Competitive Supply Chain Strategy Optimization Based on Game Model and NSGA-II Algorithm

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Abstract
In order to better understand the competitive dynamics between e-commerce platforms and traditional retail outlets, a Stackelberg game model was developed. Subsequently, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed to determine the Pareto solution set for this multi-objective optimization problem. The findings reveal that: a) The effect of consumer reference quality can lead enterprises to adjust their strategy levels downwards, potentially resulting in profit loss under certain conditions. b) When the influence of competitive intensity on market demand is minimal, a reduction in enterprise profits occurs in both centralized and cost-sharing decision-making frameworks, with more significant detriment observed in the cost-sharing mode; conversely, when the influence is substantial, enhancements in competitive intensity can significantly increase overall system profits. c) The model's validity was confirmed through the application of the NSGA-II.

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