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Automatic Trip Generation

Automatic Trip Generation

Optimizing vehicle utilization and route planning with Machine Learning to reduce manual efforts, minimize delivery time, and cut operational costs for seamless logistics management.

Category: Machine Learning
Industry: Logistics & Supply Chain Management

Project Info

  • Client:

    Digital Health Platform | UAE

  • Services:

    Credit Restoration

  • Date:

    February 12, 2024

  • Category:

    Finance

  • Team:

    Jonathan Hunt

Business Objective/Challenges:

  • Reduce Manual Effort – Automate trip creation to minimize human intervention and dependency on trip planners.
  • Optimize Vehicle Utilization – Ensure maximum vehicle capacity utilization by efficiently grouping shipments.
  • Minimize Delivery Time & Costs – Optimize routes to reduce travel distance and fuel consumption.
  • Enhance Operational Efficiency – Improve the overall logistics process by reducing errors, managing peak order volumes, and ensuring timely deliveries.
  • Goal – Improve scalability, reduce costs, and enhance customer satisfaction through optimized deliveries.

Solution

  • Automated Trip Creation
    • The system receives shipment details and vehicle information from the database.
    • Shipments are grouped to minimize travel distance.
    • Considers vehicle capacity, time slot utilization, and coverage utilization.
  • Route Optimization Using Maps API
    • Calculates the most efficient delivery routes.
    • Routes adjust in real-time based on traffic conditions.
  • Dynamic Adjustments for New Orders
    • New shipments are assigned to existing trips based on minimal additional travel distance.
    • Prevents reshuffling of already assigned shipments.
  • Auto-Adjustment & Merging of Trips
    • Merges trips with low vehicle utilization to increase efficiency.
    • Combines trips from consecutive time slots to reduce the total number of trips.
  • Manual Adjustments
    • Fulfillment Center users can modify trips:
      • Move shipments between trips.
      • Split a trip into two if necessary.
      • Merge two trips into one to optimize efficiency further.

Technologies

  • Artificial Intelligence, Machine Learning.

Images :

Business Outcome :

  • Optimized Routes – Reduced travel distances, saving time and fuel. Real-time traffic-aware routing via Maps API.
  • Higher Vehicle Capacity Utilization – Maximized use of each vehicle, reducing trips and operational costs.
  • Reduced Costs – Fewer trips lower fuel consumption, reduce vehicle wear-and-tear, and decrease manual labor requirements.
  • Decreased Manual Effort – Automation minimizes reliance on human planners and errors. Dynamic adjustments enable real-time optimization.
  • Scalability – Efficiently handles fluctuations in order volume; supports multiple vehicle types including Three-Wheelers, Four-Wheelers, and EV deliveries.
  • Real-Time Adjustments – Dynamically assigns new orders without disrupting existing trips, maintaining optimized routes.
  • Improved Delivery Timeliness – Optimized sequencing ensures deliveries are completed within designated time slots, reducing late delivery complaints.
  • Enhanced Operational Efficiency – Minimizes errors from manual planning and allows scaling logistics without extra manpower.
  • Customer Satisfaction – Faster and on-time deliveries improve customer experience and reliability.