Multi-Criteria Optimization of Component Placement in an Automated Warehouse

The problem of effectively placing components in an automated warehouse is key to enhancing the productivity of logistical processes and meeting modern requirements for order fulfillment speed in the context of Industry 4.0. Traditional storage approaches—including ABC analysis and single-objective optimization based on the popularity of components—often lead to an uneven distribution of the most demanded items across zones, excessive load on certain areas, and unjustified raising of heavy components to high racks. This reduces the system’s flexibility in the face of demand changes and complicates maintaining the required service level (SLA). The aim of the study was to develop a multi-criteria model for component placement that simultaneously minimizes the access time to items, balances the workload across different warehouse zones, and avoids placing heavy components on high-level racks. The proposed approach is based on taking into account three interrelated criteria—picking time, balance of component popularity among zones, and compliance with weight and dimensional constraints of storage cells—with the possibility of adjusting priorities through weighted coefficients. To verify the model’s adequacy, several validation methods were employed: residual analysis of the fulfillment of structural constraints, sensitivity analysis to changes in weighted coefficients, and comparison of the results with benchmark scenarios. The proposed validation procedure ensures that the solutions meet technical requirements, prevent violations of the storage cells’ load capacity and volume, and guarantee a balanced load across automated zones. The results obtained indicate the feasibility of using multi-criteria optimization as an alternative to traditional storage methods. This approach allows for flexible adaptation of component placement strategies in accordance with business priorities and current production constraints. The proposed model can be integrated into warehouse management information systems (ERP/MES) for automated planning and reconfiguration of item placement, thereby improving the accuracy of order picking, reducing operating costs, and enhancing the overall efficiency of automated warehouses.