OPTIMUS: Optimization Productivity Tool for Intelligent Management of Utilizable Space
Abstract
We study department-level retail space optimization, where limited bay capacity must be allocated among planograms (POGs) under business and operational constraints. The problem is formulated as a linear binary knapsack model, with potential SKUs treated as items characterized by space requirements and weighted value contributions from sales, margin, units, and assortment similarity. Dynamic Programming (DP) is employed to obtain exact and reproducible assortment decisions in O(nc) time, avoiding the variance inherent in heuristic approaches. These decisions are integrated with a second-stage bay optimization model formulated as a mixed-integer program. Evaluated end-to-end across ten optimization runs spanning multiple departments and store clusters, the OPTIMUS framework achieves an average sales lift of 11.8% and an average margin lift of 9.5%. Overall, OPTIMUS provides a scalable, interpretable, and profit-driven solution for enterprise-scale retail space management.
Keywords
Cite
@article{arxiv.2605.14430,
title = {OPTIMUS: Optimization Productivity Tool for Intelligent Management of Utilizable Space},
author = {Souvik Bhattacharyya and Nisha Singh and Salman Haider and Balaji Nagarajan and Ved Prakash Dwivedi and Nithin Surendran and Karthik Nair},
journal= {arXiv preprint arXiv:2605.14430},
year = {2026}
}