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  3.  > Dr. Rainer Schlosser

Dr. Rainer Schlosser

Senior Researcher (Group Leader "Data-Driven Decision Support")

Phone:+49 (331) 5509-1309
Fax:+49 (331) 5509-579
Email: rainer.schlosser(at)hpi.de
Room: V-2.05
  ResearchGate, Google Scholar, DBLP, Semantic Scholar, ORCID
  

Research

Data-Driven Decision Support & Revenue Management

The need for automated decision-making is steadily increasing. The goal is to derive methods and data-driven models for automated decision support for practical applications in uncertain and changing environments. Solving problems in practical applications requires bringing together data management, analytics, optimization, and computer science.

Our research group Data-Driven Decision Support focuses on automated decision-making in the areas of Operations Management and beyond using quantitative methods of operations research (cf. modelling, simulation, and optimization) and data science (cf. AI/ML). Our research has been published in over 80 peer-reviewed publications including renowned Journals (EJOR, JEDC, IJPE, IJPR, COR, DGAA, ITOR, JRPM, JIMS, JCLP, BISE), distinguished data science conferences (ICML, KDD, IJCAI, RECSYS, SDM, ICDM, PMLR, ECML, ICDE), and leading computer science venues (VLDB, EDBT, DAPD, CIKM, EDOC, SSDBM). Rainer serves as a reviewer for over 70 Journals in the areas of operations management, AI/ML, and information systems.

Keywords: Data-Driven Decision Support, Business Analytics, Risk & Robustness, Markov Decision Processes, Reinforcement Learning, Revenue Management & Pricing, Inventory Management, Resource Allocation, Recommerce, Sustainability, Circular Economy

Current Projects

Selected Talks & Presentations

  • "Reinforcement Learning for Dynamic Pricing Strategies in Competitive Markets with Strategic Customers", OR 2024, Sep 2024, Munich, Germany
  • "Dynamic Pricing in Circular Markets under Competition", INFORMS Revenue Management & Pricing Conference, July 2024, UCLA, USA
  • "AI meets Sustainability: Using Digital Twins to Leverage Reinforcement Learning – Based Dynamic Pricing in Circular Markets under Competition", EURO 2024, July 2024, Copenhagen, Denmark
  • "Mean-Variance Optimization for Finite Horizon Markov Decision Processes", Research in Operations Management Seminar, Nov 2023, UZH, Switzerland (invited talk)
  • "Dynamic Pricing under Competition: Challenges & Opportunities", Keynote presentation at ICORES 2023, Feb 2023, Lisbon, Portugal (Invited Talk)
  • "A Conceptual Framework for Studying Self-Learning Agents in Recommerce Markets", at OR 2022, Sep 2022, KIT, Germany (Invited Talk)
  • "Risk-Averse Revenue Management using Mean-Variance and Mean-Semivariance Optimization", INFORMS Revenue Management & Pricing Conference, June 2021, Johns Hopkins Carey Business School, USA
  • "Dynamic Pricing on Online Marketplaces – Theory and Applications", 17th conference of the GOR working group „Pricing & Revenue Management", Feb 2020, Munich, Germany (Invited Talk)
  • "Dynamic Pricing Competition in E-Commerce with Data-Driven Price Anticipations", ICIAM 2019, July 2019, Valencia, Spain (Invited Talk)
  • "Risk-Sensitive Control of Markov Decision Processes in Revenue Management: A Moment-Based Approach with Target Distributions", INFORMS Revenue Management & Pricing Conference, June 2019, Stanford Graduate School of Business, CA, USA
  • "Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies" and "Workload-Driven Fragment Allocation for Partially Replicated Databases Using Linear Programming", ICDE 2019, April 2019, Macao SAR, China
  • "Dynamic Pricing under Competition on Online Marketplaces: A Data-Driven Approach", KDD 2018, Aug 2018, London, UK
  • "How To Survive Dynamic Pricing Competition in E-commerce", RecSys 2016, Sep 2016, Massachusetts Institute of Technology (MIT) and the IBM Research campuses in Boston, MA, USA

  • "Dynamic Pricing with Time-Dependent Elasticities", INFORMS Revenue Management & Pricing Conference, June 2015, Columbia University, New York, USA

Education

  • Ph.D. in Operations Research (summa cum laude), Humboldt-University of Berlin, Germany (05/2014), Topic: Dynamic Pricing and Advertising Models
  • Master's degree (Dipl.-Math.) in Mathematics (very good), Humboldt-University of Berlin, Institute of Applied Mathematics, Germany (10/2010), Focus: Optimization, Stochastic Models, Numerical Analysis
  • Master's degree (Dipl.-Kfm.) in Business Administration (very good), Humboldt-University of Berlin, School of Business and Economics, Germany (02/2010), Focus: Operations Research, Econometrics, Risk Management

Grants, Awards & Research Stays

  • Planned Research Stay: University of Waterloo, Waterloo, Canada (2024)
  • Co-Chair: ICORES Conference (since March 2024)
  • Grant Application: ORA 8 Grant Application (with UK, Canada & France):  “A Unified Approach to Green Innovation by Fossil Fuel Companies”, Nov 2023 (role: Co-Director)
  • Research Stay: University of Zurich, Zurich, Switzerland (Nov 2023)
  • Travel Grant: for ICORES 2023, Lisbon, Portugal (Feb 2023)
  • Grant Application: BMBF Project Proposal "AI for Recommerce Markets", Feb 2022 (Role: Coordinator)
  • Grant: Funding for one PhD Position over 3 years (HPI Research School, Dec 2020)
  • Research Stay: KEDGE Business School, Marseille, France (Oct 2019)
  • Research Stay: CESIT Center of Excellence, Bordeaux, France (Oct 2019)
  • Travel Grant: for ICIAM 2019, Valencia, Spain (Invited Talk); Grant managed within CEREMADE Department of the University Paris Dauphine (July 2019)
  • Best Paper Award: Schlosser, R. "Stochastic Dynamic Pricing with Strategic Customers and Reference Price Effects", 8th International Conference on Operations Research and Enterprise Systems (ICORES 2019), Prague, Czech Republic
  • Best Paper Award: Schlosser, R., Richly, K. "Dynamic Pricing Strategies in a Finite Horizon Duopoly with Partial Information", 7th International Conference on Operations Research and Enterprise Systems (ICORES 2018), Funchal, Portugal
  • Program: OR 2015 Emerging Scholar Program, GOR
  • Grant: Postdoctoral Scholarship, 2014 - 2015, Humboldt Graduate School
  • Award Nomination:  Ph.D. Thesis nominated for Humboldt Prize 2014
  • Ph.D. Grant: Elsa-Neumann-Scholarship, 2011 - 2013, Berlin Senate

Teaching

At HPI:

At Humboldt University Berlin, School of Business and Economics:

  • Software in Operations Research (SS14, Evaluation 1,0)
  • Excel Seminar (SS14, Evaluation 1,4)
  • Software in Operations Research (WS13/14, Evaluation 1,4)
  • Excel Seminar (WS13/14, Evaluation 1,3)
  • Teaching Assistant for Revenue Management, Operations Management (WS11/12-SS14)
  • Teaching Assistant for Operations Research I, II, III, IV (WS10/11-SS14)

Co-Supervision of PhD Students

Journal Reviewing & Refereeing

Analytics & Data Science

  • IEEE Transactions on Knowledge & Data Engineering
  • IEEE Transactions on Neural Networks & Learning Systems
  • Expert Systems with Applications
  • Engineering Applications of Artificial Intelligence
  • The Knowledge Engineering Review
  • ACM Transactions on Knowledge Discovery from Data
  • Journal of the Royal Statistical Society
  • Information Sciences

Management Science & Economics

  • Management Science
  • International Journal of Production Economics
  • International Journal of Production Research
  • Omega
  • Journal of Revenue & Pricing Management
  • Transportation Research (Part E)
  • International Journal of Operations & Quantitative Management
  • Journal of Retailing & Consumer Services
  • Applied Stochastic Models in Business & Industry
  • Production Planning & Control
  • Managerial & Decision Economics
  • Economics E-Journal
  • Electronic Commerce Research and Applications
  • Journal of Economic Dynamics & Control

Operations Research

  • European Journal of Operational Research
  • Journal of the Operational Research Society
  • Operations Research Perspectives
  • IMA Journal of Management Mathematics
  • Operational Research
  • Optimal Control, Applications & Methods
  • RAIRO - Operations Research
  • TOP
  • OR Spectrum
  • Applied Mathematics & Computation
  • Simulation Modelling Practice and Theory
  • Journal of Computational and Applied Mathematics
  • Mathematical Methods of Operations Research

Information Systems & Engineering

  • Business & Information Systems Engineering
  • Computers & Industrial Engineering
  • SIGMOD Record
  • Systems Science & Control Engineering
  • Journal of Systems Science & Systems Engineering
  • Science China Information Sciences
  • Journal of Process Mechanical Engineering
  • Heliyon
  • Springer Nature Applied Sciences
  • IISE Transactions
  • IEEE Transactions on Systems, Man & Cybernetics
  • IEEE Access
  • Expert Systems
  • Applied Soft Computing
  • ACM Computing Surveys

Research Foundations

  • Czech Science Foundation (GACR)
  • Dutch Research Council (NWO)
  • German Research Foundation (DFG)

      Sustainability

      • Journal of Cleaner Production
      • Resources Conservation & Recycling
      • Mathematical Biosciences & Engineering
      • Maritime Policy & Management
      • Green Finance
      • Sustainable Computing: Informatics & Systems
      • Journal of Energy Storage

      Program Committees

      • ICLR 2025
      • IJCAI 2024
      • ECML-PKDD 2024
      • ICDE 2024
      • KDD 2024
      • ICORES 2024
      • IJCAI 2023
      • ECML-PKDD 2023
      • HICSS 2023
      • IJCAI-ECAI 2022

      Program Co-Chair

      • ICORES (since March 2024)

      Scientific Communities: INFORMS, ACM, IEEE, GOR (AG PRM), DHV

      Patents and Patent Applications

      • Kossmann, J., R. Schlosser, A. Kastius, M. Perscheid, H. Plattner (2022). Training an Agent for Iterative Multi-Attribute Index Selection, Patent Submission App., HPI26526EP (EP4227821A1, pending; US18/167,667, pending)
      • Schlosser, R., Kossmann, J., Boissier, M., Uflacker, M., Plattner, H. (2020). Iterative Multi-Attribute Index Selection for Large Database Systems, Patent Submission  App. (EP3719663, accepted Oct 2022; US16/838,830, pending; CN111797118A, pending)
      • Schlosser, R., Boissier, M., Uflacker, M., Plattner, H. (2019). Data Placement in Hybrid Data Layouts for Tiered HTAP Databases, Patent Submission App. (EP3547166A1,  accepted Nov 2020; US11/256,718, accepted Feb 2022; CN110362566A, accepted Mar 2024)

      Publications

      • 1.
        Halfpap, S., Schlosser, R.: Fragment Allocations for Partially Replicated Databases Considering Data Modifications and Changing Workloads. CIKM 2024. pp. 758–767 (2024).
        [ BibTeX ]
         
      • 2.
        Mattes, P., Schlosser, R., Herbrich, R.: Hieros: Hierarchical Imagination on Structured State Space Sequence World Models. ICML 2024, accepted (2024).
        [ BibTeX ]
         
      • 3.
        Halfpap, S., Kossmann, J., Schlosser, R., Markl, V.: Looking Deeply into the Magic Mirror: An Interactive Analysis of Database Index Selection Approaches. VLDB 2024, PVLDB 17 (12), accepted (2024).
        [ BibTeX ]
         
      • 4.
        Groeneveld, J., Herrmann, J., Mollenhauer, N., Dreessen, L., Bessin, N., Schulze-Tast, J., Kastius, A., Huegle, J., Schlosser, R.: Self-Learning Agents for Recommerce Markets. Business & Information Systems Engineering 66 (4). 441–463 (2024).
        [ BibTeX ]
         
      • 5.
        Schlosser, R., Gönsch, J.: Risk-Averse Dynamic Pricing using Mean-Semivariance Optimization. European Journal of Operational Research. 310 (1), 1151–1163 (2023).
        [ BibTeX ]
         
      • 6.
        Schlosser, R., Weisgut, M., Huebscher, L., Nordemann, O.: Robust Index Selection for Stochastic Dynamic Workloads. Springer Nature Computer Science. 4 (1), 59 (2023).
        [ BibTeX ]
         
      • 7.
        Perscheid, M., Plattner, H., Ritter, D., Schlosser, R., Teusner, R.: Enterprise Platform and Integration Concepts Research at HPI. ACM SIGMOD Record. 51 (4), 68–73 (2023).
        [ BibTeX ]
         
      • 8.
        Schröder, K., Kastius, A., Schlosser, R.: Welcome to the Jungle: A Conceptual Comparison of Reinforcement Learning Algorithms. ICORES 2023. pp. 143–150 (2023).
        [ BibTeX ]
         
      • 9.
        Schlosser, R., Chenavaz, R.: Joint Dynamic Pricing and Marketing-Mix Strategies for Revenue Management Applications with Stochastic Demand. International Transactions in Operational Research, accepted. (2023).
        [ BibTeX ]
         
      • 10.
        Huegle, J., Hagedorn, C., Schlosser, R.: A kNN-based Non-Parametric Conditional Independence Test for Mixed Data and Application in Causal Discovery. ECML-PKDD 2023, accepted (2023).
        [ BibTeX ]
         
      • 11.
        Klinke, P., Naumann, A., Wersich, R., Schlosser, R.: Coopetition: Learning to Play Skat using Reinforcement Learning. EGAI @ ECAI 2023 (2023).
        [ BibTeX ]
         
      • 12.
        Schröder, K., Kastius, A., Schlosser, R.: A Comparison of State-of-the-Art Reinforcement Learning Algorithms Applied to the Traveling Salesman Problem. The Knowledge Engineering Review, accepted. (2023).
        [ BibTeX ]
         
      • 13.
        Kossmann, J., Kastius, A., Schlosser, R.: SWIRL: Selection of Workload-aware Indexes using Reinforcement Learning. 25th International Conference on Extending Database Technology (EDBT 2022). pp. 155–168 (2022).
        [ BibTeX ]
         
      • 14.
        Richly, K., Schlosser, R., Brauer, J.: Enabling Risk-averse Dispatch Processes for Transportation Network Companies by Probabilistic Location Prediction. Communications in Computer and Information Science, Springer. 1623, 21–42 (2022).
        [ BibTeX ]
         
      • 15.
        Schlosser, R.: Heuristic Mean Variance Optimization in Markov Decision Processes using State-Dependent Risk Aversion. IMA Journal of Management Mathematics. 33 (2), 181–199 (2022).
        [ BibTeX ]
         
      • 16.
        Chenavaz, R., Klibi, W., Schlosser, R.: Dynamic Pricing with Reference Price Effects in Integrated Online and Offline Retailing. International Journal of Production Research. 60, 5854–5875 (2022).
        [ BibTeX ]
         
      • 17.
        Weisgut, M., Hübscher, L., Nordemann, O., Schlosser, R.: Solver-Based Approaches for Robust Multi-Index Selection Problems with Reconfiguration Costs under Stochastic Dynamic Workloads. 11th International Conference on Operations Research and Enterprise Systems (ICORES 2022). pp. 28–39 (2022).
        [ BibTeX ]
         
      • 18.
        Kastius, A., Schlosser, R.: Towards Transfer Learning for Revenue and Pricing Management. Operations Research Proceedings, OR2021. pp. 361–366 (2022).
        [ BibTeX ]
         
      • 19.
        Schlosser, R., Kastius, A.: Stochastic Dynamic Pricing under Duopoly Competition with Mutual Strategy Adjustments. Operations Research Proceedings (OR 2021). pp. 367–372 (2022).
        [ BibTeX ]
         
      • 20.
        Richly, K., Schlosser, R., Boissier, M.: Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications. Proceedings of the VLDB Endowment. pp. 4079–4092 (2022).
        [ BibTeX ] [ Download ]
         
      • 21.
        Hagedorn, C., Huegle, J., Schlosser, R.: Understanding Unforeseen Production Downtimes in Manufacturing Processes using Log Data-driven Causal Reasoning. Journal of Intelligent Manufacturing. 33, 2027–2043 (2022).
        [ BibTeX ]
         
      • 22.
        Schlosser, R., Westphal, J., Pörschke, M., Maltenberger, T., Kaminsky, Y.: Self-Adaptive Agents in a Dynamic Pricing Duopoly: Competition, Collusion, and Risk Considerations. Springer Nature Computer Science. 3 (3), 1–17 (2022).
        [ BibTeX ]
         
      • 23.
        Perscheid, M., Plattner, H., Ritter, D., Schlosser, R., Teusner, R.: Das Fachgebiet “Enterprise Platform and Integration Concepts” am Hasso-Plattner-Institut. Datenbank-Spektrum. 22, 175–180 (2022).
        [ BibTeX ]
         
      • 24.
        Figge, F., Dimitrov, S., Schlosser, R., Chenavaz, R.: Does the circular economy fuel the throwaway society? The role of opportunity costs for products that lose value over time. Journal of Cleaner Production. 368 (133207), (2022).
        [ BibTeX ]
         
      • 25.
        Hagedorn, C., Lange, C., Huegle, J., Schlosser, R.: GPU Acceleration for Information-theoretic Constraint-based Causal Discovery. In: Le, T.D., Liu, L., Kıcıman, E., Triantafyllou, S., and Liu, H. (eds.) Proceedings of The KDD’22 Workshop on Causal Discovery, Proceedings of Machine Learning Research (PMLR) 185. pp. 30–60 (2022).
        [ BibTeX ]
         
      • 26.
        Schlosser, R., Kastius, A.: A Conceptual Framework for Studying Self-Learning Agents in Recommerce Markets. Operations Research Proceedings (OR 2022), to appear (2022).
        [ BibTeX ]
         
      • 27.
        Kastius, A., Schlosser, R.: Multi-Agent Dynamic Pricing Using Reinforcement Learning and Asymmetric Information. Operations Research Proceedings (OR2022), to appear (2022).
        [ BibTeX ]
         
      • 28.
        Braun, T., Hurdelhey, B., Meier, D., Tsayun, P., Hagedorn, C., Huegle, J., Schlosser, R.: GPUCSL: GPU-Based Library for Causal Structure Learning. ICDM Open Project Forum. pp. 1236–1239 (2022).
        [ BibTeX ]
         
      • 29.
        Kastius, A., Schlosser, R.: Dynamic Pricing under Competition using Reinforcement Learning. Journal of Revenue and Pricing Management. 21, 50–63 (2022).
        [ BibTeX ]
         
      • 30.
        Kaminsky, Y., Maltenberger, T., Pörschke, M., Westphal, J., Schlosser, R.: Pricing Competition in a Duopoly with Self-Adapting Strategies. 10th International Conference on Operations Research and Enterprise Systems (ICORES 2021). pp. 60–71 (2021).
        [ BibTeX ]
         
      • 31.
        Richly, K., Schlosser, R., Brauer, J., Plattner, H.: A Probabilistic Location Prediction Approach to Optimize Dispatch Processes in the Ride-Hailing Industry. HICSS 2021. pp. 1830–1840 (2021).
        [ BibTeX ]
         
      • 32.
        Schlosser, R.: Scalable Relaxation Techniques to Solve Stochastic Dynamic Multi-Product Pricing Problems with Substitution Effects. Journal of Revenue and Pricing Management. 20 (1), 54–65 (2021).
        [ BibTeX ]
         
      • 33.
        Schlosser, R., Halfpap, S.: Robust and Memory-Efficient Database Fragment Allocation for Large and Uncertain Database Workloads. 24th International Conference on Extending Database Technology (EDBT 2021). pp. 367–372 (2021).
        [ BibTeX ]
         
      • 34.
        Halfpap, S., Schlosser, R.: Memory-Efficient Database Fragment Allocation for Robust Load Balancing when Nodes Fail. 37th IEEE International Conference on Data Engineering, ICDE 2021, Chania, Greece, April 19-22, 2021. pp. 1811–1816 (2021).
        [ BibTeX ] [ Download ]
         
      • 35.
        Schlosser, R., Chenavaz, R., Dimitrov, S.: Circular Economy: Joint Dynamic Pricing and Recycling Investments. International Journal of Production Economics. 108117, 1–13 (2021).
        [ BibTeX ]
         
      • 36.
        Richly, K., Schlosser, R., Boissier, M.: Joint Index, Sorting, and Compression Optimization for Memory-Efficient Spatio-Temporal Data Management. 37th IEEE International Conference on Data Engineering (ICDE). pp. 1901–1906 (2021).
        [ BibTeX ] [ Download ]
         
      • 37.
        Huegle, J., Hagedorn, C., Boehme, L., Poerschke, M., Umland, J., Schlosser, R.: MANM-CS: Data Generation for Benchmarking Causal Structure Learning from Mixed Discrete-Continuous and Nonlinear Data. WHY-21 @ NeurIPS 2021 (2021).
        [ BibTeX ]
         
      • 38.
        Kossmann, J., Schlosser, R.: Self-driving database systems: a conceptual approach. Distributed and Parallel Databases. 38 (4), 795–817 (2020).
        [ BibTeX ]
         
      • 39.
        Richly, K., Brauer, J., Schlosser, R.: Predicting Location Probabilities of Drivers to Improve Dispatch Decisions of Transportation Network Companies Based on Trajectory Data. 9th International Conference on Operations Research and Enterprise Systems, ICORES 2020. pp. 47–58 (2020).
        [ BibTeX ]
         
      • 40.
        Schlosser, R.: Stochastic Dynamic Pricing with Waiting and Forward-Looking Consumers. Communications in Computer and Information Science (CCIS), Vol. 1162. pp. 47–69. Springer (2020).
        [ BibTeX ]
         
      • 41.
        Schlosser, R., Halfpap, S.: A Decomposition Approach for Risk-Averse Index Selection. 32nd International Conference on Scientific and Statistical Database Management (SSDBM 2020). pp. 16:1–16:4 (2020).
        [ BibTeX ] [ Download ]
         
      • 42.
        Schlosser, R.: Risk-Sensitive Control of Markov Decision Processes: A Moment-Based Approach with Target Distributions. Computers and Operations Research. 123 (104997), 1–15 (2020).
        [ BibTeX ]
         
      • 43.
        Kossmann, J., Halfpap, S., Jankrift, M., Schlosser, R.: Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms. Proceedings of the VLDB Endowment. pp. 2382–2395 (2020).
        [ BibTeX ] [ Download ]
         
      • 44.
        Halfpap, S., Schlosser, R.: Exploration of Dynamic Query-Based Load Balancing for Partially Replicated Database Systems with Node Failures. CIKM ’20: The 29th ACM International Conference on Information and Knowledge Management. pp. 3409–3412 (2020).
        [ BibTeX ]
         
      • 45.
        Kossmann, J., Schlosser, R.: A Framework for Self-Managing Database Systems. 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW). pp. 100–106 (2019).
        [ BibTeX ] [ Download ]
         
      • 46.
        Schlosser, R., Richly, K.: Dynamic Pricing under Competition with Data-Driven Price Anticipations and Endogenous Reference Price Effects. Journal of Revenue & Pricing Management. 18, 451–464 (2019).
        [ BibTeX ]
         
      • 47.
        Schlosser, R., Kossmann, J., Boissier, M.: Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies. 35th IEEE International Conference on Data Engineering, ICDE. pp. 1238–1249. IEEE (2019).
        [ BibTeX ] [ Download ]
         
      • 48.
        Halfpap, S., Schlosser, R.: Workload-Driven Fragment Allocation for Partially Replicated Databases Using Linear Programming. IEEE 35th International Conference on Data Engineering (ICDE 2019). pp. 1746–1749 (2019).
        [ BibTeX ] [ Download ]
         
      • 49.
        Halfpap, S., Schlosser, R.: A Comparison of Allocation Algorithms for Partially Replicated Databases. IEEE 35th International Conference on Data Engineering (ICDE 2019). pp. 2008–2011 (2019).
        [ BibTeX ] [ Download ]
         
      • 50.
        Schlosser, R.: Stochastic Dynamic Pricing with Strategic Customers and Reference Price Effects. 8th International Conference on Operations Research and Enterprise Systems, ICORES 2019. pp. 179–188 (2019).
        [ BibTeX ]
         
      • 51.
        Schlosser, R., Walther, C., Boissier, M., Uflacker, M.: Automated Repricing and Ordering Strategies in Competitive Markets. AI Communications. 32, 15–29 (2019).
        [ BibTeX ] [ Download ]
         
      • 52.
        Schlosser, R.: Data-Driven Stochastic Dynamic Pricing and Ordering. Operations Research Proceedings 2018. pp. 397–403 (2019).
        [ BibTeX ]
         
      • 53.
        Schlosser, R., Richly, K.: Dynamic Pricing Competition with Unobservable Inventory Levels: A Hidden Markov Model Approach. Communications in Computer and Information Science. pp. 15–36. Springer (2019).
        [ BibTeX ]
         
      • 54.
        Schlosser, R., Richly, K.: Dynamic Pricing Strategies in a Finite Horizon Duopoly with Partial Information. 7th International Conference on Operations Research and Enterprise Systems, ICORES 2018. pp. 21–30 (2018).
        [ BibTeX ]
         
      • 55.
        Schlosser, R., Boissier, M.: Optimal Repricing Strategies in a Stochastic Infinite Horizon Duopoly. Communications in Computer and Information Science (CCIS). pp. 129–150. Springer (2018).
        [ BibTeX ] [ Download ]
         
      • 56.
        Schlosser, R.: Stochastic Dynamic Multi-Product Pricing under Competition. Operations Research Proceedings 2017. pp. 527–533 (2018).
        [ BibTeX ]
         
      • 57.
        Schlosser, R., Boissier, M.: Dynamic Pricing under Competition on Online Marketplaces: A Data-Driven Approach. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD. pp. 705–714 (2018).
        [ BibTeX ] [ Download ]
         
      • 58.
        Boissier, M., Schlosser, R., Uflacker, M.: Hybrid Data Layouts for Tiered HTAP Databases with Pareto-Optimal Data Placements. 34th IEEE International Conference on Data Engineering, ICDE. pp. 209–220 (2018).
        [ BibTeX ] [ Download ]
         
      • 59.
        Schlosser, R., Boissier, M.: Dealing with the Dimensionality Curse in Dynamic Pricing Competition: Using Frequent Repricing to Compensate Imperfect Market Anticipations. Computers and Operations Research. 100, 26–42 (2018).
        [ BibTeX ] [ Download ]
         
      • 60.
        Schlosser, R., Walther, C., Boissier, M., Uflacker, M.: Data-Driven Inventory Management and Dynamic Pricing Competition on Online Marketplaces. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2018). pp. 5856–5858 (2018).
        [ BibTeX ] [ Download ]
         
      • 61.
        Zimmermann, T., Djürken, T., Mayer, A., Janke, M., Boissier, M., Schwarz, C., Schlosser, R., Uflacker, M.: Detecting Fraudulent Advertisements on a Large E-Commerce Platform. Proceedings of the Nineteenth International Workshop on Data Warehousing and OLAP, DOLAP, Venice, Italy, March 21, 2017 (2017).
        [ BibTeX ] [ Download ]
         
      • 62.
        Seiffert, M., Holstein, F., Schlosser, R., Schiller, J.: Next Generation Cooperate Wearables: Generalized Activity Assessment Computed Fully Distributed Within a Wireless Body Area Network. IEEE Access Journal. 5, 16793–16807 (2017).
        [ BibTeX ]
         
      • 63.
        Serth, S., Podlesny, N., Bornstein, M., Lindemann, J., Latt, J., Selke, J., Schlosser, R., Boissier, M., Uflacker, M.: An Interactive Platform to Simulate Dynamic Pricing Competition on Online Marketplaces. 21st IEEE International Enterprise Distributed Object Computing Conference, EDOC 2017, Quebec City, QC, Canada, October 10-13, 2017. pp. 61–66. IEEE (2017).
        [ BibTeX ] [ Download ]
         
      • 64.
        Boissier, M., Schlosser, R., Podlesny, N., Serth, S., Bornstein, M., Latt, J., Lindemann, J., Selke, J., Uflacker, M.: Data-Driven Repricing Strategies in Competitive Markets: An Interactive Simulation Platform. Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys ’17). pp. 355–357. ACM, New York, NY, USA (2017).
        [ BibTeX ] [ Download ]
         
      • 65.
        Uflacker, M., Schlosser, R., Meinel, C.: Ertragsmanagement im Wandel - Potentiale der In-Memory Technologie. In: Gläß, R. and Leukert, B. (eds.) Handel 4.0: Die Digitalisierung des Handels. Strategien, Technologien, Transformation. pp. 177–190. Springer Gabler (2017).
        [ BibTeX ]
         
      • 66.
        Schlosser, R.: Stochastic Dynamic Pricing and Advertising in Isoelastic Oligopoly Models. European Journal of Operational Research. 259, 1144–1155 (2017).
        [ BibTeX ]
         
      • 67.
        Schlosser, R., Boissier, M.: Optimal Price Reaction Strategies in the Presence of Active and Passive Competitors. Proceedings of the 6th International Conference on Operations Research and Enterprise Systems (ICORES), Porto, Portugal. pp. 47–56 (2017).
        [ BibTeX ] [ Download ]
         
      • 68.
        Schlosser, R.: Joint Stochastic Dynamic Pricing and Advertising with Time-Dependent Demand. Journal of Economic Dynamics and Control. 73, 439–452 (2016).
        [ BibTeX ]
         
      • 69.
        Schlosser, R., Boissier, M., Schober, A., Uflacker, M.: How To Survive Dynamic Pricing Competition in E-commerce. Proceedings of the Poster Track of the 10th ACM Conference on Recommender Systems (RecSys 2016), Boston, USA, September 17, 2016 (2016).
        [ BibTeX ] [ Download ]
         
      • 70.
        Boissier, M., Djürken, T., Schlosser, R., Faust, M.: A Cost-Aware and Workload-Based Index Advisor for Columnar In-Memory Databases. 22nd International Conference, ICIST 2016, Druskininkai, Lithuania, October 13-15, 2016, Proceedings, CCIS 639. pp. 285–299 (2016).
        [ BibTeX ] [ Download ]
         
      • 71.
        Schlosser, R.: Stochastic Dynamic Multi-Product Pricing with Dynamic Advertising and Adoption Effects. Journal of Revenue and Pricing Management. 15, 153–169 (2016).
        [ BibTeX ]
         
      • 72.
        Schlosser, R.: Dynamic Pricing and Advertising Models with Inventory Holding Costs. Journal of Economic Dynamics and Control. 57, 163–181 (2015).
        [ BibTeX ]
         
      • 73.
        Schlosser, R.: A Stochastic Dynamic Pricing and Advertising Model under Risk Aversion. Journal of Revenue and Pricing Management. 14, 451–468 (2015).
        [ BibTeX ]
         
      • 74.
        Helmes, K., Schlosser, R.: Oligopoly Pricing and Advertising in Isoelastic Adoption Models. Dynamic Games and Applications. 5, 334–360 (2015).
        [ BibTeX ]
         
      • 75.
        Schlosser, R.: Dynamic Pricing with Time-Dependent Elasticities. Journal of Revenue and Pricing Management. 14, 365–383 (2015).
        [ BibTeX ]
         
      • 76.
        Helmes, K., Schlosser, R., Weber, M.: Dynamic Advertising and Pricing in a Class of General New-Product Adoption Models. European Journal of Operational Research. 229, 433–443 (2013).
        [ BibTeX ]
         
      • 77.
        Helmes, K., Schlosser, R.: Dynamic Advertising and Pricing with Constant Demand Elasticities. Journal of Economic Dynamics and Control. 37, 2814–2832 (2013).
        [ BibTeX ]
         

      Artificial Intelligence and Sustainability

      Our research group investigates both the use of energy in developing artificial intelligence (AI) as well as the use of AI in generating, storing and managing energy. This includes research into energy-efficient algorithms for solving basic AI tasks such as classification, ranking or planning & search, as well as the development and application of AI methods to refined modeling of batteries in order to extend their working lifetime, and the control of domestic energy consumption.

      Contact

      Prof. Dr. Ralf Herbrich
      Artificial Intelligence and Sustainability

      Room: F-E.03
      Tel.: +49 (0) 331 5509-3471
      E-Mail: ralf.herbrich(at)hpi.de


      Office: Thi Ngoc Dung Pham 

      Room: F-E.01
      Tel.: +49 (0) 331 5509-3468
      E-Mail: office-herbrich(at)hpi.de