Simulation in Operation Research

  • Post last modified:29 July 2022
  • Reading time:17 mins read

Simulation is a representation of real-life situations. It is a method in which a replica of a real-world process or system is developed over a period of time. The simulated model acts in the same manner as the selected physical or abstract process or system behaves in reality.

For example, aircraft pilots are given training through simulation models, because training with real aircrafts can incur huge costs as well as involve various risks.

Similarly, in the education sector, teachers are trained in the simulated models of students (a group of individuals who imitate as students). This avoids the risk of spoiling the future of students if the teacher is not able to teach properly. Therefore, simulation models have proved to be useful for training purposes.

Category of Simulation

Simulation used for training purposes is divided into three categories, which are as follows:

Live simulation

It refers to a simulation in which equipment is used to imitate a real system. For example, testing the battery of a car with the help of an electrical tester.

Virtual simulation

It refers to a simulation in which real people operate on simulated systems. For example, a pilot flying a simulated jet.

Constructive simulation

It refers to a simulation in which simulated people operate on simulated systems. For example, in war games, the players supervise soldiers and equipment, which move around the board.

From the discussion so far, it can be understood that simulation models are the most appropriate for uncertain situations. By using simulation, an analyst can introduce constants and variables related to a problem, develop possible courses of action and establish measures of effectiveness. Simulation helps an analyst in determining how a system would respond under given conditions. However, it does not generate optimal solutions. Therefore, an analyst has to generate solutions for what he/she wants to test.

Reasons for Using Simulation

As discussed earlier, simulation is a quantitative tool for business decision making. Moreover, it is useful in situations when experimentating with a real system is risky and expensive.

Apart from training purposes, simulation is also used in various other fields, such as science and technology, business decision making and defence operations. For example, in organisations, simulation models are used to understand various business processes and take decisions accordingly. Different organisations use different types of simulation models based on their requirements.

Simulation is applicable for solving problems in various areas, such as police dispatching, selection of location for emergency vehicles (ambulance and fire brigade), inventory control and financial planning. Apart from this, simulation can also be used for portfolio selection, capital budgeting and production process scheduling. In military operations, it is used for designing weapon systems. Some of the important applications of simulation are mentioned below:

Simulation in the Education Sector

Simulation is widely used for educational purposes. Simulation models are used to create a real-world environment in a classroom that helps students to understand various key concepts. In business schools, these models are used for management games and to perform experimentation with different business strategies in a risk-free environment and conduct case study discussions.

Apart from this, social simulation models are used to demonstrate social and political processes in various subjects such as anthropology, economics, history, political science and sociology. For example, in civics simulations, students assume to behave in a simulated society. Similarly, in international relations simulations, students are engaged in negotiations, formation of alliances and cross-border trade.

Simulation in the Medical Sector

In the medical sector, simulation models are developed to teach therapeutic and diagnostic procedures and various medical concepts. Moreover, these models are used to make decisions related to the recruitment of healthcare personnel.

Simulation models are also developed for providing training on blood draw, laparoscopic surgery and trauma care. They are also used for designing devices for biomedical engineering problems, and new therapies and treatments.

Simulation in the Entertainment Sector

In the entertainment sector, simulation has been proved to be very effective in various fields, which are as follows:

  • Computer and video games: It refers to simulation games that represent a real environment. These games represent interactions between playable characters and environment realistically.

    Nowadays, simulation games are popular among people of all age groups. Some of the popular simulation games are The Sims, Command and Conquer, SimCity, Black and White, Tiger Woods PGA Tour and Tony Hawk’s Pro Skater.


  • Film: It refers to one of the most important applications of simulation, in the field of entertainment. Simulation models are used to provide 3D computer graphics and special visual effects in films.

    In this way, these models are used for producing high-quality films and controlling visual effects. Some of the films in which simulated models have been used are Finding Nemo, Live Free or Die Hard, 300, Up, Iron Man, Wall-E, etc.


  • Theme park rides: It refers to one of the most rapidly growing fields of entertainment. Simulator models are used here to record the moves of rides. The first simulated ride was Star Tours, which used a hydraulic motion-based cabin.

    Some of the popular simulated rides are Soarin’ over California, The Amazing Adventures of Spiderman, Mission Space and The Simpsons Ride.

Simulation in Manufacturing

The field of manufacturing has a wide application of simulation models. These models are used for evaluating the effect of capital investments in equipment, plants, warehouses and distribution centres. Simulation models can also be used to determine the performance of the existing and planned system.

The following are some of the main applications of simulation models in manufacturing:

  • Determining the throughput under average and peak loads

  • Calculating system cycle time

  • Making efficient utilisation of resources, such as men, raw material, money and machines

  • Identifying bottlenecks and shortcomings in the production process

  • Solving the problems of queuing in a work environment

  • Identifying staffing requirements

  • Determining the effectiveness of scheduling and control systems

Types of Simulation

Simulation models are used when a real system is not accessible, can be dangerous to engage or has not been built. As discussed earlier, these models are used in various fields, such as safety engineering, testing, training and education.

Simulation models can be classified on the basis of the changes produced in a system and uncertainty in the processes, events and controlling parameters of the system. The two classifications of simulation models are mentioned below:

On the basis of the changes produced in a system: This classification includes the following:

  • Static simulation: It refers to a simulation in which a system does not show any change with time. Static simulation represents a system at a particular point in time.

  • Dynamic simulation: It refers to a simulation in which a system changes and evolves with time. The main objective of dynamic simulation is to estimate the future performance of a system.

On the basis of uncertainty in the processes, events and controlling parameters of a system: This classification includes the following:

  • Deterministic simulation: It refers to a simulation in which parameters are uncertain and are expressed by using a single value. The result of deterministic simulation is in the form of a qualified statement.

  • Probabilistic simulation: It refers to a simulation in which uncertainties are defined very clearly by specifying inputs as probability distribution. The results of probabilistic simulation are a quantified statement.

These aforementioned simulations are used for solving different busi- ness problems and taking decisions accordingly.


Steps of Simulation Process

The steps involved in the process of simulation are explained as follows:

Identifying a problem

Refers to the first step of the simulation process in which the problem is clearly defined and the intended solutions to be achieved are identified. If the problem is defined clearly, it will lead to the development of an appropriate model and provide a basis for the evaluation of simulation results.

Developing a model

Involves formulating a model by considering the problem that is identified in the first step. A simulation model can be a physical or mathematical model, idea or combination.

Collecting data

Involves gathering data for the development and evaluation of the simulation model. The collected data helps in analysing the problem and reaching at an appropriate solution.

Running the simulation model

Refers to one of the most important steps of the simulation process. After developing the simulation model and collecting the relevant data, the next step is to run it. If the model is deterministic (in which all parameters are known and constant), a single run would be sufficient.

On the contrary, if the model is stochastic (in which parameters are subject to random variation), a number of runs would be required to evaluate the performance of the model.

Interpreting the results

Involves analysing the results of the runs. If the model portrays real-life situations, no rectifications will be required. However, if the model is not closer to reality, a number of runs will be required.


Importance and Limitations of Simulation in Management

Simulation is a useful technique, especially in situations where mathematical simplification is not possible. The following are some of the advantages of simulation:

  • Helps an analyst in observing and studying a real-world environment or process that cannot be studied in its actual form due to some reasons, such as high costs and greater risks.

  • Enables an analyst to study processes that are long-term and require more time to display results. For example, for studying trends in the world population in the long run, an analyst cannot wait for the required number of years to see results.

  • Helps in studying the systems that are too disrupting in nature.

However, in the case of complex business problems, a simulation may suffer from various limitations. Some of the limitations of the simulation are as follows:

  • Time consuming: It refers to one of the major drawbacks of simulation. In certain analytical processes, simulation has proved to be time-consuming.

  • Expensive: It implies that a better and more advanced simulation model would be more expensive. Simulation often requires significant use of computers, which increases the cost of electricity. This incurs high costs to organisations.

  • No optimum results: It refers to another major drawback of simulation. Since a simulation model generally deals with uncertainties, optimum results cannot be produced. A simulation model can provide a way of evaluating solutions but does not generate any solutions.

  • Complicated process: Simulation involves a long and complicated process to develop a model. Such a model is unique and developed for obtaining the solution of a specific problem.

Monte Carlo Simulation Using Random Numbers

Monte Carlo simulation is a problem-solving technique that is used to represent the probability of certain outcomes by running a number of trial runs (called simulations) and using random variables.

This technique was introduced by John von Neumann, Stanislaw Ulam and Nicholas Metropolis while they were working on the atomic bomb in the 1940s. These scientists named this technique after the city in Monaco, which is famous for casinos and games of chance. The Monte

The steps involved in the Monte Carlo simulation technique are explained as follows:

Making probability distribution for variables

This step involves estimating a probability distribution for the given vari- able based on the past experience. The distribution can be based on Poisson, binomial or normal distribution.

Building a cumulative probability distribution for each variable

It is one of the most important steps of the Monte Carlo technique. The cumulative distribution of an experiment represents all possible values and probabilities of outcomes.

Setting random number intervals

In this step, after determining the cumulative distribution for each variable, a set of numbers to represent each possible outcome is assigned. This set of numbers is known as random number intervals.

Generating random numbers

A random number is a series of two-digit or three-digit numbers (for example, 00, 01, …, 8 8). A random number can be generated in many ways. For example, if a problem is very complex and involves a number of simulation trials, random numbers can be generated using computers.

Simulating the experiment through random sampling

It involves repeating the process till the required number of simulation runs has been generated.

Application of the Monte Carlo Technique

The following are some areas that have an extensive application of the Monte Carlo technique:

  • Physical science: It is a field in which the Monte Carlo technique has proved to be very effective. The Monte Carlo technique is used in computational physics, physical chemistry and molecular modelling. Apart from this, it is also used for designing detectors and analysing their behaviour.

  • Finance: It is another area where the Monte Carlo technique has been used extensively. The technique is used for evaluating investments in projects and analysing financial derivatives. In such cases, stochastic or probabilistic simulation models are used.

  • Telecommunication: It is an area where the Monte Carlo technique is used to establish an effective wireless network and evaluate its performance. If the desired performance is not achieved, the network design is moving through the optimisation process.

  • Games: Monte Carlo technique is used to design games related to artificial intelligence.

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