WHU | Logo

Mercator Endowed Chair of Demand Management & Sustainable Transport

Our work is focussed on developing innovative digital technologies to enable sustainable transportation. One major theme in that context is the combination of demand management concepts (such as dynamic pricing or availability control of services) and classic transportation/logistics management (such as route optimisation) so as to increase sustainability.

Welcome to the Mercator Endowed Chair of Demand Management & Sustainable Transport. 

Our work encompasses planning and control problems in urban logistics, mobility as well as air traffic management. Typically, these applications involve customer choice modelling, optimal control, large-scale optimisation and optimal learning. We develop solutions in collaboration with various stakeholders.

Professor Arne Strauss on Google Scholar

Our team

Jens Frische
Jens Frische
Research Assistant / Doctoral Candidate
Gideon Gottschalg
Gideon Gottschalg
Research Assistant / Doctoral Candidate
Vivien Schoepf
Vivien Schoepf
Research Assistant / Doctoral Candidate
Dr Jan-Rasmus Künnen
Dr Jan-Rasmus Künnen

Teaching – our courses offered in 2024-25

Sustainable Urban Transport BSc

This course is concerned with creating awareness of what is currently happening in the domain of sustainable mobility and transport solutions. Moreover, we will discuss how to evaluate innovative business models, assess their eco-efficiency and sustainability potential, and consider some data-driven modelling approaches that help to achieve sustainability.

The course features several case studies to illustrate the concepts in a hands-on fashion. Content-wise, we look at post-Covid-19 trends, sustainability assessment, green vehicles (electric, shared mobility, autonomous driving), innovative logistics concepts and on-demand air mobility.

Predictive Analytics 

This course is dedicated to conveying a sense of how analytics projects work so as to be able to manage them and/or assess their merit. 

It is not a modelling course - although we will do modelling. It is also not a programming course - although we will do plenty of programming in R. Instead, the modelling and programming just serves as an illustration of the steps featured in typical analytics projects. This should help in the planning of such a project, starting from understanding of the business problem over modelling up to model assessment and communication of the project's results (or a project proposal) to a client.

There is no classic split between lecture sessions and tutorial sessions; instead, lecture elements, practical demonstrations and exercises are mixed together in all sessions so as to create a more engaging environment. In an assessed groupwork, you will go through all the stages of a data science project including shaping the business objectives and connecting the modelling results to them.

The syllabus looks as follows:

  1. Introduction to the CRISP-DM process (business understanding)
  2. Sampling and Partitioning (data preparation)
  3. Information selection, modelling and overfitting (modelling)
  4. Model evaluation
  5. Applications: Naïve Bayes, association mining, clustering, text mining


PT MBA / FT MBA / Online MBA - Data Science & AI

With the dramatically increased use of data science in business there comes an even higher increased need for managers with knowledge of the fundamentals of data science to make effective decisions: McKinsey estimated that about 10 managers with these skills will be needed for every data scientist (because leverage from a data science team can be gotten in multiple areas of the business).

This course seeks to impart this knowledge. Specifically, the objective is to convey an understanding of data science sufficient to become a critical consumer of data science solutions. You will acquire the skills needed to ask the right questions when consultants are proposing data science projects, and you will be able to communicate better with internal data science teams as you will have an understanding of how data scientists work. The aim is not to train you to become a data scientist, but to work with them as a manager.

The following concepts are covered (taught in a hands-on, case-based manner):

  • Introduction to the Cross-Industry Standard Process for Data Mining: from business understanding over data understanding, data preparation, modelling, evaluation to deployment.
  • Data types and why this matters
  • Data sampling and partitioning
  • Conceptual understanding of key machine learning models for predictive analytics (decision trees, linear classifiers, …)
  • What is a good model? Evaluation and visualisation of model performance
  • Data Science and business strategy: assessing data science project proposals, working with data scientists
  • A manager's guide to Generative AI
  • Visualization concepts, interactive maps and dashboards: theory and practice using Tableau
Fundamentals of Optimization – Doctoral Program

Optimization is important to many applications in business, be that finance, operations, marketing or others. This course aims to provide a broad overview of the concepts that underpin optimization to help students to gain an understanding of what type of optimization problem they may be dealing with in their studies, and how this could be tackled.

Coverage includes:

  • Structure of an optimization problem
  • Deterministic versus stochastic optimization
  • Continuous versus discrete optimization
  • Constrained versus unconstrained optimization
  • Fundamentally important concepts like convexity, duality, complexity, total unimodularity, ...
  • Introduction to various techniques including linear and non-linear mathematical programming, (approximate) dynamic programming for control problems, optimal learning

We will not go overly deep into the topics due to time constraints; instead, the focus is on imparting an intuitive understanding of optimization techniques and of structures that can be exploited. The intention is to make this course useful and relevant to any students who face some form of optimization problem and who do not yet have received formal training in optimization.

Our publications

European Journal of Operational Research, Vol. 315 (3), pp. 913-925
An algorithm for flexible transshipments with perfect synchronization
Falkenberg, S., Spinler, S., Strauss, A. (2024)

Journal of the Operational Research Society, Vol. 75 (3), pp. 423–617
Operational research: methods and applications
Petropoulos, F, …, Strauss, A. et al. (2024)

European Journal of Operational Research, Vol. 310 (1), pp. 168-184
Dynamic multi-period vehicle routing with touting
Keskin, M., Branke, J., Deineko, V., Strauss, A. (2023)

Transportation Science, Vol. 57 (4), pp. 999-1018
Cross-border capacity planing in air traffic management under uncertainty
Künnen, J.-R., Strauss, A., Ivanov, N., Jovanovic, R., Fichert, F., Starita, S. (2023)

Service Science, Vol. 15 (1), pp. 22-40
Feeding the nation
Schwamberger, J., Fleischmann, M., Strauss, A. (2023)

Transportation Research Part A: Policy and Practice, Vol. 174, 103716
Leveraging demand-capacity balancing to reduce air traffic emissions and improve overall network performance
Künnen, J.-R., Strauss, A., Ivanov, N., Jovanovic, R., Fichert, F. (2023)

Transportation Research Part B: Methodological, Vol. 160, pp. 76-96
The value of flexible flight-to-route assignments in pre-tactical air traffic management
Künnen, J.-R., Strauss, A. (2022)

European Journal of Operational Research, Vol. 294 (3), pp. 1022-1041
Dynamic pricing of flexibel time slots for attended home delivery
Strauss, A., Gülpinar, N., Zheng, Y. (2021)

Flexible Services and Manufacturing Journal, Vol. 33 (1), pp. 253-280
Home healthcare routing and scheduling of multiple nurses in a dynamic environment
Demirbilek, M., Branke, J., Strauss, A. (2021)

European Journal of Operational Research, Vol. 284 (2), pp. 397-412
A review of revenue management
Klein, R., Koch, S., Steinhard, C., Strauss, A. (2020)

Transportation Science, Vol. 54 (4), pp. 882-896
Air traffic control capacity planning under demand and capacity provision uncertainty
Starita, S., Strauss, A., Fei, X., Jovanovic, R., Ivanov, N., Pavlovic, G., Fichert, F. (2020)

Journal of Air Transport Management, Vol. 75, pp. 139-152
Coordinated capacity and demand management in a redesigned air traffic management value-chain
Ivanov, N., Jovanovic, R., Fichert, F., Strauss, A., Starita, S., Babic, O., Pavlovic, G. (2019)

Health Care Management Science, Vol. 22 (1), pp. 140-155
Dynamically accepting and scheduling patients for home healthcare
Demirbilek, M., Branke, J., Strauss, A. (2019)

Journal of Revenue and Pricing Management, Vol. 18 (1), pp. 27-48
Unconstraining methods for revenue management systems under small demand
Kourentzes, N., Li, D., Strauss, A. (2019)


Integrated Fleet and Demand Control for On-Demand Meal Delivery Platforms

Hildebrandt, F. D., Lesjak, Z., Strauss, A., Ulmer, M. W. (2022)


Dynamic Capacity Allocation in Remote Control Centres for Vehicle Teleoperation

G. Gottschalg and A.K. Strauss (2024) - under review

Get in touch with us –

We look forward to hearing from you

Our location

Mercator Endowed Chair of Demand Management & Sustainable Transport
WHU – Otto Beisheim School of Management
D'Esterstraße 9
56179 Vallendar
Mercator Endowed Chair of Demand Management & Sustainable Transport