mlsanityData to 14 Oct 2026

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

arXiv 2603.13279 ↗First posted 27 Feb 2026cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

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.

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.

Builds on

6 of 19 references.

References · 6

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2019Deep Q-Learning for Same-Day Delivery with Vehicles and Dronescited by 15
  3. 2020A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problemcited by 5
  4. 2022Solving Large-Scale Dynamic Vehicle Routing Problems with Stochastic Requestscited by 3
  5. 2023TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problemcited by 3
  6. 2025Demand Selection for VRP with Emission Quotacited by 1

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  1. 2024Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows
  2. 2026Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry
  3. 2025Constrained Reinforcement Learning for the Dynamic Inventory Routing Problem under Stochastic Supply and Demand
  4. 2024Multi-Agent Environments for Vehicle Routing Problems
  5. 2025Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms
  6. 2025Learning to Search for Vehicle Routing with Multiple Time Windows
  7. 2025Deep Reinforcement Learning for Solving the Fleet Size and Mix Vehicle Routing Problem
  8. 2026A Unified Knowledge Embedded Reinforcement Learning-based Framework for Generalized Capacitated Vehicle Routing Problems