Abstract: The problem of multi-criterion optimal pricing model for perishable commodities is mainly studied.According to the principle of profit maximization and based on a discrete demand function，the multi-criterion optimal pricing model for the perishable products is established.Since the model involves some different stochastic distributions of several variables，which is difficult for the normal numerical methods to solve，the Quantum-behaved Particle Swarm Optimization（QPSO） algorithm with a penalty function is introduced to settle it.The experiment results show that by using QPSO method，the optimal prices and discount prices for different inventory levels can be derived quickly and effectively.

Key words: perishable commodities, optimal pricing, multi-criterion optimization, penalty function, Quantum-behaved Particle Swarm Optimization（QPSO）

摘要： 对易逝品的多目标定价问题进行了研究。从利润最大化角度建立易逝品多目标最优定价模型。模型中涉及复杂的需求函数，常规函数极值法不易获得问题解析解，因此引入量子粒子群算法，结合惩罚函数对模型进行演化求解。根据给出的算例分析表明，利用量子粒子群算法，可以快速有效地得到不同订货量下的最优定价与折扣价组合。

关键词: 易逝品, 最优定价, 多目标优化, 惩罚函数, 量子粒子群算法