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<p>Cross-border e-commerce platforms face intense global competition, requiring strategic optimization of pricing and product selection to maintain market relevance and profitability. The proposed research addresses the challenge of optimizing market competition strategies using heuristic algorithms tailored to the dynamics of international digital trade. Existing methods often rely on static rule-based models or oversimplified economic theories, which fail to adapt to real-time market fluctuations, competitor actions, and region-specific consumer behavior. These approaches typically lack robustness in handling multi-objective constraints such as profit maximization, inventory balance, and delivery efficiency. To overcome these limitations, this study introduces a <b>Dynamic Pricing and Product Selection Optimization using Genetic Algorithm (DP-PSO-GA)</b> framework. The model simulates competitive platform behavior, evolving optimal pricing and product assortments by encoding strategic parameters as chromosomes within a fitness-driven evolutionary process. The proposed method is applied to a multi-platform, multi-region e-commerce simulation environment. It dynamically adjusts pricing and selection in response to competitor moves, shifts in consumer demand, and logistical limitations. Results demonstrate that the DP-PSO-GA model significantly improves market share (20%), customer satisfaction (95.1%), and operational profitability compared to baseline heuristic and static optimization methods. It offers a scalable and adaptive solution for real-world cross-border e-commerce competition scenarios.</p>

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DP-PSO-GA: A heuristic optimization framework for dynamic pricing and product selection competition strategies in cross-border E-Commerce platforms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月8日

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  • Jie Zeng,
  • Xin Yan

摘要

Cross-border e-commerce platforms face intense global competition, requiring strategic optimization of pricing and product selection to maintain market relevance and profitability. The proposed research addresses the challenge of optimizing market competition strategies using heuristic algorithms tailored to the dynamics of international digital trade. Existing methods often rely on static rule-based models or oversimplified economic theories, which fail to adapt to real-time market fluctuations, competitor actions, and region-specific consumer behavior. These approaches typically lack robustness in handling multi-objective constraints such as profit maximization, inventory balance, and delivery efficiency. To overcome these limitations, this study introduces a Dynamic Pricing and Product Selection Optimization using Genetic Algorithm (DP-PSO-GA) framework. The model simulates competitive platform behavior, evolving optimal pricing and product assortments by encoding strategic parameters as chromosomes within a fitness-driven evolutionary process. The proposed method is applied to a multi-platform, multi-region e-commerce simulation environment. It dynamically adjusts pricing and selection in response to competitor moves, shifts in consumer demand, and logistical limitations. Results demonstrate that the DP-PSO-GA model significantly improves market share (20%), customer satisfaction (95.1%), and operational profitability compared to baseline heuristic and static optimization methods. It offers a scalable and adaptive solution for real-world cross-border e-commerce competition scenarios.