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Integrated Assortment–Shelf Optimization under Substitution and Space Elasticity: A Hybrid Memetic Algorithm | ||
| Industrial Management Journal | ||
| دوره 18، شماره 2، 2026، صفحه 1-33 اصل مقاله (1.47 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/imj.2026.404854.1008268 | ||
| نویسندگان | ||
| Ali Ebrahimi Kordler* 1؛ Saleh Mansouri2؛ Farzad Bahrami3 | ||
| 1Associate Prof., Department of Accounting and Finance, College of Management, University of Tehran, Tehran, Iran. | ||
| 2Ph.D. Candidate, Department of Industrial Management, School of Management, Kish International Campus, University of Tehran, Kish Island, Iran. | ||
| 3Assistant Prof., Department of Industrial Management, Faculty of Administrative Sciences and Economics, Arak University, Arak, Iran. | ||
| چکیده | ||
| Objective: This study maximizes expected retail profit by jointly optimizing product assortment and shelf-space allocation, considering substitution effects and space-elastic demand. The problem’s NP-hardness, compounded by category-level bounds and store capacity, renders exact methods impractical for large-scale instances. Therefore, we develop a computationally efficient framework to generate near-optimal shelf plans aligned with real-world constraints. Methodology: We propose a hybrid Memetic Algorithm embedded with Iterated Local Search (ILS), combining evolutionary global exploration with local hill-climbing refinement. A two-phase initialization ensures every candidate planogram satisfies capacity constraints. Each chromosome encodes item-to-shelf mappings, with facings emerging endogenously. Mechanisms, including crossover, mutation, and diversity control, preserve solution validity and mitigate premature convergence. The framework was validated using real data from the Iranian retail chain Ofoq Kourosh, encompassing 39 product categories and 21,000 cm of total shelf length. Results: The algorithm consistently converged toward feasible solutions. Under the original category bounds, profit reached 3.04 million; relaxing these bounds improved profit by 6.2% to 3.25 million. Allocation outcomes aligned with demand elasticity: impulse-driven categories reached upper limits, while low-elasticity staples stabilized near minima. Pareto analysis confirmed that roughly 20% of categories generated over 80% of profit. Notably, the optimized solution resulted in a 37% increase in profit compared to the current store configuration. Conclusion: Results confirm the efficacy of hybrid metaheuristics for complex retail optimization. The framework consistently achieved near-optimal solutions under realistic constraints. Managerially, shelf-space allocation should prioritize high-elasticity categories while maintaining a minimal representation of staples. Future research should extend this framework to multi-store and omni-channel contexts with dynamic demand modeling. | ||
| کلیدواژهها | ||
| Assortment planning؛ shelf-space optimization؛ substitution effects؛ metaheuristics؛ local search | ||
| مراجع | ||
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Abbasnia, F., Zandieh, M., Bahrami, F., & Pourhejazy, P. (2025). A Decision Analysis Framework for the Identification and Performance Preservation of Strategic Products in the Supply Chain. Logistics, 9(3), 89. https://doi.org/10.3390/logistics9030089
Abbaszadeh, A., Iranpoor, M., Mahnam, M., & Bahrami, F. (2025). Optimisation of assortment breadth and allocation of the selected product groups to the two-dimensional shelves. International Journal of Systems Science: Operations & Logistics, 12(1), 2478426. https://doi.org/10.1080/23302674.2025.2478426
Agarwal, K., Jain, P., & Rajnayak, M. A. (2019). Comparative Analysis of Store Clustering Techniques in the Retail Industry. DATA, https://doi.org/10.5220/0007917500650073
Anderson, E. E., & Amato, H. N. (1974). A mathematical model for simultaneously determining the optimal brand-collection and display-area allocation. Operations Research, 22(1), 13–21. https://doi.org/10.1287/opre.22.1.13
Aouad, A., Farias, V., Levi, R., & Segev, D. (2018). The approximability of assortment optimization under ranking preferences. Operations Research, 66(6), 1661–1669. https://doi.org/10.1287/opre.2018.1754
Bahrami, F. (2025). Many-to-many location-routing problem with multiple paths, heterogeneous vehicles and time windows. Industrial Management Journal. https://doi.org/10.22059/imj.2025.402989.1008266
Baron, O., Berman, O., & Perry, D. (2011). Shelf space management when demand depends on the inventory level. Production and operations management, 20(5), 714–726. https://doi.org/10.1111/j.1937-5956.2010.01201.x
Bianchi-Aguiar, T., Hübner, A., Carravilla, M. A., & Oliveira, J. F. (2021). Retail shelf space planning problems: A comprehensive review and classification framework. European Journal of Operational Research, 289(1), 1–16. https://doi.org/10.1016/j.ejor.2020.06.018
Brandimarte, P., Craparotta, G., & Marocco, E. (2024). Inventory reallocation in a fashion retail network: A matheuristic approach. European Journal of Operational Research, 317(2), 603–615. https://doi.org/10.1016/j.ejor.2024.04.016
Chen, X., Ma, W., Simchi-Levi, D., & Xin, L. (2024). Assortment planning for recommendations at checkout under inventory constraints. Mathematics of Operations Research, 49(1), 297–325. https://doi.org/10.1287/moor.2023.1357
Çömez-Dolgan, N., Fescioglu-Unver, N., Cephe, E., & Şen, A. (2021). Capacitated strategic assortment planning under explicit demand substitution. European Journal of Operational Research, 294(3), 1120-1138. https://doi.org/10.1016/j.ejor.2021.02.026
Curhan, R. C. (1972). The relationship between shelf space and unit sales in supermarkets. Journal of Marketing Research, 9(4), 406–412. https://doi.org/10.2307/3149304
Czerniachowska, K., Michalak, K., & Hernes, M. (2023). Heuristics for the shelf space allocation problem. Opsearch, 60(2), 835-869. https://doi.org/10.1007/s12597-023-00636-1
Czerniachowska, K., Sachpazidu-Wójcicka, K., Sulikowski, P., Hernes, M., & Rot, A. (2021). Genetic algorithm for the retailers’ shelf space allocation profit maximization problem. Applied Sciences, 11(14), 6401. https://doi.org/10.3390/app11146401
Désir, A., Goyal, V., Jiang, B., Xie, T., & Zhang, J. (2024). Robust assortment optimization under the Markov chain choice model. Operations Research, 72(4), 1595–1614. https://doi.org/10.1287/opre.2022.2420
Dreze, X., Hoch, S. J., & Purk, M. E. (1994). Shelf management and space elasticity. Journal of Retailing, 70(4), 301–326. https://doi.org/10.1016/0022-4359(94)90002-7
Eisend, M. (2014). Shelf space elasticity: A meta-analysis. Journal of Retailing, 90(2), 168–181. https://doi.org/10.1016/j.jretai.2013.03.003
Farias, V. F., Jagabathula, S., & Shah, D. (2013). A nonparametric approach to modeling choice with limited data. Management science, 59(2), 305–322. https://doi.org/10.1287/mnsc.1120.1610
Geismar, H. N., Dawande, M., Murthi, B., & Sriskandarajah, C. (2015). Maximizing revenue through two‐dimensional shelf‐space allocation. Production and operations management, 24(7), 1148–1163. https://doi.org/10.1111/poms.12316
Haji, R., Moarefdoost, M., & Ebrahimi, B. (2009). Finding the cost of inventory in make to order supply chain under vendor managed inventory program. Industrial Management Journal, 1(3). https://dor.isc.ac/dor/20.1001.1.20085885.1388.1.3.2.1
Hansen, J. M., Raut, S., & Swami, S. (2010). Retail shelf allocation: A comparative analysis of heuristic and meta-heuristic approaches. Journal of Retailing, 86(1), 94–105. https://doi.org/10.1016/j.jretai.2010.01.004
Hansen, P., & Heinsbroek, H. (1979). Product selection and space allocation in supermarkets. European Journal of Operational Research, 3(6), 474-484. https://doi.org/10.1016/0377-2217(79)90030-4
Heger, J., & Klein, R. (2024). Assortment optimization: A systematic literature review. OR Spectrum, 46(4), 1099–1161. https://doi.org/10.1007/s00291-024-00752-4
Hense, J., & Hübner, A. (2022). Assortment optimization in omni-channel retailing. European Journal of Operational Research, 301(1), 124–140. https://doi.org/10.1016/j.ejor.2021.09.045
Hübner, A. (2011). Retail category management: decision support systems for assortment, shelf space, inventory and price planning (Vol. 656). Springer Science & Business Media. https://doi.org/10.1007/978-3-642-22477-5_5
Hübner, A., & Kuhn, H. (2024). Decision support for managing assortments, shelf space, and replenishment in retail. Flexible Services and Manufacturing Journal, 36(1), 1–35. https://doi.org/10.1007/s10696-023-09492-z
Hübner, A., Kuhn, H., & Kühn, S. (2016). An efficient algorithm for capacitated assortment planning with stochastic demand and substitution. European Journal of Operational Research, 250(2), 505–520. https://doi.org/10.1016/j.ejor.2015.11.007
Hübner, A., & Schaal, K. (2017). An integrated assortment and shelf-space optimization model with demand substitution and space-elasticity effects. European Journal of Operational Research, 261(1), 302–316. https://doi.org/10.1016/j.ejor.2017.01.039
Hübner, A., Schäfer, F., & Schaal, K. N. (2020). Maximizing profit via assortment and shelf‐space optimization for two‐dimensional shelves. Production and operations management, 29(3), 547–570. https://doi.org/10.1111/poms.13111
Hübner, A. H., & Kuhn, H. (2012). Retail category management: State-of-the-art review of quantitative research and software applications in assortment and shelf space management. Omega, 40(2), 199–209. https://doi.org/10.1016/j.omega.2011.05.008
Hwang, H., Choi, B., & Lee, M.-J. (2005). A model for shelf space allocation and inventory control considering location and inventory level effects on demand. International Journal of Production Economics, 97(2), 185–195. https://doi.org/10.1016/j.ijpe.2004.07.003
Kök, A. G., & Fisher, M. L. (2007). Demand estimation and assortment optimization under substitution: Methodology and application. Operations Research, 55(6), 1001–1021. https://doi.org/10.1287/opre.1070.0409
Kuhn, D., Shafiee, S., & Wiesemann, W. (2025). Distributionally robust optimization. Acta Numerica, 34, 579–804. https://doi.org/10.1017/s0962492924000084
Leitner, M., Lodi, A., Roberti, R., & Sole, C. (2024). An exact method for (constrained) assortment optimization problems with product costs. INFORMS Journal on Computing, 36(2), 479–494. https://doi.org/10.1287/ijoc.2022.0262
Lotfi, M., Rabbani, M., & Ghaderi, S. (2011). A weighted goal programming approach for replenishment planning and space allocation in a supermarket. Journal of the Operational Research Society, 62(6), 1128–1137. https://doi.org/10.1057/jors.2010.52
Mohaghar, F., Jolai, F., & Heydari, J. (2020). Modeling a Four Echelon Omni-Channel Supply Chain for Seasonal Product under Stochastic Demand. Industrial Management Journal, 12(2), 206–235. https://doi.org/10.22059/imj.2020.306753.1007759
Morrison, R. W., & De Jong, K. A. (2001). Measurement of population diversity. International conference on artificial evolution (evolution artificielle), https://doi.org/10.1007/3-540-46033-0_3
Murray, C. C., Talukdar, D., & Gosavi, A. (2010). Joint optimization of product price, display orientation and shelf-space allocation in retail category management. Journal of Retailing, 86(2), 125–136. https://doi.org/10.1016/j.jretai.2010.02.008
Neri, F., Cotta, C., & Moscato, P. (2011). Handbook of memetic algorithms (Vol. 379). Springer. https://doi.org/10.1007/978-3-642-23247-3
OfoqKourosh. (2025). Ofoq Kourosh Annual General Meeting. https://okcs.com/posts/view/639
Pham, T., Mathmann, F., Septianto, F., & Chylinski, M. (2025). The impact of assortment size and population density on product evaluation. Journal of Retailing. https://doi.org/10.1016/j.jretai.2025.02.004
Russell, S. J., & Norvig, P. (2010). Artificial Intelligence-A Modern Approach, Third International Edition. http://aima.cs.berkeley.edu/
Sethuraman, R., Gázquez-Abad, J. C., & Martínez-López, F. J. (2022). The effect of retail assortment size on perceptions, choice, and sales: Review and research directions. Journal of Retailing, 98(1), 24–45. https://doi.org/10.1016/j.jretai.2022.01.001
Shakir Hameed, A., Alrikabi, H. M. B., Abdul–Razaq, A. A., Ahmed, Z. H., Nasser, H. K., & Mutar, M. L. (2023). Appling the roulette wheel selection approach to address the issues of premature convergence and stagnation in the discrete differential evolution algorithm. Applied Computational Intelligence and Soft Computing, 2023(1), 8892689. https://doi.org/10.1155/2023/8892689
Yu, Y., Wang, B., & Zheng, S. (2024). Data-driven product design and assortment optimization. Transportation Research Part E: Logistics and Transportation Review, 182, 103413. https://doi.org/10.1016/j.tre.2024.103413
Ziari, M., & Sajadieh, M. S. (2025). Shelf space allocation in retailing: a literature review. RAIRO-Operations Research, 59(5), 2721-2748. https://doi.org/10.1051/ro/2025037 | ||
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