International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 16, No. 4, 8 Aug. 2026
Cover page and Table of Contents: PDF (size: 1008KB)
Shelf Space Optimization in Retail: A Study Using Linear Programming, Genetic Algorithm, and Proximal Policy Optimization
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Author(s)
Anitha Palakshappa
1,*
Shruti J. R.
1
Sowmya Kyathanahalli Nanjappa
2
Ashwitha Anni
3
Aditya Gaonkar
1
Bhawna Botra
1
Index Terms
Shelf Space optimization, linear programming, genetic algorithm, proximal policy optimization, sales forecasting, inventory management, interactive dashboard, seasonal demand analysis
Abstract
The aim is to design a comprehensive shelf space optimization framework that maximizes profitability, enhances sales forecasting, improves efficiency of inventory management, and supports effective decision-making in retail businesses. A robust and interactive analytical dashboard is developed that allows users to visualize critical sales metrics, analyze historical data trends, and accurately forecast product demand and supply requirements based on seasonal variations and sales performance. The work integrates three mathematical optimization paradigms like Linear Programming (LP), metaheuristic search via Genetic Algorithms (GA), and reinforcement learning using Proximal Policy Optimization (PPO) to support both static and adaptive allocation strategies. Experimental validation highlights the relative advantages of each method, with detailed evaluations based on forecast accuracy, inventory turnover efficiency, shelf utilization rate, and overall improvement in profitability. Unlike traditional static optimization models, the PPO-based framework continuously adapts allocation decisions using environmental feedback, improving flexibility in dynamic retail scenarios The paper uses multi-objective shelf optimization considering profitability, utilization, and customer demand simultaneously. The results demonstrate that the integration of predictive analytics and advanced optimization techniques significantly performs traditional shelf management approaches, offering retailers actionable insights and operational advantages.
Cite This Paper
Anitha Palakshappa, Shruti J. R., Sowmya Kyatanahalli Nanjappa, Ashwitha Anni, Aditya Gaonkar, Bhawna Botra, “Shelf Space Optimization in Retail: A Study Using Linear Programming, Genetic Algorithm, and Proximal Policy Optimization”, International Journal of Education and Management Engineering (IJEME), Vol.16, No.4, pp. 45-60, 2026. DOI:10.5815/ijeme.2026.04.04
Reference
[1]Frazzon, E. M., Rodriguez, C. M. T., Pereira, M. M., Pires, M. C., & Uhlmann, I. (2019). Towards supply chain management 4.0. Brazilian Journal of Operations & Production Management, 16(2), 180-191.doi: 10.14488/BJOPM.16.2.180.2019.
[2]F. M. Isa, W. N. M. Ariffin, and M. S. Jusoh, “Shelf space allocation problem (SSAP) in the retail industry: A systematic literature review,” International Journal of Business Society, vol. 25, no. 3, 2024.
[3]H. Riazi, M. Doroodian, and B. Afshar-Nadjafi, “Improving the sales process of profitable perishable goods: an inventory control strategy in a planned economy,” International Journal of Retail Distribution Management, vol. 52, no. 6, pp. 721–735, 2024.doi: 10.1108/IJRDM-03-2023-0104.
[4]M. Afsahi, “A bi-level, bi-objective optimization model integrating promotion, assortment, shelf-space, and inventory replenishment in supermarkets,” Assortment, Shelf-Space, and Inventory Replenishment in Supermarkets, 2024.
[5]M. Ostermeier, T. Du¨sterho¨ft, and A. Hu¨bner, “A model and solution approach for store-wide shelf space allocation,” Omega, vol. 102, p. 102425, 2021.doi: 10.1016/j.omega.2021.102425.
[6]C. Gencosman and M. A. Begen, “Exact optimization and decom- position approaches for shelf space allocation,” European Journal of Operational Research, vol. 299, no. 2, pp. 432–447, 2022.doi: 10.1016/j.ejor.2021.12.028.
[7]K. Czerniachowska, “A genetic algorithm for the retail shelf space allocation problem with virtual segments,” Opsearch, vol. 59, no. 1, pp. 364–412, 2022.doi: 10.1007/s41269-021-00276-0.
[8]C. Murray, D. Talukdar, and A. Gosavi, “Joint optimization of product price, display orientation and shelf-space allocation in retail category management,” Journal of Retailing, vol. 86, no. 2, pp. 125–136, 2022.doi: 10.1016/j.jretai.2019.03.004.
[9]S. J. Sajadi and A. Ahmadi, “An integrated optimization model and metaheuristics for assortment planning, shelf space allocation, and inventory management of perishable products: a real application,” PLOS ONE, vol. 17, no. 3, p. e0264186, 2022.doi: 10.1371/journal.pone.0264186.
[10]K. Czerniachowska and M. Hernes, “A heuristic approach to shelf space allocation decision support including facings, capping, and nesting,” Symmetry, vol. 13, no. 2, p. 314, 2021.doi: 10.3390/sym13020314.
[11]Frontoni, F. Marinelli, R. Rosetti, and P. Zingaretti, “Shelf space re-allocation for out of stock reduction,” Computers & Industrial Engi- neering, vol. 106, pp. 32–40, 2017.doi: 10.1016/j.cie.2017.02.007.
[12]T. Flamand, A. Ghoniem, and B. Maddah, “Promoting impulse buying by allocating retail shelf space to grouped product categories,” Journal of the Operational Research Society, vol. 67, no. 7, pp. 953–969, 2016.doi: 10.1057/jors.2015.103.
[13]K. Higuchi and K. Takeyasu, “Optimization in allocating goods to shop shelves utilizing genetic algorithm under expanded shelf position case,” Journal of Computations & Modelling, vol. 6, no. 2, pp. 15–31, 2016.
[14]S. S. Rautaray, M. Pandey, S. Chakraborty, and K. A. Barua, “Proposal for shelf placement optimization for retail industry using big data analytics,” in Data Science Congress, Mumbai, India, 2017.
[15]Y. Duan, Z. Mao, and J. Huo, “Introduction of store brands considering product cost and shelf space opportunity cost,” Mathematical Problems in Engineering, pp. 1–19, 2018.doi: 10.1155/2018/5092476.
[16]H. K. Gajjar and G. K. Adil, “Retail shelf space allocation considering inventory replenishment,” International Journal of Services and Operations Management, vol. 22, no. 2, pp. 221–234, 2015.doi: 10.1504/IJSOM.2015.069401.
[17]H. N. Geismar et al., “Maximizing revenue through two-dimensional shelf-space allocation,” Production and Operations Management, vol. 24, no. 7, pp. 1148–1163, 2015.doi: 10.1111/poms.12326.
[18]Khan, T., & Emon, M. M. H. (2025). The role of digital supply chain practices in enhancing firm performance: insights from the manufacturing sector of Bangladesh. Brazilian Journal of Operations & Production Management, 22(2), 2493 . https://doi.org/10.14488/BJOPM.2493.2025.doi: 10.14488/BJOPM.2493.2025.
[19]Hübner, A., & Schaal, K. (2017). A shelf-space optimization model when demand is stochastic and space-elastic. Omega, 68, 139-154.doi: 10.1016/j.omega.2016.08.007.