Agent skill

driver-scheduling-optimizer

Automated driver assignment and hours of service compliance skill ensuring regulatory compliance and operational efficiency

View SKILL.md on GitHub Repository
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Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/logistics/skills/driver-scheduling-optimizer

Metadata

Additional technical details for this skill

domain
business
category
fleet-management
priority
medium
specialization
logistics

SKILL.md

Driver Scheduling Optimizer

Overview

The Driver Scheduling Optimizer automates driver assignment and ensures hours of service (HOS) compliance while maximizing operational efficiency. It matches drivers to loads based on qualifications, availability, and location while preventing regulatory violations and managing fatigue risk.

Capabilities

  • HOS Compliance Monitoring: Track and enforce hours of service regulations in real-time
  • Driver-Load Matching: Match drivers to loads based on qualifications, location, and availability
  • Qualification Verification: Verify driver certifications, endorsements, and training requirements
  • Fatigue Risk Assessment: Assess driver fatigue risk based on work patterns and rest periods
  • Break and Rest Planning: Plan mandatory breaks and rest periods into driver schedules
  • ELD Data Integration: Integrate with electronic logging devices for accurate time tracking
  • Violation Prevention Alerting: Alert dispatchers and drivers before potential violations occur

Tools and Libraries

  • ELD APIs
  • FMCSA Compliance Databases
  • Scheduling Optimization Libraries
  • Driver Management Systems

Used By Processes

  • Driver Scheduling and Compliance
  • Route Optimization
  • Fleet Performance Analytics

Usage

yaml
skill: driver-scheduling-optimizer
inputs:
  drivers:
    - driver_id: "DRV001"
      name: "John Smith"
      current_location: "Chicago, IL"
      endorsements: ["hazmat", "tanker"]
      hos_status:
        driving_remaining_hours: 8.5
        duty_remaining_hours: 12.0
        cycle_remaining_hours: 55.0
        last_rest_end: "2026-01-25T06:00:00Z"
    - driver_id: "DRV002"
      name: "Jane Doe"
      current_location: "Indianapolis, IN"
      endorsements: []
      hos_status:
        driving_remaining_hours: 11.0
        duty_remaining_hours: 14.0
        cycle_remaining_hours: 60.0
        last_rest_end: "2026-01-25T05:00:00Z"
  loads:
    - load_id: "LOAD001"
      origin: "Chicago, IL"
      destination: "Columbus, OH"
      pickup_time: "2026-01-25T10:00:00Z"
      estimated_drive_time_hours: 5.5
      requirements:
        endorsements_required: []
        experience_years: 1
  scheduling_parameters:
    buffer_hours: 1.0
    prefer_home_time: true
outputs:
  driver_assignments:
    - load_id: "LOAD001"
      assigned_driver: "DRV002"
      assignment_rationale:
        - "Full HOS availability (11 hours driving)"
        - "Closer to pickup location"
        - "No endorsement requirements"
      schedule:
        deadhead_start: "2026-01-25T08:00:00Z"
        pickup: "2026-01-25T10:00:00Z"
        estimated_delivery: "2026-01-25T15:30:00Z"
        required_breaks:
          - type: "30_min_break"
            location: "Rest Area - I-70 Mile 85"
            time: "2026-01-25T12:30:00Z"
  hos_projections:
    DRV002:
      after_load:
        driving_remaining: 5.5
        duty_remaining: 7.5
        reset_required_by: "2026-01-26T19:00:00Z"
  compliance_alerts: []

Integration Points

  • Electronic Logging Devices (ELD)
  • Fleet Management Systems
  • Transportation Management Systems (TMS)
  • Driver Mobile Apps
  • FMCSA Systems

Performance Metrics

  • HOS violation rate
  • Driver utilization rate
  • Load acceptance rate
  • On-time pickup percentage
  • Driver satisfaction score

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