Agent-Based Queueing Simulation
Also known as: AB-QS, Agent-Based Queue Simulation, ABM Queueing, Agent Queue Simulation
Agent-Based Queueing Simulation (AB-QS) combines agent-based modeling with queueing theory to simulate systems where autonomous, decision-making entities interact through waiting lines and service points. Each entity (patient, customer, job) is modeled as an independent agent with its own state and behavioral rules, enabling richer, more realistic dynamics than classical queueing models alone.
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When to use it
Use AB-QS when the system has heterogeneous entities with distinct behavioral rules (balking, reneging, priority classes) that cannot be captured by homogeneous queueing assumptions, or when emergent phenomena (cascading congestion, fairness collapse) are of interest. Ideal for healthcare patient flow, airport security lanes, call-center staffing, and supply-chain bottleneck analysis. Do NOT use when all entities are statistically identical and simple analytical queueing formulas (M/M/1, M/M/c) are sufficient — the added complexity is unjustified. Also avoid when data on agent behavioral parameters (patience distributions, routing preferences) are unavailable, as uncalibrated behavioral rules can produce misleading results.
Strengths & limitations
- Captures heterogeneous entity behavior (balking, reneging, priority) that classical queueing models cannot represent.
- Reveals emergent system-level phenomena arising from local agent interactions without assuming global equations.
- Supports what-if policy testing by directly modifying agent rules and queue configurations.
- Scales naturally to multi-stage, multi-queue systems with complex routing and feedback loops.
- Produces rich, entity-level output enabling fairness and equity analysis across agent subgroups.
- Requires detailed specification of agent behavioral rules, which demands domain expertise and empirical calibration data.
- Computationally expensive compared to analytical queueing formulas; large-scale systems may require high-performance computing.
- Model validation is challenging — it is difficult to confirm that emergent behavior matches real-world dynamics without extensive observational data.
- Results depend on the number of replications and random seed management; poor experimental design inflates variance.
Frequently asked
How does AB-QS differ from standard discrete-event simulation?
Standard DES models the system from a global process perspective and treats entities as passive. AB-QS gives each entity autonomous decision-making capability — agents actively respond to queue state (balking, reneging), which can produce qualitatively different system dynamics.
How many replications are needed?
A minimum of 30 replications is a common rule of thumb for initial estimates. For stable confidence intervals on rare events (e.g., extreme congestion), 100–1000 replications may be needed. Use a pilot study to estimate variance and determine the required sample size via confidence interval width criteria.
Can AB-QS be used for optimization, not just analysis?
Yes. AB-QS is often coupled with metaheuristics (genetic algorithms, simulated annealing) or response-surface methods to optimize queue configuration parameters (number of servers, scheduling rules) while honoring agent behavioral constraints. This is sometimes called simulation-based optimization.
What software supports agent-based queueing simulation?
AnyLogic is the most widely used platform, with native support for both agent-based and queueing libraries in a single model. Arena, Simul8, and SIMIO support queueing, while NetLogo and Mesa (Python) support agent-based modeling; combining both paradigms typically requires AnyLogic or custom code.
When should I prefer a classical M/M/c formula over AB-QS?
Use analytical queueing formulas when entities are homogeneous, arrivals are Poisson, service times are exponential, and you need a quick steady-state estimate. AB-QS is warranted when those assumptions fail — heterogeneous behavior, non-Markovian service, or transient (non-steady-state) analysis.
Sources
- Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162. DOI: 10.1057/jos.2010.3 ↗
- Agent-based model. Wikipedia. link ↗
How to cite this page
ScholarGate. (2026, June 3). Agent-Based Queueing Simulation. ScholarGate. https://scholargate.app/en/simulation/agent-based-queueing-simulation
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Agent-based Discrete-Event SimulationSimulation↔ compare
- Agent-Based ModelingSimulation↔ compare
- Discrete-Event SimulationSimulation↔ compare
- MONTE-CARLO-SIMULATIONDecision-making↔ compare
- Queueing SimulationSimulation↔ compare