TY - JOUR A1 - Hagedorn, Christopher A1 - Huegle, Johannes A1 - Schlosser, Rainer T1 - Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning JF - Journal of intelligent manufacturing N2 - 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. KW - Causal structure learning KW - Log data KW - Causal inference KW - Manufacturing KW - industry Y1 - 2022 U6 - https://doi.org/10.1007/s10845-022-01952-x SN - 0956-5515 SN - 1572-8145 VL - 33 IS - 7 SP - 2027 EP - 2043 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Schlosser, Rainer T1 - Stochastic dynamic pricing and advertising in isoelastic oligopoly models JF - European Journal of Operational Research N2 - In this paper, we analyze stochastic dynamic pricing and advertising differential games in special oligopoly markets with constant price and advertising elasticity. We consider the sale of perishable as well as durable goods and include adoption effects in the demand. Based on a unique stochastic feedback Nash equilibrium, we derive closed-form solution formulas of the value functions and the optimal feedback policies of all competing firms. Efficient simulation techniques are used to evaluate optimally controlled sales processes over time. This way, the evolution of optimal controls as well as the firms’ profit distributions are analyzed. Moreover, we are able to compare feedback solutions of the stochastic model with its deterministic counterpart. We show that the market power of the competing firms is exactly the same as in the deterministic version of the model. Further, we discover two fundamental effects that determine the relation between both models. First, the volatility in demand results in a decline of expected profits compared to the deterministic model. Second, we find that saturation effects in demand have an opposite character. We show that the second effect can be strong enough to either exactly balance or even overcompensate the first one. As a result we are able to identify cases in which feedback solutions of the deterministic model provide useful approximations of solutions of the stochastic model. KW - Pricing KW - Advertising KW - Stochastic differential games KW - Oligopoly competition KW - Adoption effects Y1 - 2017 U6 - https://doi.org/10.1016/j.ejor.2016.11.021 SN - 0377-2217 SN - 1872-6860 VL - 259 SP - 1144 EP - 1155 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kossmann, Jan A1 - Schlosser, Rainer T1 - Self-driving database systems BT - a conceptual approach JF - Distributed and parallel databases N2 - Challenges for self-driving database systems, which tune their physical design and configuration autonomously, are manifold: Such systems have to anticipate future workloads, find robust configurations efficiently, and incorporate knowledge gained by previous actions into later decisions. We present a component-based framework for self-driving database systems that enables database integration and development of self-managing functionality with low overhead by relying on separation of concerns. By keeping the components of the framework reusable and exchangeable, experiments are simplified, which promotes further research in that area. Moreover, to optimize multiple mutually dependent features, e.g., index selection and compression configurations, we propose a linear programming (LP) based algorithm to derive an efficient tuning order automatically. Afterwards, we demonstrate the applicability and scalability of our approach with reproducible examples. KW - database systems KW - self-driving KW - recursive tuning KW - workload prediction KW - robustness Y1 - 2020 U6 - https://doi.org/10.1007/s10619-020-07288-w SN - 0926-8782 SN - 1573-7578 VL - 38 IS - 4 SP - 795 EP - 817 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Schlosser, Rainer T1 - Scalable relaxation techniques to solve stochastic dynamic multi-product pricing problems with substitution effects JF - Journal of revenue and pricing management N2 - In many businesses, firms are selling different types of products, which share mutual substitution effects in demand. To compute effective pricing strategies is challenging as the sales probabilities of each of a firm's products can also be affected by the prices of potential substitutes. In this paper, we analyze stochastic dynamic multi-product pricing models for the sale of perishable goods. To circumvent the limitations of time-consuming optimal solutions for highly complex models, we propose different relaxation techniques, which allow to reduce the size of critical model components, such as the state space, the action space, or the set of potential sales events. Our heuristics are able to decrease the size of those components by forming corresponding clusters and using subsets of representative elements. Using numerical examples, we verify that our heuristics make it possible to dramatically reduce the computation time while still obtaining close-to-optimal expected profits. Further, we show that our heuristics are (i) flexible, (ii) scalable, and (iii) can be arbitrarily combined in a mutually supportive way. KW - multi-product pricing KW - substitution effects KW - data-driven demand KW - dynamic KW - programming KW - heuristics Y1 - 2020 U6 - https://doi.org/10.1057/s41272-020-00249-z SN - 1476-6930 SN - 1477-657X VL - 20 IS - 1 SP - 54 EP - 65 PB - Palgrave Macmillan CY - Basingstoke ER - TY - JOUR A1 - Schlosser, Rainer T1 - Risk-sensitive control of Markov decision processes BT - a moment-based approach with target distributions JF - Computers & operations research : and their applications to problems of world concern N2 - In many revenue management applications risk-averse decision-making is crucial. In dynamic settings, however, it is challenging to find the right balance between maximizing expected rewards and minimizing various kinds of risk. In existing approaches utility functions, chance constraints, or (conditional) value at risk considerations are used to influence the distribution of rewards in a preferred way. Nevertheless, common techniques are not flexible enough and typically numerically complex. In our model, we exploit the fact that a distribution is characterized by its mean and higher moments. We present a multi-valued dynamic programming heuristic to compute risk-sensitive feedback policies that are able to directly control the moments of future rewards. Our approach is based on recursive formulations of higher moments and does not require an extension of the state space. Finally, we propose a self-tuning algorithm, which allows to identify feedback policies that approximate predetermined (risk-sensitive) target distributions. We illustrate the effectiveness and the flexibility of our approach for different dynamic pricing scenarios. (C) 2020 Elsevier Ltd. All rights reserved. KW - risk aversion KW - Markov decision process KW - dynamic programming KW - dynamic KW - pricing KW - heuristics Y1 - 2020 U6 - https://doi.org/10.1016/j.cor.2020.104997 SN - 0305-0548 VL - 123 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Richly, Keven A1 - Brauer, Janos A1 - Schlosser, Rainer T1 - Predicting location probabilities of drivers to improved dispatch decisions of transportation network companies based on trajectory data JF - Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - ICORES N2 - The demand for peer-to-peer ridesharing services increased over the last years rapidly. To cost-efficiently dispatch orders and communicate accurate pick-up times is challenging as the current location of each available driver is not exactly known since observed locations can be outdated for several seconds. The developed trajectory visualization tool enables transportation network companies to analyze dispatch processes and determine the causes of unexpected delays. As dispatching algorithms are based on the accuracy of arrival time predictions, we account for factors like noise, sample rate, technical and economic limitations as well as the duration of the entire process as they have an impact on the accuracy of spatio-temporal data. To improve dispatching strategies, we propose a prediction approach that provides a probability distribution for a driver’s future locations based on patterns observed in past trajectories. We demonstrate the capabilities of our prediction results to ( i) avoid critical delays, (ii) to estimate waiting times with higher confidence, and (iii) to enable risk considerations in dispatching strategies. KW - trajectory data KW - location prediction algorithm KW - Peer-to-Peer ridesharing KW - transport network companies KW - risk-aware dispatching Y1 - 2020 PB - Springer CY - Berlin ER - TY - JOUR A1 - Seiffert, Martin A1 - Holstein, Flavio A1 - Schlosser, Rainer A1 - Schiller, Jochen T1 - Next generation cooperative wearables BT - generalized activity assessment computed fully distributed with in a wireless body area network JF - IEEE access : practical research, open solutions N2 - Currently available wearables are usually based on a single sensor node with integrated capabilities for classifying different activities. The next generation of cooperative wearables could be able to identify not only activities, but also to evaluate them qualitatively using the data of several sensor nodes attached to the body, to provide detailed feedback for the improvement of the execution. Especially within the application domains of sports and health-care, such immediate feedback to the execution of body movements is crucial for (re-) learning and improving motor skills. To enable such systems for a broad range of activities, generalized approaches for human motion assessment within sensor networks are required. In this paper, we present a generalized trainable activity assessment chain (AAC) for the online assessment of periodic human activity within a wireless body area network. AAC evaluates the execution of separate movements of a prior trained activity on a fine-grained quality scale. We connect qualitative assessment with human knowledge by projecting the AAC on the hierarchical decomposition of motion performed by the human body as well as establishing the assessment on a kinematic evaluation of biomechanically distinct motion fragments. We evaluate AAC in a real-world setting and show that AAC successfully delimits the movements of correctly performed activity from faulty executions and provides detailed reasons for the activity assessment. KW - Body sensor networks KW - distributed computing KW - motion analysis KW - physical activity assessment KW - biomechanics KW - multilevel systems Y1 - 2017 U6 - https://doi.org/10.1109/ACCESS.2017.2749005 SN - 2169-3536 VL - 5 SP - 16793 EP - 16807 PB - Institute of Electrical and Electronics Engineers CY - Piscataway ER - TY - JOUR A1 - Kossmann, Jan A1 - Halfpap, Stefan A1 - Jankrift, Marcel A1 - Schlosser, Rainer T1 - Magic mirror in my hand, which is the best in the land? BT - an experimental evaluation of index selection algorithms JF - Proceedings of the VLDB Endowment N2 - Indexes are essential for the efficient processing of database workloads. Proposed solutions for the relevant and challenging index selection problem range from metadata-based simple heuristics, over sophisticated multi-step algorithms, to approaches that yield optimal results. The main challenges are (i) to accurately determine the effect of an index on the workload cost while considering the interaction of indexes and (ii) a large number of possible combinations resulting from workloads containing many queries and massive schemata with possibly thousands of attributes.
In this work, we describe and analyze eight index selection algorithms that are based on different concepts and compare them along different dimensions, such as solution quality, runtime, multi-column support, solution granularity, and complexity. In particular, we analyze the solutions of the algorithms for the challenging analytical Join Order, TPC-H, and TPC-DS benchmarks. Afterward, we assess strengths and weaknesses, infer insights for index selection in general and each approach individually, before we give recommendations on when to use which approach. Y1 - 2020 U6 - https://doi.org/10.14778/3407790.3407832 SN - 2150-8097 VL - 13 IS - 11 SP - 2382 EP - 2395 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Schlosser, Rainer T1 - Joint stochastic dynamic pricing and advertising with time-dependent demand JF - Geophysical journal international N2 - This paper examines the sale of a finite number of items in a class of stochastic dynamic pricing and advertising models with time-dependent demand elasticities. We prove structural properties of the optimal expected profits with respect to time, inventory level, price impact, advertising impact and different model parameters, such as discount rate, marginal unit costs, and holding costs. We find that the value of an additional item (opportunity costs) is decreasing in the unit costs, the discount rate, the holding cost rate and the number of items left to sell. We also derive structural properties of optimal joint pricing and advertising strategies. This way, we obtain general qualitative insights in the complex interplay and the mutual dependence of optimal pricing and advertising decisions. Among other properties, we show that a higher advertising impact leads to higher optimal prices and lower advertising rates, which in turn implies a lower speed of sale. The results obtained help practitioners to respond to changes in market conditions by adjusting price and advertising accordingly. Our results allow speeding up numerical computations of decisions as the set of possible actions can be reduced significantly. Our analysis implies general results for pure pricing as well as pure advertising models with time-dependent demand elasticities. (C) 2016 Elsevier B.V. All rights reserved. KW - Dynamic pricing and advertising KW - Optimal stochastic control KW - Time-dependent demand elasticities KW - Structural properties Y1 - 2016 U6 - https://doi.org/10.1016/j.jedc.2016.10.006 SN - 0165-1889 SN - 1879-1743 VL - 73 SP - 439 EP - 452 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schlosser, Rainer T1 - Heuristic mean-variance optimization in Markov decision processes using state-dependent risk aversion JF - IMA journal of management mathematics / Institute of Mathematics and Its Applications N2 - 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. KW - risk aversion KW - mean-variance optimization KW - Markov decision process; KW - dynamic programming KW - dynamic pricing KW - heuristics Y1 - 2021 U6 - https://doi.org/10.1093/imaman/dpab009 SN - 1471-678X SN - 1471-6798 VL - 33 IS - 2 SP - 181 EP - 199 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Schlosser, Rainer A1 - Richly, Keven T1 - Dynamic pricing under competition with data-driven price anticipations and endogenous reference price effects JF - Journal of revenue and pricing management N2 - Online markets have become highly dynamic and competitive. Many sellers use automated data-driven strategies to estimate demand and to update prices frequently. Further, notification services offered by marketplaces allow to continuously track markets and to react to competitors’ price adjustments instantaneously. To derive successful automated repricing strategies is challenging as competitors’ strategies are typically not known. In this paper, we analyze automated repricing strategies with data-driven price anticipations under duopoly competition. In addition, we account for reference price effects in demand, which are affected by the price adjustments of both competitors. We show how to derive optimized self-adaptive pricing strategies that anticipate price reactions of the competitor and take the evolution of the reference price into account. We verify that the results of our adaptive learning strategy tend to optimal solutions, which can be derived for scenarios with full information. Finally, we analyze the case in which our learning strategy is played against itself. We find that our self-adaptive strategies can be used to approximate equilibria in mixed strategies. KW - Dynamic pricing competition KW - Data-driven price anticipation KW - e-Commerce KW - Dynamic programming KW - Response strategies Y1 - 2019 U6 - https://doi.org/10.1057/s41272-019-00206-5 SN - 1476-6930 SN - 1477-657X VL - 18 IS - 6 SP - 451 EP - 464 PB - Palgrave Macmillan CY - Basingstoke ER - TY - JOUR A1 - Kastius, Alexander A1 - Schlosser, Rainer T1 - Dynamic pricing under competition using reinforcement learning JF - Journal of revenue and pricing management N2 - 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. KW - Dynamic pricing KW - Competition KW - Reinforcement learning KW - E-commerce KW - Price collusion Y1 - 2021 U6 - https://doi.org/10.1057/s41272-021-00285-3 SN - 1476-6930 SN - 1477-657X VL - 21 IS - 1 SP - 50 EP - 63 PB - Springer Nature Switzerland AG CY - Cham ER - TY - JOUR A1 - Schlosser, Rainer T1 - Dynamic pricing and advertising of perishable products with inventory holding costs JF - Journal of economic dynamics & control N2 - We examine a special class of dynamic pricing and advertising models for the sale of perishable goods, including marginal unit costs and inventory holding costs. The time horizon is assumed to be finite and we allow several model parameters to be dependent on time. For the stochastic version of the model, we derive closed-form expressions of the value function as well as of the optimal pricing and advertising policy in feedback form. Moreover, we show that for small unit shares, the model converges to a deterministic version of the problem, whose explicit solution is characterized by an overage and an underage case. We quantify the close relationship between the open-loop solution of the deterministic model and the expected evolution of optimally controlled stochastic sales processes. For both models, we derive sensitivity results. We find that in the case of positive holding costs, on average, optimal prices increase in time and advertising rates decrease. Furthermore, we analytically verify the excellent quality of optimal feedback policies of deterministic models applied in stochastic models. (C) 2015 Elsevier B.V. All rights reserved. KW - Dynamic pricing and advertising KW - Optimal stochastic and deterministic KW - control KW - Inventory holding costs KW - Finite horizon KW - Feedback heuristics Y1 - 2015 U6 - https://doi.org/10.1016/j.jedc.2015.05.017 SN - 0165-1889 SN - 1879-1743 VL - 57 SP - 163 EP - 181 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schlosser, Rainer A1 - Boissier, Martin T1 - Dealing with the dimensionality curse in dynamic pricing competition BT - Using frequent repricing to compensate imperfect market anticipations JF - Computers & Operations Research N2 - 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%. KW - Dynamic pricing KW - Oligopoly competition KW - Dynamic programming KW - Data-driven strategies KW - E-commerce Y1 - 2018 U6 - https://doi.org/10.1016/j.cor.2018.07.011 SN - 0305-0548 SN - 1873-765X VL - 100 SP - 26 EP - 42 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Schlosser, Rainer A1 - Chenavaz, Régis Y. A1 - Dimitrov, Stanko T1 - Circular economy BT - joint dynamic pricing and recycling investments JF - International journal of production economics N2 - 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. KW - Dynamic pricing KW - Recycling investments KW - Optimal control KW - General demand function KW - Circular economy Y1 - 2021 U6 - https://doi.org/10.1016/j.ijpe.2021.108117 SN - 0925-5273 SN - 1873-7579 VL - 236 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Richly, Keven A1 - Schlosser, Rainer A1 - Boissier, Martin T1 - Budget-conscious fine-grained configuration optimization for spatio-temporal applications JF - Proceedings of the VLDB Endowment N2 - 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. KW - General Earth and Planetary Sciences KW - Water Science and Technology KW - Geography, Planning and Development Y1 - 2022 U6 - https://doi.org/10.14778/3565838.3565858 SN - 2150-8097 VL - 15 IS - 13 SP - 4079 EP - 4092 PB - Association for Computing Machinery (ACM) CY - [New York] ER - TY - JOUR A1 - Schlosser, Rainer A1 - Walther, Carsten A1 - Boissier, Martin A1 - Uflacker, Matthias T1 - Automated repricing and ordering strategies in competitive markets JF - AI communications : AICOM ; the European journal on artificial intelligence N2 - 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. KW - Dynamic pricing KW - inventory management KW - demand learning KW - oligopoly competition KW - e-commerce Y1 - 2019 U6 - https://doi.org/10.3233/AIC-180603 SN - 0921-7126 SN - 1875-8452 VL - 32 IS - 1 SP - 15 EP - 29 PB - IOS Press CY - Amsterdam ER -