Discrete event simulation skill for process modeling, scenario testing, and optimization
Creates discrete event simulation models for process optimization and scenario analysis.
npx claudepluginhub a5c-ai/babysitterThis skill is limited to using the following tools:
The Process Simulation Modeler skill provides comprehensive capabilities for discrete event simulation. It supports process flow modeling, resource allocation analysis, scenario comparison, and capacity optimization.
skill: process-simulation-modeler
inputs:
model_type: "discrete_event" # discrete_event | continuous | agent_based
process_flow:
- step: "Arrival"
distribution: "exponential"
rate: 10 # per hour
- step: "Processing"
distribution: "normal"
mean: 5
std_dev: 1
- step: "Inspection"
distribution: "uniform"
min: 2
max: 4
resources:
- name: "Operator"
quantity: 2
- name: "Inspector"
quantity: 1
simulation_parameters:
run_length: 480 # minutes
replications: 30
warm_up: 60 # minutes
outputs:
- simulation_model
- performance_metrics
- utilization_statistics
- queue_analysis
- scenario_comparison
- recommendations
| Distribution | Use Case | Parameters |
|---|---|---|
| Exponential | Arrival times | Mean |
| Normal | Processing times | Mean, Std Dev |
| Triangular | Limited data | Min, Mode, Max |
| Uniform | Equal probability | Min, Max |
| Lognormal | Repair times | Mean, Std Dev |
| Weibull | Equipment life | Shape, Scale |
| Metric | Definition | Target |
|---|---|---|
| Throughput | Units per time period | Maximize |
| Cycle Time | Time through system | Minimize |
| WIP | Work in process | Minimize |
| Utilization | Resource busy % | 70-85% |
| Queue Length | Entities waiting | Minimize |
| Wait Time | Time in queue | Minimize |
For uncertainty analysis:
1. Define input distributions
2. Run many iterations
3. Collect output distributions
4. Calculate confidence intervals
5. Identify risk factors
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