# Demand Acceptance using Reinforcement Learning for Dynamic Vehicle Routing Problem with Emission Quota

arXiv: https://arxiv.org/abs/2603.13279 · HTML: https://mlsanity.com/paper/2603.13279/

## 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 6 of its 19 references that are in the map.
- First posted on arXiv 27 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 6 of 6)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Deep Q-Learning for Same-Day Delivery with Vehicles and Drones](https://mlsanity.com/paper/1910.11901/) (2019)
- [A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem](https://mlsanity.com/paper/2009.00363/) (2020)
- [Solving Large-Scale Dynamic Vehicle Routing Problems with Stochastic Requests](https://mlsanity.com/paper/2202.12983/) (2022)
- [TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problem](https://mlsanity.com/paper/2311.18662/) (2023)
- [Demand Selection for VRP with Emission Quota](https://mlsanity.com/paper/2505.19315/) (2025)

## Abstract (arXiv)

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.