A Two-Stage Ant Colony Optimization-Based Approach to the Storage Location Assignment Problem (SLAP): An Application in a Cable Manufacturing Facility

In this study, a two-stage approach based on K-Means clustering and Ant Colony Optimization (ACO) is proposed for solving the rack storage location assignment problem (SLAP) in a cable manufacturing facility where automated transport robots are used. In the first stage, 184 cables were clustered using the K-Means algorithm according to their monthly usage quantities across six production machines; the optimal number of clusters was determined as k=7 (silhouette score of 0.554) by jointly evaluating the elbow and silhouette analyses. In the second stage, a cost matrix was constructed by taking into account the usage-weighted access time of each cable to all the machines it serves, and cable-cell assignments were performed using the ACO algorithm. The ACO parameters were tuned using the Taguchi experimental design (L27 orthogonal array), and the resulting solutions were improved with Swap and Relocate local search operators. Applied to a real storage layout consisting of 18 rows × 30 columns with 504 available cells, the proposed approach reduced the total usage-weighted access cost to 1,608,326.24 usage-weighted seconds (usage count × s) after 1,000 iterations. The average usage-weighted access cost per cable was 8,740.90 usage-weighted seconds, with minimum and maximum values of 6.41 and 29,347.75 usage-weighted seconds, respectively. The results demonstrated that cables with high usage intensity were largely placed in cells closest to the machine supply points. The proposed approach, applicable to real industrial data, contributes as a data-driven decision-support tool to reducing robot movements and organizing storage areas more efficiently.