Docs
  • Solver
  • Models
    • Field Service Routing
    • Employee Shift Scheduling
    • Pick-up and Delivery Routing
  • Platform
Try models
  • Pick-up and Delivery Routing
  • Recommendations

Pick-up and Delivery Routing

    • Introduction
    • Getting started: Hello world
    • User guide
      • Terminology
      • Use case guide
      • Planning AI concepts
      • Integration
      • Constraints
      • Understanding the API
      • Demo datasets
      • Input datasets
        • Model configuration
        • Model input
        • Planning window
      • Input validation
      • Output datasets
        • Metadata
        • Model output
        • Input metrics
        • Key performance indicators (KPIs)
      • Routing with Timefold’s maps service
      • Metrics and optimization goals
    • Driver resource constraints
      • Lunch breaks and personal appointments
      • Route optimization
      • Shift hours and overtime
      • Driver capacity
    • Job service constraints
      • Time windows and opening hours
      • Skills
      • Multi-day schedules and movable stops
      • Dependencies between stops
      • Priority jobs and optional jobs
      • Stop service level agreement (SLA)
      • Job requirements and tags
        • Job required drivers
        • Job pooling
        • Prohibit job combinations
        • Maximum time burden
        • Tags
    • Recommendations
      • Job time window recommendations
      • Stop time window recommendations
    • Real-time planning
      • Real-time planning: pinning stops
      • Real-time planning: extended stop
      • Real-time planning: reassignment
      • Real-time planning: no show
      • Real-time planning: driver ill
    • Real-time planning with patches
      • Real-time planning: pinning stops (using patches)
      • Real-time planning: extended stop (using patches)
      • Real-time planning: reassignment (using patches)
      • Real-time planning: no show (using patches)
      • Real-time planning: driver ill (using patches)
    • Changelog
    • Upgrading to the latest versions
    • Feature requests

Recommendations

Plans change and evolve over time.

When a customer calls to request a driver for a pick-up and delivery job, they’d like to know when to expect the driver, and the more specific the timeframe the better. Tuesday, February 2nd between 13:00 and 16:00 is much better than the first week in February.

Timefold can quickly provide a range of time window recommendations for a pick-up and delivery job when a customer is requesting service. After the customer has accepted one of the recommendations, their job and the accepted time window is added to an input dataset which can be optimized straightaway (Real-time planning), or at a later date if the job occurs during a planning window that hasn’t been optimized yet, either way, the customer doesn’t need to stay on the phone to wait for the optimized solution.

The following guides explain how to use recommendations:

  • Job time window recommendations

  • Stop time window recommendations

  • © 2026 Timefold BV
  • Timefold.ai
  • Documentation
  • Changelog
  • Send feedback
  • Privacy
  • Legal
    • Light mode
    • Dark mode
    • System default