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User Guide

Learn how to use the Fleet Decision Platform to optimize your fleet operations.

Overview

The platform helps you:

  • Forecast demand across multiple locations
  • Optimize fleet allocation to minimize costs
  • Assess risks for your vehicle fleet
  • Understand decisions through explainability features

Workflow

graph LR
    A[Prepare Data] --> B[Configure]
    B --> C[Generate Forecasts]
    C --> D[Run Optimization]
    D --> E[Review Results]
    E --> F[Take Action]

Getting Started

  • Demand Forecasting

    Predict future demand across your service locations.

    Forecasting Guide

  • Fleet Optimization

    Generate cost-optimal fleet allocation plans.

    Optimization Guide

  • Risk Assessment

    Identify high-risk vehicles in your fleet.

    Risk Guide

  • Understanding Results

    Interpret optimization outputs and KPIs.

    Results Guide

Quick Example

Here's a typical workflow:

from src.utils.config import load_config
from src.forecasting import DemandPredictor
from src.optimization import CascadingOptimizer

# 1. Load configuration
config = load_config()

# 2. Generate demand forecasts
predictor = DemandPredictor(config)
forecasts = predictor.predict(features, horizon_days=7)

# 3. Run optimization
optimizer = CascadingOptimizer(config)
result = optimizer.optimize(
    demand_forecast=forecasts,
    fleet_state=fleet_state,
    network_costs=network_costs
)

# 4. Review results
print(f"Total Cost: ${result.total_cost:,.2f}")
print(f"Demand Coverage: {result.kpis['demand_coverage']:.1%}")

Use Cases

Daily Fleet Rebalancing

Optimize vehicle distribution across locations based on predicted demand:

  1. Generate next-day demand forecasts
  2. Run optimization with current fleet state
  3. Execute rebalancing plan

Weekly Planning

Plan fleet operations for the upcoming week:

  1. Generate 7-day forecasts
  2. Identify capacity constraints
  3. Schedule maintenance windows
  4. Generate daily rebalancing plans

Risk-Based Maintenance

Prioritize vehicle maintenance based on risk scores:

  1. Calculate risk scores for all vehicles
  2. Identify high-risk vehicles
  3. Schedule preventive maintenance
  4. Update fleet state

Best Practices

Data Quality

Ensure your input data is:

  • Complete: No missing values in critical fields
  • Recent: Use the latest available data
  • Accurate: Validate against ground truth

Configuration

  • Start with default settings
  • Tune parameters based on results
  • Document any custom configurations

Monitoring

  • Track forecast accuracy over time
  • Monitor optimization KPIs
  • Alert on anomalies

Next Steps

Choose your focus area: