Development of a Mathematical Model for Optimal Pricing Strategies for Perishable Goods
Main Article Content
Abstract
This study introduces a deterministic linear programming (LP) framework aimed at enhancing dynamic pricing strategies for perishable products with predictable demand trends. Since the value of these items declines over time due to spoilage or becoming outdated, pricing decisions must be made carefully and promptly. The proposed model seeks to maximize total revenue within a defined time frame by incorporating constraints related to inventory levels, time-dependent product depreciation, and the relationship between price and demand. In contrast to heuristic or stochastic models, this approach relies on fixed demand forecasts and known deterioration rates, enabling efficient and transparent solutions using conventional LP solvers. A practical example in a retail perishables items illustrates how the model improves profitability while minimizing waste. The results show that increased leads to higher optimal quantities and slightly lower prices to capture demand and the total revenue realized is N94, 500.
Article Details
References
Abbas, H., Zhao, L., Gong, X., & Faiz, N. (2023). The perishable products case to achieve sustainable food quality and safety goals implementing on-field sustainable supply chain model. Socio-Economic Planning Sciences, 87, 101562.
Azab, R., Mahmoud, R. S., Elbehery, R., & Gheith, M. (2023). A Bi-Objective Mixed-Integer Linear Programming Model for a Sustainable Agro-Food Supply Chain with Product Perishability and Environmental Considerations. Logistics, 7(3), 46.
FAO. 2023. The Impact of Disasters on Agriculture and Food Security. Food and Agriculture Organization.
George, A. S. (2024). Realizing the Promise of Dynamic Pricing Through Responsible Innovation. Partners Universal International Research Journal, 3(3), 21-37.
Gonen, L. D., Tavor, T., & Spiegel, U. (2024). Unlocking Market Potential: Strategic Consumer Segmentation and Dynamic Pricing for Balancing Loyalty and Deal Seeking. Mathematics, 12(21), 3364.
Guchhait Haugen, M., Farahmand, H., Jaehnert, S., & Fleten, S. E. (2023). Representation of uncertainty in market models for operational planning and forecasting in renewable power systems: a review. Energy Systems, 1-36.
Hemmati, H., Baradaran Kazemzadeh, R., Nikbakhsh, E., & Nakhai Kamalabadi, I. (2023). Designing a Green-Resilient Supply Chain Network for Perishable Products Considering a Pricing Reduction Strategy to Manage Optimal Inventory: A Column Generation-based Approach. Journal of Quality Engineering and Production Optimization, 8(1), 171-196.
Iannacone, L., & Gardoni, P. (2024). Modeling deterioration and predicting remaining useful life using stochastic differential equations. Reliability Engineering & System Safety, 251, 110251.
Jalving, J., Ghouse, J., Cortes, N., Gao, X., Knueven, B., Agi, D., ... & Dowling, A. W. (2023). Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates. Applied Energy, 351, 121767.
Kopalle, P. K., Pauwels, K., Akella, L. Y., & Gangwar, M. (2023). Dynamic pricing: Definition, implications for managers, and future research directions. Journal of Retailing, 99(4), 580-593.
Morán-Figueroa, G. H., Muñoz-Pérez, D. F., Rivera-Ibarra, J. L., & Cobos-Lozada, C. A. (2024). Model for Predicting Maize Crop Yield on Small Farms Using Clusterwise Linear Regression and GRASP. Mathematics, 12(21), 3356.
Ni, X., Yan, L., Xiong, K., & Liu, Y. (2024). A Hierarchical Bayesian Market Mix Model with Causal Inference for Personalized Marketing Optimization. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 378-396.
Noble, J., John, K., & Paul, B. (2023). Inventory management of perishable products with fixed shelf life for a single echelon system. Materials Today: Proceedings, 72, 2863-2868.
Sanders, R. E. (2024). Dynamic pricing and organic waste bans: A study of grocery retailers’ incentives to reduce food waste. Marketing Science, 43(2), 289-316.
Syed, T. A., Aslam, H., Bhatti, Z. A., Mehmood, F., & Pahuja, A. (2024). Dynamic pricing for perishable goods: A data-driven digital transformation approach. International Journal of Production Economics, 277, 109405.
Winkler, T., Ostermeier, M., & Hübner, A. (2023). Proactive food waste prevention in grocery retail supply chains–An exploratory study. International Journal of Physical Distribution & Logistics Management, 53(11), 125-156.
Yavuz, T., & Kaya, O. (2024). Deep reinforcement learning algorithms for dynamic pricing and inventory management of perishable products. Applied Soft Computing, 163, 111864.
Yegane, B. Y. (2023). An Integrated Production-distribution Problem of Perishable Items with Dynamic Pricing Consideration in a Three-echelon Supply Chain. International Journal of Engineering, 36(11), 2038-2051.
Yontar, E. (2023). Critical success factor analysis of blockchain technology in agri-food supply chain management: A circular economy perspective. Journal of Environmental Management, 330, 117173.
Zakaria, A. F., Lim, S. C. J., & Aamir, M. (2024). A pricing optimization modelling for assisted decision making in telecommunication product-service bundling. International Journal of Information Management Data Insights, 4(1), 100212.