Hybrid optimization of EDLP and high-low pricing strategies

Abstract: In today's fiercely competitive retail landscape, implementing effective pricing strategies is critical not only for boosting sales but also for securing a larger market share and ensuring long-term business sustainability. The ability to capture a greater share of the market directly influences a retailer's positioning and competitive edge, making pricing decisions pivotal. This paper introduces a hybrid optimization model that strategically combines Everyday Low Pricing (EDLP) and High-Low Pricing (HL) strategies, designed to address the intricacies of dynamic retail markets. The model is initially formulated as a nonlinear optimization problem aimed at maximizing sales to increase market share, all while maintaining profitability within a predefined threshold to ensure the retailer does not incur losses. To enhance the model's practical applicability, particularly in small-scale scenarios, the nonlinear problem is transformed into a Mixed-Integer Programming (MIP) model, facilitating its solvability. However, as retail applications scale up, the computational complexity becomes more challenging, necessitating the use of the Grey Wolf Optimization (GWO) algorithm. The GWO algorithm effectively balances computational efficiency with solution quality, making it a robust approach for large-scale problems. A significant contribution of this research is the linearization of the model under conditions where the products designated for High-Low pricing (referred to as 'Golden' products) are predetermined by the retailer. This linearization simplifies the computational process, enabling the model to scale and be applied in large retail settings. Developed in collaboration with a major Iranian supermarket chain, the model leverages real-world data to optimize discount levels and timing across various product categories. Extensive numerical experiments demonstrate the model's effectiveness in increasing sales, thereby contributing to a larger market share while ensuring that profitability remains within acceptable bounds. By providing actionable insights and strategic recommendations, this research offers a practical, scalable solution for optimizing retail pricing strategies in a data-driven and competitive environment, ultimately supporting retailers in their quest to dominate the market.

How to cite this paper

 Karimi, H. (2025). Hybrid optimization of EDLP and high-low pricing strategies.Management Science Letters , 15(4), 177-192.

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