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

TL;DR

This paper introduces a bi-objective vehicle routing approach that balances routing costs with driver preferences using data mining and a two-stage GRASP algorithm, improving last-mile delivery planning.

Contribution

It presents a novel bi-objective model for last-mile routing that incorporates driver preferences and proposes an effective heuristic algorithm to solve it.

Findings

Data mining outperforms visual attractiveness in route planning.

The approach generates a small set of Pareto-optimal solutions.

Routes effectively balance cost and driver preferences.

Abstract

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.