Fleet Repair & Vehicle Loss Minimization

⚠️ Warning: This project is currently under development. Features may be incomplete, data sources may be placeholders, and functionality is not fully tested. Use for learning or experimentation only — do not rely on it for production decisions.

Overview

The Fleet Repair & Vehicle Loss Minimization Idea is a decision-support tool designed to help manage vehicles and fleets by determining whether to keep repairing a vehicle or sell it before repair costs exceed its value.

It uses Monte Carlo simulations to model uncertainty in repair costs, fuel/operating expenses, seasonal events, and vehicle-specific factors (type, mileage, age). This allows data-driven decisions for fleet maintenance and asset management.

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Features

• Loss-Minimization Decision: Determine SELL or KEEP for individual vehicles.
• Monte Carlo Simulation: Handles uncertainty in repair costs, fuel, and events.
• Seasonal & Regional Adjustments: Includes weather, disasters, and parts delays (tailored for DFW).
• Vehicle-Specific Adjustments: Accounts for mileage, vehicle type, and depreciation.
• Fleet Management: Supports multiple vehicles.
• Risk Profiling: Outputs expected loss, min/max, and standard deviation.
• Extensible Design: Add vehicle types, seasonal events, or regional adjustments.

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InputsFuture Enhancements

Fleet-wide batch analysis to rank vehicles by expected loss

GPU-accelerated Monte Carlo for large fleets

Seasonal resale value modeling

CLI or web dashboard for visualization

Integration with labor/fuel cost estimators for total operating cost

Input	Description
vehicle_type	Type of vehicle (light, truck, offroad)
current_value	Current resale value
mileage	Vehicle mileage
repair_cost_range	Tuple (low, high) for repair cost estimates
fuel_cost	Expected operating/fuel cost per period
insurance_coverage	Amount covered by insurance
month	Current month (for seasonal adjustments)
num_samples	Monte Carlo simulations (default: 1000)
future_periods	Optional: number of periods to simulate ahead

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Outputs

• decision: "SELL" or "KEEP"
• expected_loss: Average cost of repairs and operations
• resale_value: Current vehicle value
• risk_profile: Dictionary with min_loss, max_loss, std_dev

Example Output:

{
  "decision": "SELL",
  "expected_loss": 3200.50,
  "resale_value": 3000,
  "risk_profile": {
    "min_loss": 2800,
    "max_loss": 4100,
    "std_dev": 250
  }
}

Possible Future Enhancements

Fleet-wide batch analysis to rank vehicles by expected loss

GPU-accelerated Monte Carlo for large fleets

Seasonal resale value modeling

CLI or web dashboard for visualization

Integration with labor/fuel cost estimators for total operating cost

Other

fleet_optimizer/
│
├── main.py                        # Entry point
├── models/                        # Database models
│   ├── db.py                      # SQLAlchemy database setup
│   ├── vehicle.py                 # Vehicle table for Monte Carlo
│   ├── fleet.py                   # Fleet vehicle table
│   ├── fuel.py                    # Fuel efficiency table
│   ├── maintenance.py             # Maintenance log table
│   └── weather.py                 # Weather data table
├── simulation/                    # Monte Carlo simulation and sampling
│   ├── monte_carlo.py             # Core simulation
│   ├── fuel_model.py              # Fuel cost sampling
│   ├── maintenance_model.py       # Maintenance cost sampling
│   └── weather_model.py           # Weather multipliers
├── services/                      # Analysis / decision logic
│   ├── decision_engine.py         # Vehicle-level decision
│   └── fleet_analysis.py          # Fleet-wide analysis
└── utils/                         # CSV loaders
    ├── fleet_loader.py
    ├── fuel_loader.py
    └── maintenance_loader.py