本文研究了基于Matlab的蔬菜类商品自动定价与补货决策优化问题。随着零售业竞争的加剧和消费者需求的多样化,蔬菜类商品的定价与补货决策成为超市经营者面临的重要挑战。本文分析了蔬菜类商品的经营特点,梳理了现有的定价策略和补货决...本文研究了基于Matlab的蔬菜类商品自动定价与补货决策优化问题。随着零售业竞争的加剧和消费者需求的多样化,蔬菜类商品的定价与补货决策成为超市经营者面临的重要挑战。本文分析了蔬菜类商品的经营特点,梳理了现有的定价策略和补货决策理论。在此基础上,利用Matlab软件,结合蔬菜销售数据,考虑了蔬菜的平均损耗率、预期销售量等因素下构建了自动定价模型,实现了基于成本加成策略的定价优化。研究过程中,运用了SPSSPRO的频数分析、皮尔逊相关系数分析等方法,揭示了蔬菜销售量的分布特征和品类间的关联性。此外,设计了补货决策优化模型,通过模拟退火算法、粒子群优化算法等智能优化方法,得出了最佳的自动定价策略及补货量。实证研究结果表明,本文提出的模型能够显著提高蔬菜类商品的销售效率和利润水平。This paper studies the automatic pricing and replenishment decision-making optimization of vegetable products based on Matlab. With the intensification of retail competition and the diversification of consumer demand, the pricing and replenishment decisions of vegetable products have become an important challenge for supermarket operators. This paper analyzes the operating characteristics of vegetable commodities and combs the existing pricing strategy and replenishment decision theory. On this basis, using Matlab software, combined with vegetable sales data, considering the average loss rate of vegetables, expected sales and other factors, an automatic pricing model was constructed to achieve pricing optimization based on cost-plus strategy. In the course of the study, SPSSPRO frequency analysis, Pearson correlation coefficient analysis and other methods were used to reveal the distribution characteristics of vegetable sales and the correlation between categories. In addition, the replenishment decision-making optimization model is designed, and the optimal automatic pricing strategy and replenishment quantity are obtained by intelligent optimization methods such as simulated annealing algorithm and particle swarm optimization algorithm. The empirical results show that the model proposed in this paper can significantly improve the sales efficiency and profit level of vegetable products.展开更多
为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷...为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷阱标记策略为粒子群提供动态速度增量,使其摆脱最优解的束缚。利用懒蚂蚁寻优策略多样化粒子速度,提升种群多样性。通过惯性认知策略在速度更新中引入历史位置,增加粒子的路径多样性和提升粒子的探索性能,使粒子更有效地避免陷入新的局部最优。理论证明了引入历史位置的粒子群算法的收敛性。仿真实验结果表明,所提算法不仅能有效解决粒子群已陷入局部最优和过早收敛的问题,且与其他算法相比,具有较快的收敛速度和较高的寻优精度。展开更多
文摘本文研究了基于Matlab的蔬菜类商品自动定价与补货决策优化问题。随着零售业竞争的加剧和消费者需求的多样化,蔬菜类商品的定价与补货决策成为超市经营者面临的重要挑战。本文分析了蔬菜类商品的经营特点,梳理了现有的定价策略和补货决策理论。在此基础上,利用Matlab软件,结合蔬菜销售数据,考虑了蔬菜的平均损耗率、预期销售量等因素下构建了自动定价模型,实现了基于成本加成策略的定价优化。研究过程中,运用了SPSSPRO的频数分析、皮尔逊相关系数分析等方法,揭示了蔬菜销售量的分布特征和品类间的关联性。此外,设计了补货决策优化模型,通过模拟退火算法、粒子群优化算法等智能优化方法,得出了最佳的自动定价策略及补货量。实证研究结果表明,本文提出的模型能够显著提高蔬菜类商品的销售效率和利润水平。This paper studies the automatic pricing and replenishment decision-making optimization of vegetable products based on Matlab. With the intensification of retail competition and the diversification of consumer demand, the pricing and replenishment decisions of vegetable products have become an important challenge for supermarket operators. This paper analyzes the operating characteristics of vegetable commodities and combs the existing pricing strategy and replenishment decision theory. On this basis, using Matlab software, combined with vegetable sales data, considering the average loss rate of vegetables, expected sales and other factors, an automatic pricing model was constructed to achieve pricing optimization based on cost-plus strategy. In the course of the study, SPSSPRO frequency analysis, Pearson correlation coefficient analysis and other methods were used to reveal the distribution characteristics of vegetable sales and the correlation between categories. In addition, the replenishment decision-making optimization model is designed, and the optimal automatic pricing strategy and replenishment quantity are obtained by intelligent optimization methods such as simulated annealing algorithm and particle swarm optimization algorithm. The empirical results show that the model proposed in this paper can significantly improve the sales efficiency and profit level of vegetable products.
文摘为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷阱标记策略为粒子群提供动态速度增量,使其摆脱最优解的束缚。利用懒蚂蚁寻优策略多样化粒子速度,提升种群多样性。通过惯性认知策略在速度更新中引入历史位置,增加粒子的路径多样性和提升粒子的探索性能,使粒子更有效地避免陷入新的局部最优。理论证明了引入历史位置的粒子群算法的收敛性。仿真实验结果表明,所提算法不仅能有效解决粒子群已陷入局部最优和过早收敛的问题,且与其他算法相比,具有较快的收敛速度和较高的寻优精度。
