🚗 UiPath Agent Project — Vehicle & Driver Availability Workflow

This repository contains my first UiPath Agent Development Project, designed to automate and optimize the process of matching available drivers and vehicles for logistics planning based on route data, weather, and traffic conditions.

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🧠 Project Overview

The Vehicle & Driver Availability Workflow is an AI-integrated automation built using UiPath Integration Service.
It combines RPA precision with AI-driven decision-making to analyze transportation routes and recommend the best possible plan for logistics dispatch.

💡 Key Features

• ✅ Intelligent vehicle & driver matching
• 🌦️ Weather and traffic analysis for route risk assessment
• 📊 Smart recommendations with plan and precautions
• 🧩 Modular workflows for scalability
• 🔄 AI evaluation pipeline for output scoring

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⚙️ System Components

Component	Description
Driver Availability Workflow	Filters and ranks available drivers based on experience, proximity, and workload.
Vehicle Availability Workflow	Selects the most suitable vehicles based on type, fuel, capacity, and availability.
Main Agent Workflows	Central logic that combines route, weather, and resource data to recommend optimal dispatch plans.
Database Tables	Local database files for storing vehicle and driver details.
Evaluation Sets	JSON datasets used to test workflow accuracy and AI evaluation performance.

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🧩 Workflow Architecture

Below are the core UiPath workflows used in the project:

🧍 Driver Filtration Workflow

🚘 Vehicle Filtration Workflow

🤖 Main Agents

Agent 1:

Agent 2:

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🗂️ Database Structure

👨‍✈️ Driver Database

Schema Overview

Field	Description
Driver ID	Unique identifier for each driver
Name	Full name
Experience	Years of driving experience
Proximity	Distance from current pickup location
Phone	Contact number
Remarks	Notes on driver.

🚛 Vehicle Database

Schema Overview

Field	Description
Vehicle ID	Unique identifier for each vehicle
Vehicle Number	Registration number
Brand	Manufacturer
Model	Vehicle model
Type	Truck, Lorry, Van, etc.
Fuel	Petrol/Diesel/Electric
Capacity	Load capacity
Remarks	maintenance note

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📈 Evaluation Framework

The project uses AI-based semantic evaluation to score output JSONs against expected results.
The evaluator prompt compares contextual accuracy — not exact string matches — allowing flexible and intelligent output scoring.

Example evaluation fields include:

• Route summary accuracy
• Weather and traffic context
• Correct mapping of driver/vehicle lists
• Logical recommendation plans

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🧾 Example

Input

{
  "from": "Chennai",
  "to": "Delhi",
  "weight": "15 tonns"
}

Output

{
  "content": {
    "routeSummary": {
      "origin": "Chennai",
      "destination": "Delhi",
      "distance": 2200,
      "duration": 40
    },
    "trafficAndWeather": {
      "trafficConditions": "Moderate traffic expected along NH44 with occasional bottlenecks near major cities.",
      "weatherForecast": "Clear skies expected for most of the route. Delhi may experience light fog in the early morning hours.",
      "risks": [
        "Light fog in Delhi may reduce visibility during early hours."
      ]
    },
    "availableDrivers": [
      {
        "name": "Raj Malhotra",
        "id": "17",
        "experience": "8 years",
        "hometownProximity": "Delhi",
        "phoneNumber": "9876001122",
        "remarks": "An expert driver who knows Delhi routes thoroughly but tends to be impatient during peak traffic"
      },
      {
        "name": "Anil Kumar",
        "id": "63",
        "experience": "10 years",
        "hometownProximity": "Delhi",
        "phoneNumber": "9811172200",
        "remarks": "He manages high-value cargo routes with precision and punctuality"
      }
    ],
    "availableVehicles": [
      {
        "VehicleID": "V009",
        "VehicleNumber": "MH85EF4599",
        "Brand": "Mahindra",
        "Model": "Supro Maxitruck",
        "Type": "Mini Truck",
        "Fuel": "Petrol",
        "Capacity": "31.99 tons",
        "Remark": "Minor dent on rear side"
      },
      {
        "VehicleID": "V027",
        "VehicleNumber": "GJ23EF5980",
        "Brand": "Volvo",
        "Model": "FM 400",
        "Type": "Truck",
        "Fuel": "Petrol",
        "Capacity": "19.56 tons",
        "Remark": "Recently returned from long trip"
      }
    ],
    "finalRecommendation": {
      "plan": "Assign driver Anil Kumar (10 years of experience) with vehicle Volvo FM 400 (19.56 tons capacity). The vehicle is in excellent condition and recently returned from a long trip. Start the journey early morning to avoid traffic congestion near Chennai and Delhi.",
      "precautions": [
        "Ensure the vehicle undergoes a pre-trip inspection.",
        "Plan for rest stops every 6-8 hours to avoid driver fatigue.",
        "Carry fog lights and ensure they are functional for early morning fog in Delhi.",
        "Monitor weather updates for any sudden changes."
      ]
    }
  }
}