@article{SchlosserChenavazDimitrov2021,
  author    = {Schlosser, Rainer and Chenavaz, R{\´e}gis Y. and Dimitrov, Stanko},
  title     = {Circular economy},
  series = {International journal of production economics},
  volume    = {236},
  journal   = {International journal of production economics},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0925-5273},
  doi       = {10.1016/j.ijpe.2021.108117},
  pages     = {13},
  year      = {2021},
  abstract  = {In a circular economy, the use of recycled resources in production is a key performance indicator for management. Yet, academic studies are still unable to inform managers on appropriate recycling and pricing policies. We develop an optimal control model integrating a firm's recycling rate, which can use both virgin and recycled resources in the production process. Our model accounts for recycling influence both at the supply- and demandsides. The positive effect of a firm's use of recycled resources diminishes over time but may increase through investments. Using general formulations for demand and cost, we analytically examine joint dynamic pricing and recycling investment policies in order to determine their optimal interplay over time. We provide numerical experiments to assess the existence of a steady-state and to calculate sensitivity analyses with respect to various model parameters. The analysis shows how to dynamically adapt jointly optimized controls to reach sustainability in the production process. Our results pave the way to sounder sustainable practices for firms operating within a circular economy.},
  language  = {en}
}
@article{KastiusSchlosser2022,
  author    = {Kastius, Alexander and Schlosser, Rainer},
  title     = {Dynamic pricing under competition using reinforcement learning},
  series = {Journal of revenue and pricing management},
  volume    = {21},
  journal   = {Journal of revenue and pricing management},
  number    = {1},
  publisher = {Springer Nature Switzerland AG},
  address   = {Cham},
  issn      = {1476-6930},
  doi       = {10.1057/s41272-021-00285-3},
  pages     = {50 -- 63},
  year      = {2022},
  abstract  = {Dynamic pricing is considered a possibility to gain an advantage over competitors in modern online markets. The past advancements in Reinforcement Learning (RL) provided more capable algorithms that can be used to solve pricing problems. In this paper, we study the performance of Deep Q-Networks (DQN) and Soft Actor Critic (SAC) in different market models. We consider tractable duopoly settings, where optimal solutions derived by dynamic programming techniques can be used for verification, as well as oligopoly settings, which are usually intractable due to the curse of dimensionality. We find that both algorithms provide reasonable results, while SAC performs better than DQN. Moreover, we show that under certain conditions, RL algorithms can be forced into collusion by their competitors without direct communication.},
  language  = {en}
}
@article{SchlosserBoissier2018,
  author    = {Schlosser, Rainer and Boissier, Martin},
  title     = {Dealing with the dimensionality curse in dynamic pricing competition},
  series = {Computers \& Operations Research},
  volume    = {100},
  journal   = {Computers \& Operations Research},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0305-0548},
  doi       = {10.1016/j.cor.2018.07.011},
  pages     = {26 -- 42},
  year      = {2018},
  abstract  = {Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustments can be time-consuming. We analyze stochastic dynamic pricing models under oligopoly competition for the sale of perishable goods. To circumvent the curse of dimensionality, we propose a heuristic approach to efficiently compute price adjustments. To demonstrate our strategy's applicability even if the number of competitors is large and their strategies are unknown, we consider different competitive settings in which competitors frequently and strategically adjust their prices. For all settings, we verify that our heuristic strategy yields promising results. We compare the performance of our heuristic against upper bounds, which are obtained by optimal strategies that take advantage of perfect price anticipations. We find that price adjustment frequencies can have a larger impact on expected profits than price anticipations. Finally, our approach has been applied on Amazon for the sale of used books. We have used a seller's historical market data to calibrate our model. Sales results show that our data-driven strategy outperforms the rule-based strategy of an experienced seller by a profit increase of more than 20\%.},
  language  = {en}
}
@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}
}