Warehouse Automation

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14 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Resource-Performance Trade-offs in Intra-Aisle Robotic Picking: A Discrete-Event Simulation Comparison of Shuttle Configurations With and Without an Onboard Tote Lift
Oct 26, 2026Mohsen Reda Mahmoud MoursiWarehouse Automation Robotics
Graz University of Technology, Graz, Austria
Reconstruction and Discrete-Event Simulation Assessment of an Analytical
Oct 19, 2026Mohsen Reda Mahmoud Moursi, Ahmed StohyWarehouse Automation Robotics
Graz University of Technology, Graz, Austria · TU Wien, Vienna, Austria
HiRAD: A Flexible Large-Scale AGV Routing System
Sep 9, 2026Yunjie Huang, Ruizhong Wu, Mengxuan Zhang +3Hierarchical RL Vehicle Routing
Fixed-Haven Reservation for Online Multi-Agent Pickup and Delivery in Dense Warehouses
Aug 27, 2026Taisei Hirayama, Kohei Yoshida, Hiroki Sakaji +1Warehouse Automation Multi-Agent Planning
Hokkaido University, Sapporo 060-0808, Japan · Toyota Industries Corporation, Aichi 474-8601, Japan
SiriusDeliver: Automating Data Warehouse Delivery at Tencent
Aug 10, 2026Haining Xie, Xiaokai Zhou, Jiaming Yang +12LLM Agent Orchestration LLM Agent Self-Improvement
TEG, Tencent Inc. · Wuhan University
Stress-Relief Annealing: Polynomial-Time Simulation-Free Layout Optimization for Automated Warehouses
Aug 2, 2026Xiangjie Luo, Yulun Zhang, Miyuki Koshimura +2Combinatorial Optimization Warehouse Automation
Graduate School of Information Science and Electrical Engineering, Kyushu University · Robotics Institute, Carnegie Mellon University
Stigmergic Graph Memory: An Environment-Aware Approach for Many-to-Many Multi-Agent Pickup and Delivery
Jul 16, 2026Aditya Dutta, Joon-Seok KimMulti-Agent Coordination Graph-Based Agent Memory
Emory University 201 Dowman Drive Atlanta, GA 30322 USA
Eluna: An Agentic LLM System for Automating Warehouse Operations with Reasoning and Task Execution
Jul 9, 2026Ning Liu, Kalle Kujanpää, Zhaoxuan Zhu +11Multi-Agent LLM Systems LLM Agent Orchestration
Amazon.com, Inc. Fulfillment Technologies and Robotics
Deep Reinforcement Learning for Dynamic Battery Management of Autonomous Order Pickers
Jul 6, 2026Taniya Shaji, Abhay Sobhanan, Christof DefrynRL for Robotics Mobile Robotics
Indian Institute of Management Bangalore, Bannerghatta Road, Bengaluru 560076, Karnataka, India · University of Antwerp, Prinsstraat 13, Antwerp 2000, Belgium
A Comparative Study of Bayesian Contextual Bandits for Real-Time Warehouse Sorter Optimization
Jun 22, 2026Tina Dongxu Li, Mouhacine Benosman, Ken Meszaros +1Contextual Bandits Warehouse Automation
Amazon.com, USA
Enhancing Computer Vision Model Generalization in Warehouse Facilities: A Case Study on Anomaly Detection in Vertical Material Handling Systems
May 29, 2026Ruiliang Liu, Tina Dongxu Li, Joshua Migdal +2Domain Generalization Industrial Anomaly Detection
Amazon, USA
Many-to-Many Multi-Agent Pickup and Delivery
May 8, 2026Ethan Schneider, Jingkai Chen, Tianyi Gu +3Multi-Agent System Optimization Multi-Robot Task Allocation
Institute of Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA · Symbotic Inc., Wilmington, MA 01887, USA · Northeastern University, Boston, MA 02115, USA
CREST: Constraint-Release Execution for Multi-Robot Warehouse Shelf Rearrangement
Mar 27, 2026Jiaqi Tan, Yudong Luo, Sophia Huang +2Multi-Robot Systems Robotics
School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada · HEC Montréal, Montréal, QC H3T 2A7, Canada · Purdue University, West Lafayette, IN 47907 USA
Learning-guided Prioritized Planning for Lifelong Multi-Agent Path Finding in Warehouse Automation
Mar 25, 2026Han Zheng, Yining Ma, Brandon Araki +2Warehouse Automation Multi-Agent Path Finding
Massachusetts Institute of Technology, Cambridge, MA, USA · Symbotic, Wilmington, MA, USA

Oct 26, 2026J/K move · Enter open · S save

Resource-Performance Trade-offs in Intra-Aisle Robotic Picking: A Discrete-Event Simulation Comparison of Shuttle Configurations With and Without an Onboard Tote Lift

Mohsen Reda Mahmoud Moursi

Graz University of Technology, Graz, Austria

Warehouse Automation Robotics Industrial Robotics

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This study compares two implemented intra-aisle robotic-picking configurations through discrete-event simulation. Without an onboard tote lift, two level-captive shuttles serve the two tested levels; with the lift, one shuttle remains on its horizontal level and its onboard mechanism moves the order tote to allow picks from the adjacent upper level. Thirty configuration-task-size experiments (n = 1-15 locations; ten replications each) yield 300 replication-level cycle-time observations. Two-sided Welch tests are applied to the replication means, with Holm adjustment across the 15 comparisons. The onboard-tote-lift configuration has longer mean cycle time at all tested task sizes. At n = 15, the means are 299.477 and 333.231 s/cycle, respectively: a 33.753 s increase (11.27%). The corresponding cycle-time-derived item rates are 180.314 and 162.050 items/h (-10.13%). These rates are calculated as 3600n divided by the scenario-mean cycle time; they are not measurements of the combined concurrent capacity of two level-captive shuttles. Importantly, task locations are sampled from different eligible-level sets in the two configurations, so the result concerns the implemented configuration-and-task-generation packages, not the isolated effect of a five-second tote-lift operation under identical picked locations. The study quantifies a cycle-performance difference alongside a reduction from two dedicated shuttles to one within the tested two-level block; it does not quantify system-level maximum throughput, investment cost, or reliability.