Towards Smarter Warehouse Layouts: Simulation-Driven Insights on Congestion and Forklift Flow Patterns
Behavioral Flow Modeling; Discrete Event Simulation; Forklift Logistics; Replication-Based Analysis; Warehouse Slotting Optimization
In high-mix, make-to-order warehouse environments, slotting decisions in constrained warehouse settings affect flow dynamics, yet their behavioral implications remain underexplored. This study employs discrete event simulation (DES) using the software FlexSim to evaluate three slotting strategies: baseline reflecting current operations, a benchmark informed by heuristic frequency-based clustering, and a proposed layout based on a learned configuration within a forklift-operated warehouse characterized by narrow aisles, unidirectional traffic, and spatial contention. Instead of emphasizing throughput alone, the study takes a closer look at how forklifts operate on a day-to-day basis, specifically how their time is divided between active movement, waiting due to blockages, and idling with no task assigned. Each strategy was tested over 20 simulation replications. Notably, the proposed layout cut blocked time by more than 30% and allowed forklifts to remain idle (and ready) more often, without reducing overall utilization. These patterns held consistently across runs. Statistical analysis confirmed that the differences in forklift behavior were significant, and Levenes tests showed that performance didnt become more erratic. These findings demonstrate that the improvements are systematic, not random. The work presents a simulation-based method for diagnosing layout effectiveness by looking at behavior, not just outputs, connecting slotting choices to real operational flow and system stability. This approach supports more resilient warehouse designs in settings with limited space and high product mix. 2025 IEEE.
El Baz, Hicham; Parag Jose, C.; Bhat, Vinayambika S.; Wang, Yong
Proceedings of 2025 International Conference on Intelligent Systems and Pioneering Innovations in Robotics and Electric Mobility: Transforming Mobility and Automation Through Intelligent Engineering, INSPIRE 2025;pp.578-582
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/INSPIRE67328.2025.11300596 https://www.scopus.com/pages/publications/105032428880?origin=resultslist
El Baz H., School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, United States; Parag Jose C., Department of Electrical and Electronics Engineering, Christ University, Karnataka, India; Bhat V.S., Department of Electronics & Communication Engineering, Mangalore Institute of Technology & Engineering, Karnataka, India; Wang Y., School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, United States