Mercator Endowed Chair

of Demand Management & Sustainable Transport

About us

Welcome to the 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.

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.

Apply for the position of Research Assistant-Doctoral Candidate (f/m/d)


At the Mercator Chair you will conduct research on topics in the field of Demand Management and/or Sustainable Transport. The focus of the chair is on mathematical modelling in the intersection of these areas (such as "dynamic pricing of transportation services"). Typically, these problems involve mathematical optimisation, machine learning, etc., and are often developed on the basis of data from partner companies. The topic of the PhD thesis in this area is open and will be agreed upon based on common interests.

We look forward to hearing from you!

Learn more about vacancy

Apply for the position of Research Assistant-Doctoral Candidate (f/m/d)


At the Mercator Chair you will conduct research on topics in the field of Demand Management and/or Sustainable Transport. The focus of the chair is on mathematical modelling in the intersection of these areas (such as "dynamic pricing of transportation services"). Typically, these problems involve mathematical optimisation, machine learning, etc., and are often developed on the basis of data from partner companies. The topic of the PhD thesis in this area is open and will be agreed upon based on common interests.

We look forward to hearing from you!

Learn more about vacancy

Team

Our team

Professor Dr. Arne Strauss

Chairholder

Office X-205

+49 (0)261 6509 775
Send email

Learn more

Johanna Häring

Personal Assistant

Office X-202

+49 (0)261 6509 777
Send email

Jens Frische

Research Assistant / Doctoral Candidate

Office X-204

+49 (0)261 6509 778
Send email

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Alumni

Jan-Rasmus Künnen

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Teaching

Our Teaching –
Courses offered in 2022

Data Science for Business BSc

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 (supported by DataCamp). 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.

We will also cover visualization concepts in both theory and practice, using Tableau for the latter. In particular, we will look into dashboard design, interactive maps (such as the one shown in Fig 1) and charts, and how to structure sales pitches.

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. Evidence combination (Naïve Bayes, association mining) and visualization
  6. Visualization, dashboards, selling your project to end users

Pricing Analytics BSc

Pricing analytics and revenue management focuses on how a firm should model demand, set and update automated pricing and product availability decisions across its various selling channels in order to maximize its profitability. The use of such strategies has transformed the transportation and hospitality industries, and they are increasingly important in retail, telecommunications, entertainment, financial services, health care and manufacturing.

Within the broader area of pricing theory, the course places emphasis on tactical optimization of pricing and capacity allocation decisions, tackled using demand modeling and constrained optimization – the two main building blocks of revenue management systems.

Case studies provide hands-on experience of the subject. Students are using R for most of the exercises within the RStudio environment, involving training on both demand modeling and optimization problems. For example, in the context of B2B customized pricing, we look into the question of how to estimate the win probability function from historical data and how to use this to optimize individual price quotes.

The syllabus consists of the following:

  1. Introduction, customer valuation game
  2. Demand modelling (parametric, non-parametric models, unconstraining)
  3. Constrained price optimization, capacity control, network revenue management
  4. Dynamic price control, (approximate) dynamic programming
  5. Markdown pricing, behavioural pricing
  6. Customized B2B pricing, win probability function estimation

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.

Modern Tools and Applications of Data Science- MSc

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.

We will also cover visualization concepts in both theory and practice, using Tableau for the latter. In particular, we will look into dashboard design (and create a few such as the one in Fig. 1), interactive maps and charts, and how to structure sales pitches.

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. Evidence combination (Naïve Bayes, association mining) and visualization
  6. Visualization, dashboards, selling your project to end users
  7. Tableau: using web data connectors, calling R from within Tableau, and other more advanced topics

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.

PTMBA - Data Science for Managers

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
  • Visualization concepts, interactive maps and dashboards: theory and practice using Tableau

The course uses R to illustrate a data science project, but acquiring programming skills is no learning objective and, therefore, learning about R programming is entirely voluntary. 

Data Science for Decision Makers

The value proposition of making use of data to end up with better decisions is clear: “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” (Jim Barksdale, former president, and CEO of Netscape)

WHU's “Data Science for Decision Makers” program equips participants with the knowledge they need to successfully work with data scientists. The aim is not to train participants to become data scientists themselves, but rather to develop a level of data science understanding that reduces the communication barriers between decision makers and data scientists. You will learn how to make sense of data using machine learning tools and how to critically evaluate the merit of data science project proposals.

You will receive a WHU Executive Education Certificate after successfully completing the program.

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Publikationen & Forschung

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News & stories

Read our latest news –
Find out more about our Chair's activities.

Aviation experts showed high interest in research on capacity and demand management in European aviation

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The paper analyses the value of flexible flight-to-route assignments in the pre-tactical phase of Air Traffic Management

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The value of flexible flight-to-route assignments in pre-tactical air traffic management

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Ranking underscores WHU’s top position in supply chain management research

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Contact

Get in touch with us –
We look forward to hearing from you.

Professor Dr.
Arne Strauss

Chairholder

+49 (0)261 6509 775
arne.strauss(at)whu.edu

Johanna Häring

Personal Assistant

+49 (0)261 6509 777
johanna.haering(at)whu.edu

WHU – Otto Beisheim School of Management

Mercator Chair of Demand Management & Sustainable Transport
Hellenstraße 18; Entrance D'Esterstraße; Building X
56179 Vallendar