Queuing analysis: Is it appropriate to use Monte Carlo simulation for queueing problems that resist closed-form mathematical analysis?

Introduction / Context:
Many real-world queues include balking, reneging, complex service rules, or non-Markovian times. Analytical solutions may be intractable; simulation becomes the practical alternative.

Given Data / Assumptions:

• Arrival/service processes may be general (non-exponential) or state-dependent.
• Networked or priority queues complicate analysis.
• Goal is performance estimation (e.g., waiting time, queue length, utilisation).

Concept / Approach:
Monte Carlo simulation samples random arrivals and services from specified distributions and tracks the system over time, estimating steady-state or transient metrics with statistical confidence.

Step-by-Step Solution:

Verification / Alternative check:
When analytical formulas are unavailable, validated simulation provides accurate, decision-grade insights.

Why Other Options Are Wrong:
“Disagree” would deny simulation’s central role in complex queuing analysis.

Common Pitfalls:
Poor random number generation, inadequate warm-up, or too-short runs causing biased estimates.

Final Answer:

Agree