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Last Mile Delivery Routing

Building effective Last Mile Delivery Routing management software is complex. There are various constraints to consider in order to ensure operational efficiency. For instance: delivery time windows, package size, driver availability, traffic conditions, and more.

At Timefold, we provide software builders with the necessary tools to tackle these challenging mathematical problems. Our Last Mile Delivery Routing model helps companies efficiently manage their delivery fleet and minimize wasteful planning.

What is Last Mile Delivery Routing?

Last Mile Delivery Routing focuses on optimizing the final leg of the delivery process. It involves determining the most efficient routes for delivery vehicles to visit each customer location while considering time constraints, delivery capacity, and other relevant factors.


Similar to other optimization problems, Last Mile Delivery Routing has both hard and soft constraints that shape the solution.

Hard constraints

Hard constraints represent limitations or requirements that cannot be violated.

They are crucial for ensuring feasibility, complying with regulations, and meeting business rules and service level agreements (SLAs). Failing to satisfy a hard constraint would render the solution infeasible or impractical.

Examples of hard constraints in Last Mile Delivery Routing:

  • Delivery capacity: A vehicle cannot exceed its maximum capacity for package load.

  • Time windows: Each customer has a specific time window within which the delivery must occur.

  • Vehicle limitations: Constraints on vehicle characteristics, such as maximum speed or weight limits.

  • Driver availability: Availability and working hours of drivers for efficient route planning.

  • Traffic conditions: Consideration of real-time traffic data to optimize routes and avoid congested areas.

Soft constraints

Soft constraints represent preferences, cost reductions, and service quality improvements. While meeting these constraints is desirable, they are not mandatory for a feasible planning solution.

Why Last Mile Delivery Routing is complex

As the number of customers and delivery vehicles increases, the number of possible combinations grows exponentially. Conducting an exhaustive search becomes infeasible. Additionally, incorporating constraints such as time windows, vehicle capacities, and real-time traffic conditions escalates the complexity further.

A mathematical planning optimization solver like Timefold is designed to handle these complexities efficiently.

Operational fit

When dealing with day-to-day Last Mile Delivery Routing planning, your software should offer the following:

  • Continuous planning: Adapt and update optimized routes and schedules dynamically as new information becomes available.

  • Pinning: Fix or lock specific elements of the problem to their current state or desired values, such as assigning a specific driver to a customer’s delivery preference.

  • Overconstrained planning: Help find a solution when there are too few resources to cover all the deliveries by making certain visits optional.

  • Real-time planning: React to disruptions in real-time, allowing for quick adjustments to the plan within milliseconds.

Variations of Last Mile Delivery Routing

While there are various variations of Last Mile Delivery Routing, they are typically based on the basic routing problem with additional constraints tailored to specific requirements:

  • Capacitated Last Mile Delivery Routing Problem (C-LMDRP): Accounts for limited delivery vehicle capacities, ensuring that the total demand served by each vehicle does not exceed its capacity.

  • Last Mile Delivery Routing Problem with Time Windows (LMDRPTW): Incorporates time constraints for customer deliveries, ensuring that deliveries occur within specific time intervals.

  • Last Mile Pickup and Delivery Problem (LMPDP): Involves the pickup and delivery of goods, requiring efficient planning for both pickup and delivery locations.

  • Split Delivery Last Mile Delivery Routing: dividing a customer shipment into multiple smaller deliveries and optimizing the routes for each individual package to improve efficiency