6.0 Comparative Synthesis and Selection Guide
This section consolidates the preceding analysis into a direct, at-a-glance comparison of the three methodologies. The purpose of this framework is to provide clear guidance for selecting the most appropriate simulation technique based on the fundamental nature of the problem at hand.
| Criterion | Discrete-Event Simulation | Continuous Simulation | Monte Carlo Simulation |
| Handling of Time | State changes occur at discrete, separate points in time (event-driven). | State changes occur continuously over time as a smooth function. | Static and time-independent; models a single point in time. |
| Core Mechanism | Processing a sequence of events, managing queues and resources. | Solving systems of differential equations to track state variables. | Repeated random sampling from probability distributions. |
| Primary Use Case | Analyzing queuing systems, resource contention, and event-driven workflows. | Modeling physical, biological, or engineering systems governed by continuous laws. | Conducting risk analysis and quantifying the impact of uncertainty on a decision. |
| Nature of Output | Performance metrics like wait times, queue lengths, and resource utilization. | A plot or dataset showing the value of system state variables over time. | A probability distribution of potential outcomes (e.g., project cost, ROI). |
This table provides a high-level guide to inform the final, practical recommendations that follow.