Integrating Mathematical Programming and Stakeholder Consensus for Warehouse Inventory Compression: A Case Study

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Author(s)

Nataliya Mutovkina 1,*

1. Department of Management and Social Communications, Tver State Technical University, Tver, 170012, Russia

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2026.04.08

Received: 20 Jan. 2026 / Revised: 10 Mar. 2026 / Accepted: 6 May 2026 / Published: 8 Aug. 2026

Index Terms

Coordinated Optimization, Compression of Inventory, Optimization in the Warehouse, Turnover, Efficiency of Warehouse Area, Compromise

Abstract

The article discusses methods for averting irrational product placement in warehouses. This problem is relevant for many manufacturing enterprises and trade organizations. The most common embodiments of the problem are unoccupied areas, or a lack of free storage space, difficulty in locating a specific product, and challenges in shipping it from the warehouse. All this leads to unnecessary costs for the business entity and hurts its financial and economic activities. The author suggests an integrated approach to warehousing. It integrates both classical optimization models and iterative approval procedures for accounting for the human factor. The key criteria in this case are minimizing costs, the cargo flow in the warehouse, and maximizing the utilization factor of the usable area. The optimal placement of goods is to achieve maximum compression of their residues in the warehouse while minimizing their movement. The presence of two contradictory criteria makes the task a task of consistent optimization. The article discusses the possibilities for solving the optimization problem when conflicting target criteria and differing preferences are present. We are using the example of a storage room with 16 racks for water heaters and similar equipment. As a result of matching optimization procedures, it was possible to reduce the average cost of moving goods by 7.1% and increase the free warehouse area by 15 times. We performed the experiments over the seven days of the warehouse’s operation. The practical value of the research is that, through this approach, we find a compromise in conditions of conflicting opinions and interests.

Cite This Paper

Nataliya Mutovkina, "Integrating Mathematical Programming and Stakeholder Consensus for Warehouse Inventory Compression: A Case Study", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.4, pp. 119-134, 2026. DOI:10.5815/ijieeb.2026.04.08

Reference

[1]Chhavi Gupta, Vipin Kumar, Kamesh Kumar, “An efficient technique for arranging various commodities in a warehouse,” Communications on Applied Nonlinear Analysis, Vol. 31, No. 3s, 2024, pp. 265‒276. DOI: 10.52783/cana.v31.764.
[2]Enoch Oluwademilade Sodiya, Uchenna Joseph Umoga, Olukunle Oladipupo Amoo and Akoh Atadoga, “AI-driven warehouse automation: A comprehensive review of systems,” GSC Advanced Research and Reviews, Vol. 18, No. 02, 2024, pp. 272–282. DOI: 10.30574/gscarr.2024.18.2.0063.
[3]J. Yuan, Y. Huang, L. Wang, Y. Tan, and Y. Chen, “Study on cargo volume forecasting and binning planning based on prophet time series and taboo search,” Highlights in Science Engineering and Technology, Vol. 158, Nov. 2025, pp. 168–177, DOI: 10.54097/74cnb623.
[4]Jie Gao, Weidong Xie, Dingke Shi, Jian Wu and Rui Wang, “Synchronized optimization of the logistics system of a tobacco high-bay warehouse under production task fluctuations,” Engineering Research Express, Vol. 6, No. 4, 2024, DOI: 10.1088/2631-8695/ad9f24.
[5]Iveta Kubasakova, Jaroslava Kubanova, “Utilization of the intersection of ABC and XYZ analysis in stock planning in the warehouse by Covid period,” Acta Logistica, Vol. 11, No. 03, Sep. 2024, pp. 461‒472. DOI: 10.22306/al.v11i3.532.
[6]Yuyun Yuniar Rohmatin, Bambang Dwinanto, “Optimization of warehouse inventory policy using ABC–XYZ analysis and the (Q,R) model to reduce total inventory cost and stockouts,” Jurnal Ilmiah Teknik, Vol. 5, No. 1, Jan. 2026, pp. 206‒222. DOI: 10.56127/juit.v5i1.1236.
[7]Peixuan Cheng, Cuixia Gu, Youning Zhang, Yue Chen, and Jun Bi, “Optimization strategy for storage location allocation in air cargo warehouse area,” E3S Web of Conferences, Vol. 512, April 2024. DOI: 10.1051/e3sconf/202451203011.
[8]Natalia Mamedova, Yulia Khizhnyakova, “Software implementation of genetic algorithm for optimization of cargo placement in the conditions of limited warehouse space,” Wseas Transactions on Computer Research, No. 13, April 2025, pp. 245‒258. DOI: 10.37394/232018.2025.13.23.
[9]Chhavi Gupta, Vipin Kumar, Kamesh Kumar, “Implementation and performance analysis of linear integer models in warehouse optimization,” Journal of Tianjin University Science and Technology, Vol. 58, No. 7, July 2025, pp. 55‒69. DOI: 10.5281/zenodo.15788565.
[10]Michael Heaviside, Bagus Mulyawan and Tri Sutrisno, “Determination of minimum stock on system retail using forecast, economic order quantity and reorder point methods,” IOP Conference Series: Materials Science and Engineering, Vol. 1007, No. 1, Dec. 2020. DOI: 10.1088/1757-899X/1007/1/012180.
[11]Viviane Kaseka Katadi, Richard Kitondua Lubanzadio Nkubukulu-Nzambi, Pierre Kafunda Katalay, Ibanga Mbayo Jean-Marie, “Hybrid machine learning framework for real-time inventory optimization in web-based decision support system,” Journal of Advances in Mathematics and Computer Science, Vol. 41, No. 4, March 2026, pp. 52‒72. DOI: 10.9734/jamcs/2026/v41i42118.
[12]Ahmed Hassaan, Zeeshan Akbar, Sikander Niaz, Muhammad Nouman Siddique, Salman Akbar, “Transforming supply chain operations through AI and machine learning: optimizing demand forecasting, inventory management, and logistics efficiency,” Journal of Posthumanism, Vol. 5, No. 12, Dec. 2025, pp. 532‒556. DOI: 10.63332/joph.v5i12.3860.
[13]Savanam Chandra Sekhar, “Hybrid demand forecasting: integrating behavioral economics with econometric and machine learning models,” Gulf Journal of Advance Business Research, Vol. 4, No. 2, March 2026, pp. 78‒93. DOI: 10.51594/gjabr.v4i2.207.
[14]M. Nikfar, S. Mohammadi, K.G. Nodooshan, and M. Ansari, “The walking map: a novel heuristic for storage location assignment in warehouse operations,” Conference: 10th Annual World Conference of the Society for Industrial and Systems Engineering, At: 2021 SISE Virtual Conference, September 23-24, 2021, pp. 134‒140. URL: https://www.researchgate.net/publication/356084799_The_Walking_Map_A_Novel
_Heuristic_for_Storage_Location_Assignment_in_Warehouse_Operations
(accessed: 22.04.2026).
[15]A. K. Malde, T. Işik, R. Lockaby, A. Gantt and G. Thumser, “Optimal team formation and job assignment to optimize warehouse operations,” 2022 Winter Simulation Conference (WSC), Singapore, Dec. 2022. DOI: 10.1109/WSC57314.2022.10015444.
[16]D. A. Koval’, L.S. Skripnichenko, “Assessment as a method of personnel selection,” Management of the Personnel and Intellectual Resources in Russia, No. 1, 2025, pp. 87‒91. DOI: https://doi.org/10.12737/2305-7807-2025-14-1-87-91.
[17]Ping-Qi Pan, “Simplex method: the state of the art,” In book: Face Method, Edition: First, Chapter: 1, Publisher: Springer, June 2025, pp. 1‒37. DOI: 10.1007/978-3-031-93594-7_1.
[18]Wiku Larutama, Dewang Bentar, Rifqy Oktavian Risdayanto, Ridwan Salman Alvariedz, “Implementation of warehouse management system planning in finished goods warehouse,” Journal of Logistics and Supply Chain, Vol. 2, No. 2, Oct. 2022, pp. 81‒90. DOI: 10.17509/jlsc.v2i2.62840.
[19]Xuan Zhu, Nian Lu, “A study on the optimization of company K’s finished goods warehouse layout based on SLP,” International Journal of Global Economics and Management, Vol. 10, No. 3, March 2026, pp. 168‒183. DOI: 10.62051/ijgem.v10n3.17.
[20]Yifan Shen, Yansong Wu, Ning Zhao, Weijian Mi, “A reinforcement learning method for container terminal storage space allocation problem,” AIP Advances, Vol. 15, No. 9, Sept. 2025. https://doi.org/10.1063/5.0280674.
[21]Zhang Dingnan, Boyang Liu, Enqi Yue, and Dongsheng Wu, “Integrated optimization framework for AS/RS: coupling storage allocation, collaborative scheduling, and path planning via hybrid meta-heuristics,” Applied Sciences, Vol. 16, No. 8, 2026. https://doi.org/10.3390/app16083757.
[22]Wang Xiaohan, Zhihong Jin, and Jia Luo, “Joint allocation of shared yard space and internal trucks in sea-rail intermodal container terminals,” Journal of Marine Science and Engineering, Vol. 13, No. 5, 2025. https://doi.org/10.3390/jmse13050983.
[23]Doszhan Mambetalin, Abay Koshekov, Bibigul Orazbayeva, Aidos Moldabekov, Talshyn Keribayeva, “Application of machine learning to storage allocation decision-making system within air cargo terminals,” Acta Logistica, Vol. 13, No. 1, 2026, pp. 52‒65. URL: https://actalogistica.eu/issues/2026/I_2026_05_Mambetalin_Koshekov_Orazbayeva_Moldabekov_Keribayeva.pdf.
[24]Rizky Aprilianti Lestari, Yogi Catur Putra, Melan Handayani, “Analysis of the implementation of the FIFO (first in, first out) principle in optimizing pharmacy warehouse layout: a case study of logistics improvement at RSI Metro,” Journal of Hospital Administration and Management, Vol. 6, No. 2, Dec. 2025, pp. 137‒150. DOI: 10.54973/jham.v6i2.761.
[25]Lawrence Ip, “The conciliation of thought framework: a recursive cognitive model for adaptive decision-making and AI integration,” ResearchGate, Sept. 2024. URL: https://www.researchgate.net/publication/384045356_The_Conciliation
_of_Thought_Framework_A_Recursive_Cognitive_Model_for_Adaptive_Decision-Making_and_AI_Integration
(accessed: 22.04.2026).