A New Formulation for Class-Based Storage Location Assignment Problem to Minimize Pick Distance in Unit-Load Warehouses

Efficiently assigning storage locations in a ware-house is crucial to minimizing picker travel time while respecting space and operational constraints. In class-based storage (CBS) systems, misaligned product placement can lead to excessive pick distances or underutilized zones. In this paper, a CBS policy is used to group products into distinct classes and allocate each class to its own dedicated zone, alongside the introduction of enhanced constraints for an existing optimization model. The model ranks products according to the Cube-Per-Order Index of the product p ($\text{COI} _{\mathrm{p}}$) and sorts the storage slots by Manhattan distance from the input-output point, then allocates $\text{COI} _{\mathrm{p}}$ sorted products and their classes to the contiguous slots sorted by distance. The new formulation remains directly solvable by commercial solvers, specifically by genetic algorithm (GA), and is validated in three instances (5 stock keeping units, 50 stock keeping units, and 100 stock keeping units partitioned into two classes). This demonstrates both significant travel distance savings over standard CBS heuristics and the practical deployability of the model.