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

• URL: http://arxiv.org/abs/2405.16051v1
• Date: Sat, 25 May 2024 04:25:00 GMT
• Title: A Bi-Objective Approach to Last-Mile Delivery Routing Considering Driver Preferences
• Authors: Juan Pablo Mesa, Alejandro Montoya, Raul Ramos-Pollán, Mauricio Toro,
• Abstract summary: 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.
• Score: 42.16665455951525
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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