Flexible Endpoints in Multi-Day Routing Slash Empty Mileage by 15%

Why It Matters

This research highlights a critical, often overlooked, factor in logistics planning: the strategic positioning of vehicles at the end of each day. By considering how a vehicle's endpoint influences its starting point for the next day, companies can unlock substantial efficiencies, reducing operational costs and environmental impact.

Key Finding

By treating vehicle endpoints as a variable to be optimized for the next day's operations, rather than fixed points, logistics companies can significantly cut down on wasted travel (empty mileage) and reduce their fuel use.

Key Findings

• The proposed iterative two-day optimization approach effectively addresses the multi-day open-close vehicle routing problem with time windows.
• Strategic determination of flexible endpoints for open-route vehicles significantly impacts daily efficiency and future operational positioning.
• The method demonstrated a reduction in empty mileage, leading to decreased fossil fuel consumption.

Research Evidence

Aim: How can a multi-day vehicle routing problem with flexible endpoints be optimized to minimize empty mileage and fuel consumption while considering real-world operational constraints?

Method: Simulation and Optimization

Procedure: An iterative two-day optimization approach was developed using Google OR-Tools to model a multi-day open-close vehicle routing problem with time windows. This model incorporated constraints such as driver hours, vehicle compatibility, and customer time windows, with a focus on determining optimal flexible endpoints for vehicles operating on open routes.

Sample Size: Over 120 vehicles and 3500 transport requests

Context: Bulk material transportation logistics

Design Principle

Optimize for multi-day operational flow, not just daily task completion.

How to Apply

When designing or selecting logistics routing software, prioritize solutions that allow for the dynamic optimization of vehicle endpoints based on future operational needs.

Limitations

The study's validation was based on data from a single Czech logistics company, and the complexity of real-world transport request data (incomplete future information) was a significant factor.

Student Guide (IB Design Technology)

Simple Explanation: Imagine planning a road trip where you don't just decide where to sleep each night, but you pick a spot that makes it easiest to start driving the next morning. This study shows that doing this for delivery trucks saves a lot of fuel.

Why This Matters: This research shows how smart planning can save resources and reduce environmental impact, which is a key goal in many design projects.

Critical Thinking: To what extent can real-time dynamic adjustments to flexible endpoints mitigate the impact of unforeseen events (e.g., traffic delays, new urgent orders) on overall route efficiency?

IA-Ready Paragraph: The optimization of multi-day logistics routes, as demonstrated by Kubowský et al. (2026), highlights the critical impact of flexible endpoint determination on reducing empty mileage and fuel consumption. By strategically planning vehicle positions at the end of each operational day to optimize the start of the next, significant resource efficiencies can be achieved, directly contributing to more sustainable transport operations.

Project Tips

• When planning routes for a project, think about where the vehicle will end up and how that affects the start of the next journey.
• Consider using simulation tools to test different endpoint strategies.

How to Use in IA

• Reference this study when discussing the optimization of resource allocation and efficiency in your design project's logistics or operational planning.

Examiner Tips

• Demonstrate an understanding of how the 'end' of one operational cycle can be the 'start' of another, and how this interdependency can be leveraged for efficiency.

Independent Variable: Endpoint flexibility strategy (fixed vs. optimized flexible)

Dependent Variable: Empty mileage, fuel consumption, total route time

Controlled Variables: Vehicle capacity, delivery time windows, driver working hours, material compatibility, customer locations

Strengths

• Incorporates a wide range of realistic operational constraints.
• Uses a validated optimization tool (OR-Tools) with real-world data.

Critical Questions

• How would the optimal endpoint strategy change if vehicle availability was highly variable day-to-day?
• What are the computational trade-offs between optimizing for a single day versus a multi-day horizon?

Extended Essay Application

• Investigate the impact of different driver rest break policies on optimal multi-day route endpoints and overall fleet efficiency.
• Develop a predictive model for future transport requests to improve the accuracy of flexible endpoint optimization in long-term planning.

Source

Sustainable logistics: Optimizing multi-day routes with flexible endpoints · Cleaner Engineering and Technology · 2026 · 10.1016/j.clet.2026.101176