Hasso-Plattner-Institut
Prof. Dr. h.c. Hasso Plattner
  
 

Data-Driven Decision Support

Data-driven decision support for enterprise applications and revenue management (RM) has become highly relevant in recent years as firms face the challenge of integrating data-driven automation in their processes. Specifically, our research group investigates how decision problems can be solved using quantitative methods of operations research and data science in order to improve automated decision-making in the areas of RM and business analytics.

Group Leader: Dr. Rainer Schlosser

Research Activities

Our scope involves (I) the identification of causal relations of underlying dynamics, (II) specific revenue management problems, as well as (III) resource allocation and database tuning problems in close collaboration with the Hyrise database group.

I Data-Driven Causal Inference & Causal Structure Learning

We address open challenges in the context of causal structure learning in practice, from data to learned causal structures, by improvements in both the application of statistical and probabilistic concepts, and the GPU-based acceleration. Read more.

Current Projects:

  • MPCSL - A Modular Pipeleine for Causal Structure Learning
  • Causal Structure Learning from Heterogeneous Data
  • Parallel Execution Strategies for Causal Structure Learning on GPUs
  • Application Scenarios with Cooperation Partners
    • Mechanical Engineering
    • Automotive Production

Contact: Christopher HagedornJohannes Huegle, Dr. Rainer Schlosser

II Revenue Management & Dynamic Applications

Over the years, RM applications have become increasingly difficult to administrate. The number of decisions to control business processes (e.g., dynamic price optimization, inventory management, resource allocation, etc.) have become too complex to be managed manually. Firms are forced to integrate effective automated decision support systems in order to be still profitable. However, while automated decision-making has enormous potential, it is also challenging to derive optimized decisions as most RM applications are typically highly complex stochastic dynamic optimization problems. Nevertheless, the overall vision is a self-driving decision support system, which automatically analyzes sales data and optimizes decisions in an explainable manner.

In general, decision problems can be described by given performance criteria, admissible decisions, constraints, and data-driven estimations of the interplay of decisions on performance. Further, every application has its own specifics, which can be exploited to solve a problem effectively by using suitable optimization techniques. In particular, we are interested in finding robust solutions for uncertain and changing environments (read more).

Current Projects:

Contact: Dr. Rainer Schlosser, Alexander Kastius, Martin Boissier

III Database Optimization & Resource Allocation

Over the years, databases have become increasingly difficult to administrate. The number of configuration options (cf. resource allocation problems such as index selection, data placement, data replication, selection of compression schemes, etc.), the diversity of workloads, and the sheer amount of data make it impossible for an administrator to find optimized settings. Moreover, database administrators usually lack the application domain knowledge to decide how data should be efficiently stored. Hence, the overall vision is a self-driving database system, which automatically analyzes workload patterns and optimizes its configuration.

Our goal is to improve existing tuning and resource allocation approaches, which usually address only one tuning feature in a deterministic (workload) setting, to be able to (i) consider the joint tuning of multiple features, (ii) to include reconfiguration and maintenance costs, and (iii) to identify risk-averse tuning configurations by considering multiple potential future workload scenarios. The challenge of an increased problem complexity can be addressed by using suitable heuristic optimization techniques as well as ML/RL-based approaches.

Current Projects:

  • Robust Database Optimization with Stochastic Workloads
  • Memory-efficient Fragment Allocation & Load Balancing
  • Dynamic Risk-aware Index Selection
  • Spatio-temporal Data Management
  • Self-tuning Databases

Contact: Martin Boissier, Stefan Halfpap, Jan Kossmann, Keven Richly, Dr. Rainer Schlosser

Teaching

We offer lectures, seminars, and projects on data-driven decision-making in enterprise applications. For HPI master students we also provide a varity of master theses topics. 

On a more applied level, we also offer a one-year bachelor project in cooperation with the industrial partner SAP SE in the winter semester. This year the project is about simulating markets with self-learning agents in the ReCommerce industry (cf. sustainability & circular economy).

Job Offers

Our group in Potsdam is still growing. We always welcome applications of prospective Ph.D. students, who are interested in working with us. In this context, we are able to offer Ph.D. scholarships directly by the research group or for one of the two HPI Research Schools, Service-Oriented Systems Engineering or Data Science and Engineering.

Additionally, we are constantly looking for HPI-students who are interested in a part-time job as a student research assistant

Contact: Dr. Rainer Schlosser

Selected Publications

Our research has been published in renowned OR Journals (EJOR, JEDC, COR, IJPE, DGAA, JRPM), distinguished data science conferences (KDD, IJCAI, RECSYS, SDM), and leading computer science venues (VLDB, ICDE, EDBT, DAPD, CIKM, SSDBM).

  • 1.
    Schlosser, R., Chenavaz, R., Dimitrov, S.: Circular Economy: Joint Dynamic Pricing and Recycling Investments. International Journal of Production Economics. 108117, 1–13 (2021).
     
  • 2.
    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).
     
  • 3.
    Kastius, A., Schlosser, R.: Dynamic Pricing under Competition using Reinforcement Learning. Journal of Revenue and Pricing Management. 1–22 (2021).
     
  • 4.
    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).
     
  • 5.
    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).
     
  • 6.
    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).
     
  • 7.
    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).
     
  • 8.
    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).
     
  • 9.
    Schlosser, R., Walther, C., Boissier, M., Uflacker, M.: Automated Repricing and Ordering Strategies in Competitive Markets. AI Communications. 32, 15–29 (2019).
     
  • 10.
    Schlosser, R.: Data-Driven Stochastic Dynamic Pricing and Ordering. Operations Research Proceedings 2018. pp. 397–403 (2019).
     
  • 11.
    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).
     
  • 12.
    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).
     
  • 13.
    Schlosser, R.: Stochastic Dynamic Multi-Product Pricing under Competition. Operations Research Proceedings 2017. pp. 527–533 (2018).
     
  • 14.
    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).
     
  • 15.
    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).
     
  • 16.
    Schlosser, R., Boissier, M.: Dynamic Pricing under Competition on Online Marketplaces: A Data-Driven Approach. KDD ’18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 705–714 (2018).
     
  • 17.
    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).
     
  • 18.
    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).
     
  • 19.
    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).
     
  • 20.
    Schlosser, R.: Stochastic Dynamic Pricing and Advertising in Isoelastic Oligopoly Models. European Journal of Operational Research. 259, 1144–1155 (2017).
     
  • 21.
    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).
     
  • 22.
    Schlosser, R.: Joint Stochastic Dynamic Pricing and Advertising with Time-Dependent Demand. Journal of Economic Dynamics and Control. 73, 439–452 (2016).
     
  • 23.
    Schlosser, R.: Stochastic Dynamic Multi-Product Pricing with Dynamic Advertising and Adoption Effects. Journal of Revenue and Pricing Management. 15, 153–169 (2016).
     
  • 24.
    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).
     
  • 25.
    Schlosser, R.: Dynamic Pricing and Advertising Models with Inventory Holding Costs. Journal of Economic Dynamics and Control. 57, 163–181 (2015).
     
  • 26.
    Schlosser, R.: Dynamic Pricing with Time-Dependent Elasticities. Journal of Revenue and Pricing Management. 14, 365–383 (2015).
     
  • 27.
    Schlosser, R.: A Stochastic Dynamic Pricing and Advertising Model under Risk Aversion. Journal of Revenue and Pricing Management. 14, 451–468 (2015).