报告题目: Integrated Storage Allocation and Order Picking Optimization for Omni-Channel Fulfillment

报告摘要: To address inefficiencies in hybrid “online + offline” retail fulfillment, this study proposes an integrated approach that synergizes storage allocation and order picking—traditionally optimized in isolation. We introduce a picking-task-driven storage optimization method to minimize redundant picking paths. By incorporating item correlations within picking tasks and physical constraints, a storage assignment model is developed and solved using a dual-frequency gradient iteration algorithm. This algorithm progressively compares pairs of items based on historical picking data, dynamically identifies low-frequency items, and iteratively determines their near-optimal storage sequences. Complementing this, two mixed integer linear programming models are formulated to minimize both picking releases and order delays. A two-stage heuristic is designed to solve these complex problems efficiently. Numerical experiments demonstrate that our integrated framework reduces the number of picking tasks by 19% and decreases total order delay by 74%. Furthermore, the storage algorithm significantly shortens picking routes compared to conventional methods. This work offers a comprehensive and human-aware optimization solution for new retail warehousing operations.

报告人简介：郭鹏，西南交通大学机械工程学院副教授，研究方向为智能制造与物流，主持国家自然科学基金青年基金、科技部国际合作交流项目、教育部人文社科基金及四川省自然科学基金等多个项目，并参与欧盟创新框架X2Rail项目和国家重点研发计划课题等重要研究。以第一/通讯作者在IEEE TII、IJPR、TRE、Omega等国际期刊发表论文20余篇，其中2篇入选ESI高被引论文，1篇入选热点论文。担任《智能系统学报》与《工业工程》期刊青年编委，曾获得2019年Omega期刊最佳论文奖、多个会议优秀论文奖，以及2018年四川省科技进步三等奖、2021年中物联科技进步二等奖等科技奖励。