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Automated repricing and ordering strategies in competitive markets

机译：竞争市场中的自动定价和订购策略

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摘要

Merchants on modern e-commerce platforms face a highly competitive environment. They compete against each other using automated dynamic pricing and ordering strategies. Successfully managing both inventory levels as well as offer prices is a challenging task as (i) demand is uncertain, (ii) competitors strategically interact, and (iii) optimized pricing and ordering decisions are mutually dependent. We show how to derive optimized data-driven pricing and ordering strategies which are based on demand learning techniques and efficient dynamic optimization models. We verify the superior performance of our self-adaptive strategies by comparing them to different rule-based as well as data-driven strategies in duopoly and oligopoly settings. Further, to study and to optimize joint dynamic ordering and pricing strategies on online marketplaces, we built an interactive simulation platform. To be both flexible and scalable, the platform has a microservice-based architecture and allows handling dozens of competing merchants and streams of consumers with configurable characteristics.
机译：现代电子商务平台上的商家面临竞争激烈​​的环境。他们使用自动动态定价和订购策略相互竞争。成功地管理库存水平和报价都是一项具有挑战性的任务，因为（i）需求不确定，（ii）竞争对手进行战略互动，并且（iii）优化的定价和订购决策相互依赖。我们展示了如何基于需求学习技术和有效的动态优化模型来推导优化的数据驱动定价和订购策略。通过将其与双寡头和寡头垄断环境下基于规则的以及以数据为驱动力的策略进行比较，我们验证了自适应策略的优越性能。此外，为了研究和优化在线市场上的联合动态订购和定价策略，我们构建了一个交互式仿真平台。为了既灵活又可扩展，该平台具有基于微服务的架构，并允许处理具有可配置特征的许多竞争商家和消费者流。

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