Prof. Dr. Rouven Schur


Prof. Dr. Rouven Schur

LC 128b
+49 203 37-91451


Seit 01/2021                  Lehrstuhlinhaber für Produktions- und Logistikplanung,
                                         Juniorprofessur mit Tenure Track
                                        Universität Duisburg-Essen

11/2019 – 12/2020       akademischer Rat a. Z.,
                                        Lehrstuhl für Analytics and Optimization,
                                        Universität Augsburg

08/2019                         Promotion an der Universität Augsburg,
                                        Dynamic Pricing under Complex Behavior

04/2014 – 11/2019       wissenschaftlicher Mitarbeiter,
                                        Lehrstuhl für Analytics and Optimization,
                                        Universität Augsburg

02/2014                         Master of Science with honors,
                                        Finanz- & Informationsmanagement,
                                        Technische Universität München und Universität Augsburg

09/2011                         Bachelor of Science, München
                                        Technische Universität München

Ehrungen und Auszeichnungen:

Wissenschaftspreis der Stiftung der Universität Augsburg (2019)


Dynamic Pricing & Revenue Management

Modellierung von Kundenwahlverhalten

Berücksichtigung von Risikoaversion

Belieferung der letzten Meile


  • Schur, Rouven; Winheller, Kai: Optimizing Last-Mile Delivery: A Dynamic Compensation Strategy for Occasional Drivers. PDFBIB DownloadDetails

    Amid the rapid growth of online retail, last-mile delivery faces significant challenges, including the cost-effective delivery of goods to all customers. Accordingly, the development and improvement of innovative approaches thrive in current research. Our work contributes to this stream by applying dynamic pricing techniques to effectively model the possible involvement of the crowd in fulfilling delivery tasks. The use of occasional drivers (ODs) as a viable, cost-effective alternative to traditional dedicated drivers (DDs) prompts the necessity to focus on the inherent challenge posed by the uncertainty of ODs’ arrival times and willingness to perform deliveries.

    We introduce a dynamic programming framework that offers individualized bundles of delivery task and compensation to ODs as they arrive. This model, akin to a reversed form of dynamic pricing, accounts for ODs’ decision-making by treating their acceptance thresholds as a random variable. Thereby, our model addresses the dynamic and stochastic nature of OD availability and decision-making. We analytically solve the stage-wise optimization problem, outline inherent challenges such as the curses of dimensionality, and present structural properties. Designed to cope with these challenges, our approximation methods, a parametric value function approximation and a fluid approximation, aim to accurately determine avoided costs, which are a key factor in calculating optimal compensation.

    A comprehensive simulation study compares our algorithms with benchmark strategies, and shows the advantages of dynamic compensation across a range of scenarios. We conclude our work with managerial insights and a summary of our findings, offering significant implications for last-mile delivery operations.

  • Schur, R.: Approximately Optimal Solutions for Nonlinear Dynamic Pricing in the Presence of Multiunit Demand. PDFBIB DownloadDetails

    We consider a dynamic pricing setting where a firm sells a perishable product over a finite selling horizon. Different from the standard setting, the firm faces multiunit demand and can separately quote a price for every batch size. Customers differ in their attraction to the product and their preference regarding the batch size. These two attributes are depicted by a random variable each and are the basis for the calculation of customers’ willingness-to-pay. The resulting customer choice model is very challenging to work with and we put some effort into reducing its complexity. We develop optimality conditions for the stage-wise optimization problem. As finding the optimal solution in every state is non-trivial, we resort to formulating a fluid approximation model. With a simplifying assumption, we can solve this approximation and subsequently verify that this assumption indeed holds for the optimal solution. The resulting static pricing policy is approximately optimal in our dynamic setting. However, instead of applying this static policy, we use it to ensure approximate optimality of the three novel heuristics we developed in this paper. We test all heuristics in a simulation study against an upper bound and analyze patterns in the corresponding policies to gain managerial insights. For example, we find that a piecewise linear pricing structure performs very well and might be an easy-to-communicate alternative to full nonlinearity.

  • Schur, R.: Multiunit Dynamic Pricing with Different Types of Observable Customer Information. In: OR Spectrum (2024). PDFVolltextBIB DownloadDetails

    Dynamic Pricing, enabled by technological developments, is gaining more importance in fields beyond the airline industry, including retail, where neglecting multiunit demand leads to suboptimal prices and lost revenues. In these fields, nonlinear pricing is a common static pricing strategy that explicitly takes multiunit demand into account but lacks the possibility to dynamically adapt prices. In this paper, we bring the strengths of both pricing strategies together by combining them to multiunit dynamic pricing. We formulate the corresponding stage-wise optimization problem. To account for customers' preferences regarding batch size, we adapt an adequate customer choice model based on (random) willingness-to-pay. The willingness-to-pay is defined by a combination of customer’s attraction to and consumption of the product. These two aspects of customers’ preferences are private information, but the firm may have (partial) access to the information of the current customer. The firm is monopolistic and can price-discriminate between different order sizes by quoting nonlinear batch prices. This work investigates three cases of what information is observable: attraction to the product, consumption of the product, or both. We solve the resulting optimization model analytically and derive closed-form expressions of the optimal solution in two of the cases. Moreover, we proof the desirable monotonicity in time and capacity is still intact. Building on this monotonicity, we show dynamics of the optimal pricing policy. Finally, we examine the value of information in a numerical study to gain managerial insights regarding the importance of knowing customers’ preferences.

  • Schur, R.: Dynamic Pricing under Complex Behavior. Universität Augsburg 2019. BIB DownloadDetails
  • Schur, R.; Gönsch, J.; Hassler, M.: Time-consistent, risk-averse dynamic pricing. In: European Journal of Operational Research, Jg. 477 (2019) Nr. 2, S. 587-603. BIB DownloadDetails
  • Gönsch, J.; Hassler, M.; Schur, R.: Optimizing conditional value-at-risk in dynamic pricing. In: OR Spectrum, Jg. 40 (2018) Nr. 3, S. 711-750. BIB DownloadDetails


  • 21. Arbeitsgruppensitzung der GOR AG "Pricing & Revenue Management" (Frankfurt 28.02.-01.03.2024) —Vortrag: "Multiunit Dynamic Pricing with different types of customer informantion" 
  • OR 2023 — International Conference on Operations Research (Hamburg, 29.08.-01.09.2023) — Vortrag: "Dynamic Pricing with Target Revenue"
  • 20. Arbeitsgruppensitzung der GOR AG "Pricing & Revenue Management" (Düsseldorf 02.-03.03.2023)
  • OR 2022 — International Conference on Operations Research (Karlsruhe, 06.-09.09.2022) — Vortrag: "Nonlinear Dynamic Pricing in the Presence of Multiunit Demand"
  • 31. Workshop für QBWL (Online, 28.-29.03.2022)
  • EURO 2021 — 31st European Conference on Operational Research (Athen,  11.-14.07.2021) — Vortrag: "Nonlinear Dynamic Pricing in the Presence of Multiunit Demand"
  • 18. Arbeitsgruppensitzung der GOR AG "Pricing & Revenue Management" (Online, 05., 12., 26.02.2021)
  • 17. Arbeitsgruppensitzung der GOR AG "Pricing & Revenue Management" (München, 07.02.2020)
  • 6. Intensiv-Workshop Operations Research (Würzburg, 07.–09.10.2019) — Vortrag: "Time-consistent, risk-averse dynamic pricing"
  • 16. Arbeitsgruppensitzung der GOR AG "Pricing & Revenue Management" (München, 22.02.2019)
  • EURO 2018 — 29th European Conference on Operational Research (Valencia, 08.-11.07.2018) — Vortrag: "Dynamic pricing with multiunit purchases"
  • 5. Intensiv-Workshop Operations Research (Würzburg, 04.–06.10.2017)
  • 27. Workshop für QBWL (Sylt, 13.–16.03.2017)
  • 14. Arbeitsgruppensitzung der GOR AG "Pricing & Revenue Management" (Zürich, 17.02.2017)
  • 24. Süd-Workshop für Quantitative Methoden, Graduate Program in Operations Management (Augsburg, 15.07.2016)
  • 13. Arbeitsgruppensitzung der GOR AG "Pricing & Revenue Management" (München, 22.01.2016)
  • 4. Intensiv-Workshop Operations Research (Würzburg, 21.–23.09.2015)
  • OR 2015 — International Conference on Operations Research (Wien, Österreich, 01.–03.09.2015)
  • 25. Workshop für QBWL (Schwerte, 16.–19.03.2015) — Vortrag: "Berücksichtigung von Risikoaversion im Dynamic Pricing durch Optimierung des Conditional Value-at-Risk"
  • 21. Süd-Workshop für Quantitative Methoden, Graduate Program in Operations Management (München, 28.11.2014)
  • OR 2014 — International Conference on Operations Research (Aachen, 02.–05.09.2014)
  • Doktorandenworkshop "Methods for Solving Industrial Based Scheduling and Routing Problems" mit Prof. Dr. Jonathan F. Bard, University of Texas at Austin, USA, Graduate Program in Operations Research & Industrial Engineering (Augsburg, 03.–17.06.2014)
  • 11. Arbeitsgruppensitzung der GOR AG "Pricing & Revenue Management" (München, 14.02.2014)