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Due to the fierce competition in the marketplace for perishable products, retailers have to use pricing strategies to attract customers. Traditional pricing strategies adjust productsâ€™ prices according to retailersâ€™ current situations (e.g. Cost-plus pricing strategy, Value-based pricing strategy and Inventory-sensitive pricing strategy). However, many retailers lack the perception for customer preferences and an understanding of the competitive environment. This paper explores a price Q-learning mechanism for perishable products that considers uncertain demand and customer preferences in a competitive multi-agent retailer market (a model-free environment). In the proposed simulation model, agents imitate the behavior of consumers and retailers. Four potential influencing factors (competition, customer preferences, uncertain demand, perishable characteristics) are constructed in the pricing decisions. All retailer agents adjust their productsâ€™ prices over a finite sales horizon to maximize expected revenues. A retailer agent adjusts its price according to the Q-learning mechanism, while others adapt traditional pricing strategies. Shortage is allowed while backlog is not. The simulation results show that the dynamic pricing strategy via the Q-learning mechanism can be used for pricing perishable products in a competitive environment, as it can produce more revenue for retailers. Further, the paper investigates how an optimal pricing strategy is influenced by customer preferences, customer demand, retailer pricing parameters and the learning parameters of Q-learning. Based on our results, we provide pricing implications for retailers pursuing higher revenues.

Dynamic Pricing Strategies for Perishable Product in a Competitive Multi-Agent Retailers Market