arXiv subjects

Xiaozhu Sun

Publications and source records attributed to Xiaozhu Sun.

2 recordsLinked to original sources

arXiv2026

Integrated Optimization of Automated Warehouse Operations and Last-Mile Transport for Differentiated On-Demand Delivery

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.

cs.LG↗

arXiv2026

A bi-level priority sorting framework for flexible AGV service scheduling in smart warehouses

This paper proposes a bi-level optimization framework to coordinate Automated Guided Vehicle (AGV) flexible operations in smart independent warehouses, addressing the critical challenge of balancing high-throughput order fulfillment with stringent cost control. The framework is designed to simultaneously optimize flexible customer service level, system cost, and operational efficiency. The first level dynamically adjusts real-time scheduling parameters, such as order commitment times and delay tolerance, based on predefined customer priority categories. The second level performs real-time routing optimization for each AGV by identifying the shortest feasible paths while avoiding conflicts. For complex multi-capacity package picking tasks, two heuristic rules, priority, deadline, with shortest path (PDSP) and delay cost with shortest path (DCSP), are applied to multi-capacity package picking tasks and further training is carried out using the reinforcement learning algorithm of A* guided deep Q-learning (AGDQN). Comprehensive simulation experiments, conducted across diverse warehouse layouts and order demand patterns, demonstrate that the proposed framework equipped with both heuristic rules consistently reduces average order delay and total system costs by over 50% during peak demand periods. This is achieved while maintaining a service level above 90% and maximizing AGV utilization. The method also exhibits superior flexibility and sustained efficiency under normal and fluctuating demand scenarios. Additional ablation studies confirm that the proposed priority sorting mechanism delivers robust performance advantages when tested with various other reinforcement learning baselines.

math.OC↗