatives. It helps decision-makers understand system behaviour, but it does not automatically identify the optimal solution as optimization techniques such as Linear Programming do.
Common Misunderstandings
Students often confuse the different classifications of Operations Research models because some terms appear similar. The following points help avoid these common misunderstandings.
1. Confusing Iconic and Analogue Models
Many students think that both models are physical representations.
Remember:
- Iconic Model → Resembles the appearance of the real object.
- Analogue Model → Represents the behaviour or relationship of the system.
2. Confusing Analogue and Symbolic Models
Graphs, maps, and charts are usually analogue models, whereas mathematical equations, inequalities, and optimization formulations are symbolic (mathematical) models.
3. Confusing Predictive and Prescriptive Models
A predictive model estimates what is likely to happen, whereas a prescriptive model recommends what should be done.
Prediction is not optimization.
4. Confusing Deterministic and Probabilistic Models
Deterministic models assume complete certainty, while probabilistic (stochastic) models explicitly consider uncertainty through probability.
5. Confusing Static and Dynamic Models
The difference is time, not difficulty.
- Static Model → Time is ignored.
- Dynamic Model → Time is an explicit variable.
6. Thinking Simulation Always Gives the Optimal Solution
Simulation is primarily used to study system behaviour under different conditions.
It does not automatically determine the optimal solution like Linear Programming or Integer Programming.
7. Assuming Every OR Model is Mathematical
Not every model in Operations Research is purely mathematical.
Iconic and analogue models are also important because they help visualize and understand real-world systems.
Quick Revision
CLASSIFICATION OF MODELS IN OPERATIONS RESEARCH
1. By Structure
• Iconic
• Analogue
• Symbolic (Mathematical)
2. By Purpose
• Descriptive
• Predictive
• Prescriptive
3. By Nature of Environment
• Deterministic
• Probabilistic (Stochastic)
4. By Behaviour
• Static
• Dynamic
5. By Method of Solution
• Analytical
• Simulation
Frequently Asked Questions (FAQs)
1. What is the classification of models in Operations Research?
The classification of models in Operations Research is the systematic grouping of models according to their structure, purpose, environment, behaviour, or method of solution.
2. Why are OR models classified?
Models are classified to help analysts select the most suitable model for solving different types of decision-making problems.
3. What are the main classifications of OR models?
The major classifications are:
- By Structure
- By Purpose
- By Nature of Environment
- By Behaviour
- By Method of Solution
4. Which model is most commonly used in Operations Research?
The Symbolic (Mathematical) Model is the most widely used because it forms the basis of most optimization techniques.
5. What is the difference between an iconic model and an analogue model?
An iconic model resembles the physical appearance of the real object, whereas an analogue model represents its functional behaviour or relationships.
6. What is the difference between a descriptive and a predictive model?
A descriptive model explains the current situation, while a predictive model estimates future outcomes.
7. What is a prescriptive model?
A prescriptive model recommends the best possible decision among available alternatives.
8. What is a deterministic model?
A deterministic model assumes that all parameters are known with certainty.
9. What is a stochastic model?
A stochastic (probabilistic) model incorporates uncertainty by using probability distributions.
10. What is the difference between static and dynamic models?
Static models ignore time, whereas dynamic models explicitly consider changes over time.
11. What is an analytical model?
An analytical model is solved using established mathematical or optimization techniques.
12. What is a simulation model?
A simulation model imitates the behaviour of a real system to study its performance under different conditions.
13. Does simulation always provide the optimal solution?
No. Simulation helps evaluate system performance and compare alternatives but does not necessarily determine the optimal solution.
14. Which classification is most important in Operations Research?
All classifications are important because each describes a different characteristic of a model and helps in selecting the appropriate modelling approach.
15. Why are symbolic models considered the most important?
Because most Operations Research techniques, including Linear Programming, Transportation, Assignment, Integer Programming, and Goal Programming, are based on symbolic (mathematical) models.
Conclusion
Models are the foundation of Operations Research because they provide a simplified representation of complex real-world systems. Since decision problems differ in their objectives, assumptions, and operating conditions, no single model is suitable for every situation. Therefore, Operations Research models are classified according to their structure, purpose, nature of environment, behaviour, and method of solution.
Understanding these classifications enables students, researchers, and managers to select the most appropriate model for a particular problem and apply the correct analytical technique. Among all categories, symbolic (mathematical) models are the most widely used because they support scientific analysis, optimization, and computer-based decision-making.
A thorough understanding of model classification forms the basis for studying advanced Operations Research techniques such as Linear Programming, Integer Programming, Dynamic Programming, Queuing Theory, Inventory Models, Network Models, and Simulation.
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References
- Hamdy A. Taha, Operations Research: An Introduction, 10th Edition, Pearson.
- Frederick S. Hillier and Gerald J. Lieberman, Introduction to Operations Research, McGraw-Hill Education.
- J. K. Sharma, Operations Research: Theory and Applications, Macmillan India.
- Kanti Swarup, P. K. Gupta and Man Mohan, Operations Research, Sultan Chand & Sons.
- H. M. Wagner, Principles of Operations Research, Prentice Hall.
Tags: What are the classification of models in operations research? Classification of Modelling in Operations Research, Classification of Modelling in Operation Research
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