粒子群算法在季节性商品最优定价中的应用

从最小化期望损失的角度建立了季节性商品的最优定价模型，并采用粒子群算法进行求解。结合具体算例，根据不同库存量、库存量和折扣价的不同组合，分别获得达到最小期望损失的最优定价，可以很好地解释模型所具有的经济意义。对仿真结果的分析表明：粒子群算法不仅能灵活、简便地获得多种情况下的最优定价，而且反映了最优定价在库存量和折扣价不同组合时的变化规律，从而为销售商确定最优价格提供建议。因此，应用粒子群算法求解季节性商品的最优定价具有一定的现实意义。

Optimal Pricing for Seasonal Products with Particle Swarm Optimization

For minimized seller's total expected loss and seasonal products,the paper proposes an optimal pricing model that is solved by Particle Swarm Optimization(PSO).With a numerical example,minimized expected loss and optimal price can be drawn under two situations including different stocks and different combination of stocks and discount price.The analysis indicates that it is applicable for PSO to solve optimal pricing according to different situations.Furthermore,result with PSO is rational and helpful for sellers to determine the optimal price.