Optimization of Dynamic Pricing in E-Commerce Platform with Demand Side Management using Fuzzy Logic System

In e-commerce platforms, dynamic pricing has drawn a lot of attention as a potent tactic to boost sales and improve consumer happiness. But monitoring and comprehending client demand is crucial to the success of dynamic pricing. This study suggests an optimization framework for demand-side management that incorporates fuzzy logic into dynamic pricing in e-commerce platforms. To represent and capture the uncertainty and imprecision present in client demand, the suggested framework makes use of fuzzy logic. Fuzzy logic makes it possible to represent and work with linguistic variables, which makes decision-making more adaptable and natural. The approach takes into account the influence of customer behavior and preferences on pricing decisions by incorporating demand-side management. There are two primary steps in the optimization process. First, a fuzzy demand model is created to estimate consumer demand based on a variety of inputs, including pricing, product qualities, and customer traits. This model offers a quantitative knowledge of consumer behavior under various pricing conditions. Second, a pricing plan that maximizes platform profit while accounting for customer happiness and demand changes is determined using an optimization algorithm. By providing personalized pricing based on consumer preferences, the optimized pricing strategy increases revenue while also enhancing customer happiness. Demand-side management and fuzzy logic are combined to improve decision-making and help e-commerce platforms adjust to shifting consumer preferences and market conditions.