关键词: 易逝品, 马氏决策过程, Q学习算法, 订货策略, 定价策略

Abstract: To solve the jointed decisions problem of ordering,pricing and old products disposing faced for selling perishable products with a multi-period shelf life over an infinite horizon effectively,a model with Markov decision theory was established and the optimal policy was computed by using Q-learning algorithm.The optimal policy indicated the action of all states which could maximize the long-run discounted expected profit from current period.Through interacting with the environment and obtaining the feedback continuously,the iterate formula of algorithm renewed the optimal policy constantly.The stationary optimal policy would be computed after sufficient learning under situation of state and action space were finite and discrete,while the state transition probability and expected profit were not necessarily be known.The research showed that the different parameters had different and significant impact on the characteristic of each decision,and the conclusion provided some support and thought for researches of heuristic strategy.

Key words: perishable product, Markov decision process, Q-learning algorithm, ordering decisions, pricing decisions