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

2026-02-27 · Farid Najar, Dominique Barth, Yann Strozecki 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.

📄 PDF Abstract BibTeX arXiv:2603.13279

Code (0)

등록된 구현이 없습니다.

Tasks

Reinforcement Learning

Instance-Aware Parameter Configuration in Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem

2026-05-01 · Yinghao Qin, Xinwei Wang, Mosab Bazargani, Jun Chen arxiv

Algorithm performance in combinatorial optimization is highly sensitive to parameter settings, while a single globally tuned configuration often fails to exploit the heterogeneity of instances. This limitation is particu…

Parameter Prediction

Fast Approximate Solutions using Reinforcement Learning for Dynamic Capacitated Vehicle Routing with Time Windows

2021-02-24 · Nazneen N Sultana, Vinita Baniwal, Ansuma Basumatary, Piyush Mittal 외

This paper develops an inherently parallelised, fast, approximate learning-based solution to the generic class of Capacitated Vehicle Routing Problems with Time Windows and Dynamic Routing (CVRP-TWDR). Considering vehicl…

Reinforcement Learning (RL)

Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning

2020-10-22 · Shen Ren, Qianxiao Li, Liye Zhang, Zheng Qin 외

The future of mobility-as-a-Service (Maas)should embrace an integrated system of ride-hailing, street-hailing and ride-sharing with optimised intelligent vehicle routing in response to a real-time, stochastic demand patt…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Where the Action is: Let's make Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problems work!

2021-02-28 · Florentin D Hildebrandt, Barrett Thomas, Marlin W Ulmer

There has been a paradigm-shift in urban logistic services in the last years; demand for real-time, instant mobility and delivery services grows. This poses new challenges to logistic service providers as the underlying …

Reinforcement Learning (RL)

Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach

2023-07-13 · Jinhua Si, Fang He, Xi Lin, Xindi Tang

The integrated development of city clusters has given rise to an increasing demand for intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading traditional intercity bus services by i…

Hierarchical Reinforcement Learning Management