Published Online:August 2026
Product Name:The IUP Journal of Operations Management
Product Type:Article
Product Code:IJOM010726
DOI:10.71329/IUPJOM/2026.25.3.5-35
Author Name:Sandeep Bhattacharjee
Availability:YES
Subject/Domain:Management
Download Format:PDF
Pages:5-35
The study develops an attention-driven Firefly optimization (ADFO) framework for multi-warehouse inventory optimization under demand uncertainty, jointly optimizing order quantity (Q), reorder point (R), and safety stock (SS) to minimize total inventory cost. Using the publicly available Rossmann store sales dataset, ADFO is evaluated against genetic algorithm (GA), particle swarm optimization (PSO), and Gray wolf optimizer (GWO) through 30 independent runs, convergence and sensitivity analyses, and nonparametric statistical testing. PSO achieved the lowest mean inventory cost of 257.03 million, while ADFO achieved a competitive mean cost of 261.79 million and demonstrated intermediate variability across repeated runs. Friedman testing showed there were significant differences between the algorithms, while the pairwise comparisons also yielded significant results. The originality of this study lies in integrating attention-guided search with Firefly optimization for joint Q-R-SS inventory decisions in a multi-warehouse setting. The findings provide practical decision support for balancing inventory cost, replenishment responsiveness, and robustness under changing demand and lead-time conditions.
In multi-warehouse retail systems, inventory management requires the simultaneous determination of replenishment quantity (Q), reorder point (R), and safety stock (SS) under uncertain and diversified demand.