@article{RichlySchlosserBoissier2022,
  author    = {Richly, Keven and Schlosser, Rainer and Boissier, Martin},
  title     = {Budget-conscious fine-grained configuration optimization for spatio-temporal applications},
  series = {Proceedings of the VLDB Endowment},
  volume    = {15},
  journal   = {Proceedings of the VLDB Endowment},
  number    = {13},
  publisher = {Association for Computing Machinery (ACM)},
  address   = {[New York]},
  issn      = {2150-8097},
  doi       = {10.14778/3565838.3565858},
  pages     = {4079 -- 4092},
  year      = {2022},
  abstract  = {Based on the performance requirements of modern spatio-temporal data mining applications, in-memory database systems are often used to store and process the data. To efficiently utilize the scarce DRAM capacities, modern database systems support various tuning possibilities to reduce the memory footprint (e.g., data compression) or increase performance (e.g., additional indexes). However, the selection of cost and performance balancing configurations is challenging due to the vast number of possible setups consisting of mutually dependent individual decisions. In this paper, we introduce a novel approach to jointly optimize the compression, sorting, indexing, and tiering configuration for spatio-temporal workloads. Further, we consider horizontal data partitioning, which enables the independent application of different tuning options on a fine-grained level. We propose different linear programming (LP) models addressing cost dependencies at different levels of accuracy to compute optimized tuning configurations for a given workload and memory budgets. To yield maintainable and robust configurations, we extend our LP-based approach to incorporate reconfiguration costs as well as a worst-case optimization for potential workload scenarios. Further, we demonstrate on a real-world dataset that our models allow to significantly reduce the memory footprint with equal performance or increase the performance with equal memory size compared to existing tuning heuristics.},
  language  = {en}
}
@article{HagedornHuegleSchlosser2022,
  author    = {Hagedorn, Christopher and Huegle, Johannes and Schlosser, Rainer},
  title     = {Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning},
  series = {Journal of intelligent manufacturing},
  volume    = {33},
  journal   = {Journal of intelligent manufacturing},
  number    = {7},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0956-5515},
  doi       = {10.1007/s10845-022-01952-x},
  pages     = {2027 -- 2043},
  year      = {2022},
  abstract  = {In discrete manufacturing, the knowledge about causal relationships makes it possible to avoid unforeseen production downtimes by identifying their root causes. Learning causal structures from real-world settings remains challenging due to high-dimensional data, a mix of discrete and continuous variables, and requirements for preprocessing log data under the causal perspective. In our work, we address these challenges proposing a process for causal reasoning based on raw machine log data from production monitoring. Within this process, we define a set of transformation rules to extract independent and identically distributed observations. Further, we incorporate a variable selection step to handle high-dimensionality and a discretization step to include continuous variables. We enrich a commonly used causal structure learning algorithm with domain-related orientation rules, which provides a basis for causal reasoning. We demonstrate the process on a real-world dataset from a globally operating precision mechanical engineering company. The dataset contains over 40 million log data entries from production monitoring of a single machine. In this context, we determine the causal structures embedded in operational processes. Further, we examine causal effects to support machine operators in avoiding unforeseen production stops, i.e., by detaining machine operators from drawing false conclusions on impacting factors of unforeseen production stops based on experience.},
  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{Schlosser2022,
  author    = {Schlosser, Rainer},
  title     = {Heuristic mean-variance optimization in Markov decision processes using state-dependent risk aversion},
  series = {IMA journal of management mathematics / Institute of Mathematics and Its Applications},
  volume    = {33},
  journal   = {IMA journal of management mathematics / Institute of Mathematics and Its Applications},
  number    = {2},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {1471-678X},
  doi       = {10.1093/imaman/dpab009},
  pages     = {181 -- 199},
  year      = {2022},
  abstract  = {In dynamic decision problems, it is challenging to find the right balance between maximizing expected rewards and minimizing risks. In this paper, we consider NP-hard mean-variance (MV) optimization problems in Markov decision processes with a finite time horizon. We present a heuristic approach to solve MV problems, which is based on state-dependent risk aversion and efficient dynamic programming techniques. Our approach can also be applied to mean-semivariance (MSV) problems, which particularly focus on the downside risk. We demonstrate the applicability and the effectiveness of our heuristic for dynamic pricing applications. Using reproducible examples, we show that our approach outperforms existing state-of-the-art benchmark models for MV and MSV problems while also providing competitive runtimes. Further, compared to models based on constant risk levels, we find that state-dependent risk aversion allows to more effectively intervene in case sales processes deviate from their planned paths. Our concepts are domain independent, easy to implement and of low computational complexity.},
  language  = {en}
}