- Define the basic modeling and simulation terminology.
- Understand the difference between a model and a simulation.
- Describe how models and simulations are used.
A strategy for simplifying a problem by identifying the essential features of a real system and representing them in a different form.
A mental or cognitive process resulting in the selection of a course of action among several alternative scenarios.
A methodical, trial-and-error procedure used to understand how changes in the input or operating environment affect a result.
A physical, mathematical, or logical representation of something that exists in the real world.
A technique in which unimportant details are removed in an effort to define simpler relationships.
An approach for solving real-world problems that usually involves mathematical equations and a computer-generated solution.
A set of interacting pieces that respond to inputs according to a set of rules that govern the resulting behavior.
An engineering discipline that focuses on the way different parts of a system should coordinate and interact to achieve a desired goal.
A methodical procedure used to understand the differences between a simulation and the real world.
Teaching and learning with the goal of improving real-world performance under both normal and extreme conditions.
Check Your Understanding
- Is modeling and simulation a new approach to solving problems or was it used before computers?
- It is easy to recognize that modeling and simulation is used in computer games, but is it used in fields other than entertainment?
- Models and simulations have been traditionally used by engineers to design systems, such as airplanes and spacecraft – are there other uses for simulation?
- Are models and simulations used only by engineers and scientists, or can teachers, managers, and financial analysts also use them?
Simulation is a multidisciplinary approach to solving problems that includes mathematics, engineering, physical science, social science, computing, medical research, business, economics, etc. Simulation is not new; it dates back to the beginnings of civilization, where it was most commonly used in warfare. With the development of computers, simulation moved from role-playing, where people represented the systems of interest, to computer-based, where software is developed to encode algorithms that represent the systems of interest. While once referred to simply as “simulation,” the discipline is now more commonly called “modeling and simulation” (M&S, or MODSIM). While once considered an enabling tool of mostly engineers and scientists, M&S is now considered a discipline of study and research on its own. Students can receive undergraduate and graduate degrees in Modeling and Simulation.
Introduction to Terminology
There are a number of definitions of models, simulations, and modeling and simulation (M&S). The definitions published by the U.S. Department of Defense (DoD) in their online glossary are as follows:
A model is a physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process.
A simulation is a method for implementing a model over time.
Modeling and Simulation is the discipline that comprises the development and/or use of models and simulations.
Although the terms “modeling” and “simulation” are often used as synonyms, within the discipline of M&S both are treated as individual and equally important concepts.
Modeling is a purposeful abstraction of reality. The real world is too complex to be fully understood by humans. We want to select from the real world those elements that form a reasonable or adequate approximation of the world required for the purpose at hand. This is accomplished through the process of simplification and abstraction. Simplification is an analytical technique in which unimportant details are removed in an effort to define simpler relationships. Abstraction is an analytical technique that establishes the essential features of a real system and represents them in a different form. The resultant model should demonstrate the qualities and behaviors of real world system that impact the questions that the modeler is trying to answer.
A model is characterized by three essential attributes: reference, purpose, and cost-effectiveness. A model has a “referent,” some real or imagined system. A model should have some cognitive “purpose” with respect to its referent; it is impossible to evaluate or intelligently use a model without understanding its purpose. It should be more “cost-effective” to use the model for this purpose than to use the referent itself; it may be impossible to use the referent directly, or using the referent would be dangerous, inconvenient, or expensive. For example, one cannot go to Mars to test a Mars rover that is going to Mars – there must be a model or several models developed on Earth of the referent (the actual rover).
The execution of a model over time is understood as the simulation. Simulation, according to Shannon (1975), is “the process of designing a model of a real system and conducting experiments with this model for the purpose either of understanding the behavior of the system or of evaluating various strategies (within the limits imposed by a criterion or set of criteria) for the operation of the system.” There are many different types of computer-based simulations. Some of the most common approaches include discrete-event, continuous system, agent-based, and system dynamics (these will be described later in the chapter). A common feature they share is generating or predicting an artificial time-history of the system, allowing the observer or experimenter to draw inferences concerning the operating characteristics of the real system that is represented.
Uses of Models and Simulations
Why modeling and simulation? One reason is affordability. Experimentation in real life can be costly – would you rather simulate a staff reduction in a manufacturing plant or actually reduce staff to see the impact of your decision? Models and simulations also support feasibility and safety. With a simulation, you can simulate conditions of hazardous or closed areas, assess capabilities yet to be developed, and speed up the passage of time. One reason models and simulations are becoming more important in recent years is the ability to distribute them across multiple people and multiple geographic areas, made possible by the ever-increasing bandwidth of the internet. Large numbers of people or unique resources can interact together while they are physically apart from each other in geographical locations. For complex problems that touch many disciplines, allowing this cooperation and collaboration is often key to solving the problem. Lastly, M&S is used to solve problems with no closed-form solution. Many science and physics textbooks provide equations to predict the behavior of physical systems. However, these equations are typically derived for ideal situations. Most real-life science and engineering problems cannot be solved using closed-form solutions to equations out of textbooks. For example, most undergraduate physics books provide equations to predict the motion of a simple projectile. These equations assume there is no air resistance. As the effects of air resistance are included, the problem becomes unsolvable using simple projectile equations. The only way to predict the motion of a projectile and account for air resistance is by modeling and simulation using numerical techniques.
Now that we know what benefits models and simulations provide, let’s look at a few examples that illustrate how they are used in the real world.
Systems Engineering: A Wise Investment
Simulations can be used to design and implement a system effectively. They can be used to explore feasibility of new concepts, specify requirements for a system, design specific parts and how they should be manufactured, and identify maintenance issues of the system related to repair and replacement. For example, you can simulate the impact a new sub-system or component might have in your overall system. Once you determine it will have the desired impact, you can use simulation to design the actual sub-system or component. For example, a city designer could use simulation to determine the impact of changing an intersection from a stoplight to a traffic circle. Once they determine the traffic circle will have a positive impact on traffic flow, they can use simulation to design the size (or diameter) of the traffic circle, the angle of entry needed to enter the circle from surrounding roads, and size of the center island. Another example would be an automotive engineer using simulation to determine the impact of a new brake design. If the new brake system is determined to improve safety, simulation can be used to design the specific components that will be part of the brake system.
M&S makes it possible to evaluate many aspects of a proposed design change and can save a project valuable resources by eliminating the need to build and test a full-scale prototype. This is very important, because once the design of a system is completed and is ready for manufacturing, changes and corrections can be extremely expensive. Simulation can also help estimate how much a system will cost and how a company can reduce its costs. Consider the brake design example above. A model can be used to estimate how much it will cost to produce the new brake system, by including the cost of materials, labor, and retooling the manufacturing and/or assembly lines. Once the cost is known (or estimated), the company can use the model to compare different scenarios, for example the cost to produce the new brakes using material x versus material y. A simulation study can cost substantially less than what would be needed to actually design or redesign a system. In other words, it would cost less to develop a simulation of a new brake system than to develop the actual (real) brake system to see whether it has a positive impact on safety. Since the cost of a change or modification to a system after installation is so great, simulation is a wise investment.
Experimentation: Understand “Why” and Explore “What If” Possibilities
Simulations can be used to experiment: you can construct a model of the system and examine how the behavior of the entire system changes when an aspect of the system is changed. This will help expose undesired phenomena within the system and help design a more robust system. You can compress or expand time in a simulation to speed up or slow down phenomena so they can be investigated. For example, you can examine an entire manufacturing shift in a matter of minutes, or spend two hours examining all the events that occurred during one minute of simulated activity. You can also construct a model to answer questions to help plan and avoid unnecessary delays. For example, what if a specific machine is removed from service for an extended period of time? What if demand for service increases by 10 percent? The ability to explore these options through simulation is unlimited.
One of the greatest advantages of using simulation is that once you have developed a valid simulation model, you can explore new policies, operating procedures, or methods without the expense and disruption of experimenting with the real system. A deeper understanding of the cause and effect relationships in the system also helps you develop new or improved ideas to manage the system more effectively. Modifications are incorporated in the model, and you observe the effects of those changes on the computer rather than the real system.
Improved Decision Making: Diagnose Problems and Identify Constraints
Simulation is commonly used to support decision making by answering questions such as, “How complex a system is needed?” and “Which alternative is best?” Many real world systems are very complex - so complex that it is impossible to consider all the interactions taking place in one given moment. Simulation allows you to better understand the interactions, diagnose problems, and gain insight into what affects performance of the overall system. For example, production bottlenecks give manufacturers headaches. Bottlenecks are an effect rather than a cause, and simulation can be used to discover the cause of the delays in system. For example, the bottleneck could be the result of a lack of available materials, a poorly designed workflow, or attributed to a specific person (or people) that are part of the manufacturing process.
Simulation modeling has been proven an effective tool for managers to understand the effects of their decisions on the important performance metrics of the system. The cause-and-effect relationships obtained by running the simulation models increase the effectiveness of the decision-makers. Making good decisions regarding process and policy changes, schedule changes, priority changes, and addition and deletion of resources are examples of where management can gain insight from M&S. For example, accurate costing of products is an important activity within a company; it is a key requirement for identifying the organization’s profitability. Managers can use simulations to explore the impact of cost and availability of different materials on the overall schedule of the system being developed.
Testing and Training: Train the Team
Simulation is often used to support the two distinct areas of testing and training. Testing (and evaluation) is focused on understanding if the system works as expected and if it will help in the real world. It is a process by which a system or components are exercised and results analyzed to provide performance-related information. For example, a test of a car engine’s performance under different conditions (e.g., weather, terrain) could be conducted in two ways: by connecting it to a real vehicle and testing under real conditions, or by connecting it to a simulated vehicle testing under simulated conditions. In the real vehicle you would have to pay the cost of the fuel and possibly put an operator in danger if the design is not stable. In the simulated world, the engine could be tested under a wide range of simulated conditions, saving operating costs of the vehicle and keeping people out of harms way1.
In training we want to ensure the system is used correctly and that we prepare operators for rare emergency situations. Putting the operator in a simulation of the real system first enables them to learn by their mistakes and learn to operate better. An example is learning to fly a plane. In many professional flight schools, initial training is conducted with a simulator. As the student becomes familiar with basic aircraft handling and flight skills using the simulation, more emphasis is placed on learning the actual cockpit instruments. This is less expensive and less disruptive than putting someone in the real aircraft for on-the-job training. Simulation allows a pilot to train for maneuvers or situations that may be impractical or dangerous to perform in the aircraft, while keeping the pilot and instructor in a low-risk environment.
In any case, it is important to remember that while both test/evaluation and training can be done to an extensive degree using modeling and simulation, these simulation scenarios still are only approximations (often very good ones!) of the real world.
Communication: Develop Understanding and Build Consensus
Many people operate with the philosophy that talking loudly and writing complex reports will convince others that their system design is valid. Often, these designs are based on someone’s thoughts about the way the system operates rather than on real analysis. Simulation studies help you avoid these problems by providing an understanding of how a system really operates rather than one person’s opinion about how a system will operate.
Using simulation to present design changes creates an objective opinion. Simulations can be used to come up with the optimum system design to provide the most desirable results, whether it be increasing production or reducing the waiting time for service. System designers and planners have much more confidence accepting reliable simulation results, which have been modeled, tested, validated, and visually represented, as opposed to accepting one person’s opinion and personal prediction of the behavior of the system.
Although modeling and simulation (M&S) can use models that are physical, computational, or a combination of the two, computational M&S is interested in models that can be implemented using computer software. While modeling is more focused on the conceptual understanding of a problem space, simulation is more focused on executing the model using software. In other words, modeling relies on simplification and abstraction to develop an adequate model, and simulation is implemented to carry that model’s behavior forward in time. Today, models and simulations are indispensable for solving many real-world problems. The applications of modeling and simulation are ubiquitous in the 21st century. Modeling and simulation is used for design, test and evaluation, decision making, and training in areas such as health and medicine, manufacturing, planetary and space exploration, transportation, construction, entertainment, defense, and educational systems.
Further Reading / Supplemental Links
- David Goldsman, A Simulation Course For High School Students, Proceedings of the Winter Simulation Conference, 2007.
- DoD Modeling and Simulation (M&S) Glossary (http://www.msco.mil/MSGlossary.html)
- Jerry Banks, Introduction to Simulation, Proceedings of the Winter Simulation Conference, 2000.
- Modeling & Simulation Primer (http://www.corporatepress.com/clientfiles/ntsa/)
- Ricki Ingalls, Introduction to Simulation, Proceedings of the Winter Simulation Conference, 2011.
- Robert Shannon, Systems Simulation: The Art and Science, Prentice-Hall, 1975.
- Simulation (http://en.wikipedia.org/wiki/Simulation)
Points to Consider
- How do you develop a model or simulation?
- What are the important steps to follow – is there a process to follow that can help?
- When are important decisions made about how much detail to include, what data is needed, and what software to use?
1 In some engineering disciplines, this is also called a “hot bench” simulation.