Answer Key
- What are the fundamental definitions of “Modelling” and “Simulation”? Modelling is the process of creating and constructing a representation of a real system, including its properties and workings. Simulation is the act of using that model, operating it over time or space to study the performance of the system and analyze the effects of changes.
- Describe the origins of the Monte Carlo method. The Monte Carlo method was developed in 1940 by researchers and physicists, including John von Neumann and Stanislaw Ulan, who were working on the Manhattan project. They created the method as a way to study neutron scattering.
- What is the difference between Verification and Validation in the context of simulation? Verification is the process of comparing a simulation program to the conceptual model to ensure the model’s implementation and data are consistent with the developer’s specifications. Validation is the process of comparing the simulation model’s results with the real system’s measurements within a specific experimental context to ensure the model is an accurate representation.
- What are the two primary types of input variables used in simulation analysis? The two types of input variables are decision variables and uncontrollable variables. Decision variables are controlled by the programmer, while uncontrollable variables are random variables that cannot be controlled.
- List three distinct advantages of using Modelling and Simulation. Modelling and Simulation make it easy to understand how a complex system operates without working on the real system. They also make it easy to test changes and their effects on output, and they allow for bottleneck analysis to identify constraints that cause delays.
- Identify one major disadvantage of Modelling and Simulation and explain why it is a challenge. Designing a model is considered an art that requires significant domain knowledge, training, and experience. This is a challenge because it makes the process highly dependent on skilled personnel and is not a simple, mechanical task.
- How do system state variables behave in a discrete-event model versus a continuous-event model? In a discrete-event model, the system state variables remain constant over intervals of time and only change at specific points called event times. In a continuous-event model, the system state variables change continuously over time, typically defined by the results of differential equations.
- What is the role of the “Simulation Executive” in a discrete event simulation? The Simulation Executive is a key feature of discrete event simulation that is responsible for controlling the advancement of time within the model. It is also in charge of executing the discrete events in the proper sequence.
- Describe the key characteristics of a Monte Carlo simulation. A Monte Carlo simulation is a computerized mathematical technique characterized by three features: its output must generate random samples, its input distribution must be known, and its result must be known while performing the experiment. It is often applied to problems involving risk analysis and uncertain decisions.
- In the context of a queuing system, what does a “Multi Server Queue” entail? A Multi Server Queue is a system that consists of multiple identical servers and a single common queue for all incoming items. When an item requests service, it is allocated to any available server; if all servers are busy, the item joins the common queue.