Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry
On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization
Abstract (arXiv)
As an important component of the supply chain industry, transportation has experienced rapid development in the past decade with the assistance of digital platforms and intelligent algorithms. Within the field of transportation research, Vehicle Routing Problem (VRP) has remained a persistent and enduring challenge. In the realm of management science, experts, and scholars from both the industrial and academic sectors have continuously explored optimization models and algorithms to effectively address routing problems, from the classical Traveling Salesman Problem to the more general Vehicle Routing Problem. These models and algorithms are applied in real-world industrial scenarios to achieve cost optimization and reduce carbon footprints. However, due to the complexity of real-world problems, numerous specific constraints are often added, and challenges such as information opacity, uncertainty, and irrational human behavior may arise. Therefore, deploying and optimizing mathematical models for VRP in practical scenarios while maintaining optimal results poses numerous challenges. This paper discusses and provides solutions for three different logistic use cases involving external truck network design. Through these industrial case study, the paper introduces how deep reinforcement learning-based vehicle routing optimization has been implemented. As a result, it can be observed that the routes optimized by reinforcement learning agent have over 10% total cost compared to baseline results. Furthermore, the paper proposes that in future research, DRL algorithms for vehicle routing problems could be generalized into more variations of VRP.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 7 references that are in the map. First posted on arXiv 7 Aug 2026.
Builds on
1 of 7 references.
References · 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes
- 2025Deep Reinforcement Learning for Solving the Fleet Size and Mix Vehicle Routing Problem
- 2026Vehicle-as-Prompt: A Unified Deep Reinforcement Learning Framework for Heterogeneous Fleet Vehicle Routing Problem
- 2026A Curriculum-Based Deep Reinforcement Learning Framework for the Electric Vehicle Routing Problem
- 2024Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows
- 2024Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II
- 2026A Unified Knowledge Embedded Reinforcement Learning-based Framework for Generalized Capacitated Vehicle Routing Problems
- 2026Deep Reinforcement Learning solution for pickup and delivery routing problems with time window and capacity constraints