@article{SchlosserWaltherBoissieretal.2019,
  author    = {Schlosser, Rainer and Walther, Carsten and Boissier, Martin and Uflacker, Matthias},
  title     = {Automated repricing and ordering strategies in competitive markets},
  series = {AI communications : AICOM ; the European journal on artificial intelligence},
  volume    = {32},
  journal   = {AI communications : AICOM ; the European journal on artificial intelligence},
  number    = {1},
  publisher = {IOS Press},
  address   = {Amsterdam},
  issn      = {0921-7126},
  doi       = {10.3233/AIC-180603},
  pages     = {15 -- 29},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}