TY - GEN A1 - Serth, Sebastian A1 - Podlesny, Nikolai A1 - Bornstein, Marvin A1 - Lindemann, Jan A1 - Latt, Johanna A1 - Selke, Jan A1 - Schlosser, Rainer A1 - Boissier, Martin A1 - Uflacker, Matthias T1 - An interactive platform to simulate dynamic pricing competition on online marketplaces T2 - 2017 IEEE 21st International Enterprise Distributed Object Computing Conference (EDOC) N2 - E-commerce marketplaces are highly dynamic with constant competition. While this competition is challenging for many merchants, it also provides plenty of opportunities, e.g., by allowing them to automatically adjust prices in order to react to changing market situations. For practitioners however, testing automated pricing strategies is time-consuming and potentially hazardously when done in production. Researchers, on the other side, struggle to study how pricing strategies interact under heavy competition. As a consequence, we built an open continuous time framework to simulate dynamic pricing competition called Price Wars. The microservice-based architecture provides a scalable platform for large competitions with dozens of merchants and a large random stream of consumers. Our platform stores each event in a distributed log. This allows to provide different performance measures enabling users to compare profit and revenue of various repricing strategies in real-time. For researchers, price trajectories are shown which ease evaluating mutual price reactions of competing strategies. Furthermore, merchants can access historical marketplace data and apply machine learning. By providing a set of customizable, artificial merchants, users can easily simulate both simple rule-based strategies as well as sophisticated data-driven strategies using demand learning to optimize their pricing strategies. Y1 - 2017 SN - 978-1-5090-3045-3 U6 - https://doi.org/10.1109/EDOC.2017.17 SN - 2325-6354 SP - 61 EP - 66 PB - Institute of Electrical and Electronics Engineers CY - New York 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 - 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 - 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 - GEN A1 - Schlosser, Rainer A1 - Kossmann, Jan A1 - Boissier, Martin T1 - Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies T2 - 2019 IEEE 35th International Conference on Data Engineering (ICDE) N2 - An efficient selection of indexes is indispensable for database performance. For large problem instances with hundreds of tables, existing approaches are not suitable: They either exhibit prohibitive runtimes or yield far from optimal index configurations by strongly limiting the set of index candidates or not handling index interaction explicitly. We introduce a novel recursive strategy that does not exclude index candidates in advance and effectively accounts for index interaction. Using large real-world workloads, we demonstrate the applicability of our approach. Further, we evaluate our solution end to end with a commercial database system using a reproducible setup. We show that our solutions are near-optimal for small index selection problems. For larger problems, our strategy outperforms state-of-the-art approaches in both scalability and solution quality. Y1 - 2019 SN - 978-1-5386-7474-1 U6 - https://doi.org/10.1109/ICDE.2019.00113 SN - 1084-4627 SP - 1238 EP - 1249 PB - IEEE CY - New York 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 - CHAP A1 - Schlosser, Rainer A1 - Boissier, Martin ED - Liberatore, Federico ED - Parlier, Greg H. ED - Demange, Marc T1 - Optimal price reaction strategies in the presence of active and passive competitors T2 - Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - ICORES N2 - Many markets are characterized by pricing competition. Typically, competitors are involved that adjust their prices in response to other competitors with different frequencies. We analyze stochastic dynamic pricing models under competition for the sale of durable goods. Given a competitor’s pricing strategy, we show how to derive optimal response strategies that take the anticipated competitor’s price adjustments into account. We study resulting price cycles and the associated expected long-term profits. We show that reaction frequencies have a major impact on a strategy’s performance. In order not to act predictable our model also allows to include randomized reaction times. Additionally, we study to which extent optimal response strategies of active competitors are affected by additional passive competitors that use constant prices. It turns out that optimized feedback strategies effectively avoid a decline in price. They help to gain profits, especially, when aggressive competitor s are involved. KW - Dynamic Pricing KW - Competition KW - Optimal Control KW - Response Strategies KW - Reaction Time KW - Price Cycles Y1 - 2017 SN - 978-989-758-218-9 U6 - https://doi.org/10.5220/0006118200470056 SP - 47 EP - 56 PB - SCITEPRESS - Science and Technology Publications, Lda. CY - Setúbal 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 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 - 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 -