In the context of differentiated on-demand goods delivery services, this study proposes an integrated optimization method for automated guided vehicles (AGVs) based smart warehouse operations and the last-mile multi-modal transport.
A deep reinforcement learning algorithm for multi-objective joint scheduling is designed to establish a dynamic connection between two systems, solving key challenges such as achieving high-throughput continuous order scheduling, meeting competing requirements, and improving the overall system sensitivity and adaptability.
For warehouse optimization within this framework, we propose an improved algorithm based on multi-objective, Multi-Reward Machines-A* Guided Deep Q-Network (MORM-AGDQN), which combines service level, system cost, and external transportation demand.
For external optimization, we propose an improved algorithm based on a Multi-Reward, Multi Head attention-Heterogeneous Capacity Vehicle Routing Problem (MRMH-HCVRP) framework, which incorporates the optimized scheduling order sequence and grouping, combined with customer location, demand, and priority, vehicle capacity, speed, and service range.
The results show that the proposed framework significantly outperforms traditional methods, achieving 100% on-time delivery rate for warehousing operations.
After joint optimization, the average delivery time for the last mile was reduced by 29.3% to 53.2%, the total transportation distance was reduced by 46.4%, the high-priority service rate was increased to over 92%, and a balance was maintained between operating costs and customer satisfaction.