# A Bi-Objective Approach to Last-Mile Delivery Routing Considering Driver Preferences

arXiv: https://arxiv.org/abs/2405.16051 · HTML: https://mlsanity.com/paper/2405.16051/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 25 May 2024.

## Builds on (most cited first, 4 of 4)

- [Constrained Local Search for Last-Mile Routing](https://mlsanity.com/paper/2112.15192/) (2021)
- [Vehicle routing by learning from historical solutions](https://mlsanity.com/paper/1909.07893/) (2019)
- [Learning from Drivers to Tackle the Amazon Last Mile Routing Research Challenge](https://mlsanity.com/paper/2205.04001/) (2022)
- [Amazon Last-Mile Delivery Trajectory Prediction Using Hierarchical TSP with Customized Cost Matrix](https://mlsanity.com/paper/2302.02102/) (2023)

## Abstract (arXiv)

The Multi-Objective Vehicle Routing Problem (MOVRP) is a complex optimization problem in the transportation and logistics industry. This paper proposes a novel approach to the MOVRP that aims to create routes that consider drivers' and operators' decisions and preferences. We evaluate two approaches to address this objective: visually attractive route planning and data mining of historical driver behavior to plan similar routes. Using a real-world dataset provided by Amazon, we demonstrate that data mining of historical patterns is more effective than visual attractiveness metrics found in the literature. Furthermore, we propose a bi-objective problem to balance the similarity of routes to historical routes and minimize routing costs. We propose a two-stage GRASP algorithm with heuristic box splitting to solve this problem. The proposed algorithm aims to approximate the Pareto front and to present routes that cover a wide range of the objective function space. The results demonstrate that our approach can generate a small number of non-dominated solutions per instance, which can help decision-makers to identify trade-offs between routing costs and drivers' preferences. Our approach has the potential to enhance the last-mile delivery operations of logistics companies by balancing these conflicting objectives.