Simulation

Simulation is an analytical method that enables the modeling and observation of a system’s or process’s behavior as if it were operating under real-world conditions. In other words, it is a process that allows experimentation on a mathematical, logical, or physical model of a real system.

The primary purpose of simulation is to understand how a system behaves in real life, evaluate its performance, and anticipate potential outcomes under various scenarios. In this regard, simulation is an effective tool for understanding, planning, and optimizing complex systems.

Modeling and simulation are two complementary processes: modeling involves creating an abstract representation of the system, while simulation refers to running this model under dynamic conditions. This allows the system to be analyzed without direct intervention.

History

The concept of simulation traces its origins back to humanity’s early experimental thinking. However, its first scientific applications date to the mid-20th century. During World War II, simulation-based tactical studies were conducted to test possible military scenarios.

In the 1950s, with the advancement of computer technology, simulation became a mathematical discipline, giving rise to probability-based approaches such as Monte Carlo methods. In the 1960s and 1970s, computer-aided simulation techniques began to be applied in engineering, manufacturing, traffic flow, and economics.

From the 1980s onward, the widespread adoption of personal computers led to the use of simulation software (e.g., GPSS, SIMAN, Arena) in research and industry; by the 2000s, it had become integrated with virtual reality (VR), augmented reality (AR), and digital twin technologies.

Today, simulation is used both as an educational tool and a decision-support instrument in fields such as healthcare, education, aviation, energy management, and the defense industry.

Relationship Between Modeling and Simulation

According to sources, modeling is the creation of an abstract representation (model) that captures specific aspects of a system. Simulation, on the other hand, is the process of running this model over time to examine the system’s possible behaviors.

This relationship can be summarized as follows:

• Modeling defines “how the system works.”
• Simulation shows “what outcomes result from its operation.”

For example, modeling patient flow in a hospital involves defining parameters such as bed capacity, number of doctors, and waiting times. Simulation on this model allows testing system performance under varying levels of demand.

This approach is both economical and safe, as it enables analysis without interfering with the actual system.

Types of Simulation

Simulations are classified into various categories based on their purpose and methodology.

1. Discrete Event Simulation (DES): Models in which system events occur at specific points in time. Used in production lines, logistics, and service systems. Each event (e.g., a customer’s arrival, a machine breakdown) happens at a distinct moment.
2. Continuous Simulation: Processes in which system variables change continuously over time. Applied in chemical engineering, physical system modeling, and energy management.
3. Hybrid Simulation: A combination of discrete and continuous simulation. For instance, in a production line, machine movements may change continuously while product transitions are represented as discrete events.
4. Real-Time Simulation: A type of simulation that operates synchronously with a physical system. Used in flight and driving simulators and for testing robotic systems.
5. Physical (Hardware-Based) Simulation: Applications that use physical equipment or mannequins to replicate real environments. Particularly preferred in medical and engineering education.

Simulation Process

According to modeling and simulation literature, a simulation study consists of the following stages:

1. Problem Definition: The objective and system boundaries are established.
2. Model Development: A logical or mathematical structure representing the system is constructed and validated.
3. Validation and Verification: The model’s ability to adequately represent the real system is tested.
4. Simulation Execution: The model is run under various scenarios.
5. Result Analysis: The generated data is statistically evaluated and interpreted to support decision-making.

These stages are widely accepted as a standard methodology in both education and industrial applications.

Simulation in Education and Healthcare

In health sciences, simulation plays a critical role in nursing, medical, and emergency response training. It enables students and healthcare professionals to acquire skills in a safe environment before interacting with real patients. These applications include:

• Scenario-based training: Simulation of clinical situations based on real cases.
• Patient simulators: Use of electronic mannequins that mimic human physiological responses.
• Virtual reality environments: VR-based training for surgical and medical procedures.

Simulation-supported education reduces errors, enhances communication and teamwork, and improves patient safety. As a result, it is widely used in both undergraduate programs and professional development initiatives.

Engineering and Industrial Applications

In engineering, simulation is used to analyze system performance, reduce costs, and optimize production processes.

• Production Systems: Efficiency is improved by testing different machine layouts or capacity scenarios.
• Traffic and Transportation: The impact of signal timings, vehicle density, and road designs is analyzed.
• Energy Systems: Behavior of electrical grids, renewable energy sources, and heating distribution networks is modeled.
• Defense and Aviation: Safe testing environments are provided through flight simulators and military strategy analysis.

Software used in these applications typically relies on mathematical optimization, system dynamics, and discrete event simulation principles.

Advantages

According to sources, the main advantages of simulation can be grouped under the following headings:

• Safe Experimental Environment: Testing can be conducted without risking the real system.
• Cost Savings: Virtual models replace physical prototypes or field trials.
• Time Efficiency: Long processes can be analyzed in a short time.
• Improved Education Quality: Provides opportunities for hands-on learning.
• Decision Support: Enables prediction of outcomes under various scenarios in management and engineering.

Limits and Challenges

While simulation systems have many strengths, they also have certain limitations:

• Model Complexity: Creating a model that fully represents the real system is difficult.
• Data Accuracy: Incorrect or incomplete input data can lead to misleading results.
• Cost: Advanced software and hardware systems can be expensive.
• Expertise Requirement: Requires knowledge of modeling, statistics, and programming.

Therefore, simulation should not be viewed as an exact replica of the real system; rather, it is recommended as a decision-support tool.

Future of Simulation and Emerging Trends

Simulation technologies are increasingly integrated with digital twins, artificial intelligence, cloud computing, and augmented reality systems. In industrial applications especially, the digital twin approach enables the creation of a digital copy of a physical system and continuous simulation on that copy.

AI-supported simulations learn from historical data to generate scenarios, creating a new field known as predictive simulation in healthcare and manufacturing.

Simulation is an indispensable tool for analyzing complex systems, education, and decision-making processes. Its ability to conduct experiments without disrupting real systems has made it a vital method in scientific research and education. With advances in computer technology, simulation is no longer merely an analytical technique—it has become a fundamental component of virtual education, digital manufacturing, and system optimization.