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Simulation of fully automated warehouse with deep storage racks
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This article presents a model of a fully automated warehouse with deep storage racks designed for boxed goods storage. The study focuses on optimizing warehouse operations through discrete multiagent simulation of shuttle movements for pallet loading and unloading tasks. The authors investigate various product placement strategies, including the Nearest Channel Positioning Algorithm (NCPA), Most Empty Channel Group Placement (MECGP), andMost Filled Channel Group Placement (MFCGP), while analyzing optimal routing schemes for the given warehouse topology.
A key contribution is determining the optimal number of shuttles to maximize warehouse throughput. Simulation results demonstrate that increasing the number of robots beyond 15 does not significantly improve efficiency due to increased route collisions. The study also examines 24-hour warehouse occupancy dynamics, revealing optimal storage utilization levels.
The developed model enables performance evaluation and optimization of task distribution among robots to minimize order processing time. Future research directions include implementing machine learning techniques to further enhance warehouse management systems.
Copyright © 2026 Khelvas A.V., Pankratov K.K., Afanasenko T.S., Gadzhimirzayev Sh.M., Saidov A.A., Pashkov R.A., Strelnikova S.A.
Indexed in Scopus
Full-text version of the journal is also available on the web site of the scientific electronic library eLIBRARY.RU
The journal is included in the Russian Science Citation Index
The journal is included in the RSCI
International Interdisciplinary Conference "Mathematics. Computing. Education"





