Demand Acceptance using Reinforcement Learning for Dynamic Vehicle Routing Problem with Emission Quota
This addresses routing and emission management for logistics and transportation systems, but it appears incremental as it builds on existing methods with a specific constraint.
The paper tackles the Dynamic and Stochastic Vehicle Routing Problem with Emission Quota by introducing a two-layer optimization framework that combines reinforcement learning with combinatorial optimization to manage demand acceptance and routing under emission constraints, demonstrating its relevance across various input types and uncertain horizons.
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.