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

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  • Wenchong Chen
  • Hongwei Liu
  • Dan Xu

Abstract

Due to the fierce competition in the marketplace for perishable products, retailers have to use pricing strategies to attract customers. Traditional pricing strategies adjust products’ prices according to retailers’ 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 products’ 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.

Suggested Citation

  • Wenchong Chen & Hongwei Liu & Dan Xu, 2018. "Dynamic Pricing Strategies for Perishable Product in a Competitive Multi-Agent Retailers Market," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(2), pages 1-12.
  • Handle: RePEc:jas:jasssj:2017-4-3
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    References listed on IDEAS

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    1. Ilker Arslan & Eugenio Caverzasi & Mauro Gallegati & Alper Duman, 2016. "Long Term Impacts of Bank Behavior on Financial Stability. an Agent Based Modeling Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(1), pages 1-11.
    2. Yousefi, Shaghayegh & Moghaddam, Mohsen Parsa & Majd, Vahid Johari, 2011. "Optimal real time pricing in an agent-based retail market using a comprehensive demand response model," Energy, Elsevier, vol. 36(9), pages 5716-5727.
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    Cited by:

    1. Conor B. Hamill & Raad Khraishi & Simona Gherghel & Jerrard Lawrence & Salvatore Mercuri & Ramin Okhrati & Greig A. Cowan, 2023. "Agent-based Modelling of Credit Card Promotions," Papers 2311.01901, arXiv.org, revised Nov 2023.
    2. Wasfi Alrawabdeh, 2022. "Seasonal balancing of revenue and demand in hotel industry: the case of Dubai City," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(1), pages 36-49, February.

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