The Supply Chain Transportation and Route Planning Under Deep Reinforcement Learning

Abstract

As concerns over environmental pollution and the reduction of greenhouse gas emissions intensify, sustainable strategies in supply chain transportation are critical. This paper proposes a novel approach to optimizing transportation routes and reducing carbon emissions in a green supply chain using deep reinforcement learning. The research targets a three-tier green supply chain consisting of manufacturers, third-party logistics providers (3PL), and retailers. First, a carbon reduction model for transportation is established, accounting for both product greenness and carbon emissions that influence demand. The study then introduces a Proximal Policy Optimization (PPO)-based contract model, combining cost-sharing and profit-sharing mechanisms between retailers and logistics providers to incentivize eco-friendly practices.