Demand Acceptance using Reinforcement Learning for Dynamic Vehicle Routing Problem with Emission Quota
arXiv:2603.13279v1 Announce Type: new Abstract: This paper introduces and formalizes the Dynamic and Stochastic Vehicle Routing Problem with Emission Quota (DS-QVRP-RR), a novel routing problems that integrates dynamic demand acceptance and routing with a global emission constraint. A key contribution is a two-layer optimization framework designed to facilitate anticipatory rejections of demands and generation of new routes. To solve this, we develop hybrid algorithms that combine reinforcement learning with combinatorial optimization techniques. We present a comprehensive computational study that compares our approach against traditional methods. Our findings demonstrate the relevance of our approach for different types of inputs, even when the horizon of the problem is uncertain.
arXiv:2603.13279v1 Announce Type: new Abstract: This paper introduces and formalizes the Dynamic and Stochastic Vehicle Routing Problem with Emission Quota (DS-QVRP-RR), a novel routing problems that integrates dynamic demand acceptance and routing with a global emission constraint. A key contribution is a two-layer optimization framework designed to facilitate anticipatory rejections of demands and generation of new routes. To solve this, we develop hybrid algorithms that combine reinforcement learning with combinatorial optimization techniques. We present a comprehensive computational study that compares our approach against traditional methods. Our findings demonstrate the relevance of our approach for different types of inputs, even when the horizon of the problem is uncertain.
Executive Summary
This article introduces the Dynamic and Stochastic Vehicle Routing Problem with Emission Quota (DS-QVRP-RR), a novel routing problem that integrates dynamic demand acceptance and routing with a global emission constraint. A two-layer optimization framework is developed to facilitate anticipatory rejections of demands and generation of new routes. The authors combine reinforcement learning with combinatorial optimization techniques to solve the problem. A comprehensive computational study compares their approach against traditional methods, demonstrating its relevance for different types of inputs and uncertain problem horizons. The research provides a promising solution for optimizing vehicle routing while considering environmental constraints, but its real-world applicability and scalability require further investigation.
Key Points
- ▸ The DS-QVRP-RR problem integrates dynamic demand acceptance and routing with a global emission constraint.
- ▸ A two-layer optimization framework is developed to facilitate anticipatory rejections of demands and generation of new routes.
- ▸ Hybrid algorithms combining reinforcement learning with combinatorial optimization techniques are proposed to solve the problem.
Merits
Strength
The article provides a comprehensive computational study comparing the proposed approach against traditional methods, demonstrating its relevance for different types of inputs and uncertain problem horizons.
Strength
The developed two-layer optimization framework offers a promising solution for optimizing vehicle routing while considering environmental constraints.
Demerits
Limitation
The article assumes a simplified scenario where the emission quota is known in advance, which may not be the case in real-world scenarios.
Limitation
The scalability and real-world applicability of the proposed approach require further investigation and validation.
Expert Commentary
The article makes significant contributions to the field of vehicle routing problems by introducing a novel problem formulation and a two-layer optimization framework. However, the article's limitations should be acknowledged, and further research is required to investigate the scalability and real-world applicability of the proposed approach. Additionally, the article's results should be validated using real-world data and scenarios to ensure the practicality and generalizability of the findings. Overall, the research provides a promising starting point for developing more efficient and environmentally friendly vehicle routing solutions.
Recommendations
- ✓ Future research should investigate the scalability and real-world applicability of the proposed approach using larger-scale instances and real-world data.
- ✓ The authors should validate their results using real-world data and scenarios to ensure the practicality and generalizability of the findings.