Learning-Based Approach to Integrated Operational Optimization Problems in Robot-Assisted Multistation Warehouse Systems
研究了机器人移动履行系统中订单拣选的三个相互关联的优化问题，提出一种集成学习策略的局部搜索算法，在工业规模案例上比六种对比方法性能提升2.9%至33.2%。
In the era of booming e-commerce and Internet of Things technology, robotic mobile fulfillment systems (RMFSs) have gained more and more use in logistics industry. While these systems enhance labor efficiency, they introduce numerous optimization challenges. Order picking is a human–robot collaborative process in RMFS. It involves three critical and interrelated operational optimization issues: 1) PS; 2) resource scheduling; and 3) manual picking. Each of them is an NP-hard combinatorial optimization problem. Their integration is a significant challenge for operational optimization in RMFS with multiple picking stations and represents a novel problem that was not studied before to our best knowledge. To tackle this complex problem and fill the research gap, we first model it as a MIP to derive exact solutions for small-scale and illustrative cases. For industrial-scale cases that cannot be solved by exact methods given limited time, we propose a tailored learning-strategies-enhanced local search algorithm. It integrates a 3-D bi-section encoding strategy, a three-stage decoding policy, a learning-based ANS method, and two learning-based tabu mechanisms. Experimental results demonstrate the effectiveness of our proposed method, achieving 2.9% to 33.2% performance improvement over six competitive peers. This highlights its superiority in solving the concerned problem, providing significant potential for addressing practical order picking optimization challenges in RMFS.