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!
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!
Team
Our team
Interested in joining the team as external doctoral candidate? Click here!
Teaching
Our Teaching –
Courses offered in 2021/22
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:
- Introduction to the CRISP-DM process (business understanding)
- Sampling and Partitioning (data preparation)
- Information selection, modelling and overfitting (modelling)
- Model evaluation
- Evidence combination (Naïve Bayes, association mining) and visualization
- 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:
- Introduction, customer valuation game
- Demand modelling (parametric, non-parametric models, unconstraining)
- Constrained price optimization, capacity control, network revenue management
- Dynamic price control, (approximate) dynamic programming
- Markdown pricing, behavioural pricing
- 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:
- Introduction to the CRISP-DM process (business understanding)
- Sampling and Partitioning (data preparation)
- Information selection, modelling and overfitting (modelling)
- Model evaluation
- Evidence combination (Naïve Bayes, association mining) and visualization
- Visualization, dashboards, selling your project to end users
- 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.
Publikationen & Forschung
Erfahren Sie mehr über unsere Forschung und Journal Artikeln
Supply Chain Management Group
Dynamic pricing of flexible time slots for attended home delivery
Strauss, A., Gülpinar, N., Zheng, Y. (2021), European Journal of Operational Research: EJOR, Vol. 294 (3), pp. 1022-1041
Dynamic pricing of flexible time slots for attended home delivery
Strauss, A., Gülpinar, N., Zheng, Y. (2021), European Journal of Operational Research: EJOR, Vol. 294 (3), pp. 1022-1041
Supply Chain Management Group
Home healthcare routing and scheduling of multiple nurses in a dynamic environment
Demirbilek, M., Branke, J., Strauss, A. (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), Flexible Services and Manufacturing Journal, Vol. 33 (1), pp. 253–280
Supply Chain Management Group
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), 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), Transportation Science, Vol. 54 (4), pp. 882-896
Supply Chain Management Group
A review of revenue management: recent generalizations and advances in industry applications
Klein, R., Koch, S., Steinhardt, C., Strauss, A. (2020), European Journal of Operational Research: EJOR, Vol. 284 (2), pp. 397-412
A review of revenue management: recent generalizations and advances in industry applications
Klein, R., Koch, S., Steinhardt, C., Strauss, A. (2020), European Journal of Operational Research: EJOR, Vol. 284 (2), pp. 397-412
Supply Chain Management Group
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), 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), Journal of Air Transport Management, Vol. 75, pp. 139-152
Supply Chain Management Group
Dynamically accepting and scheduling patients for home healthcare
Strauss, A., Demirbilek, M., Branke, J. (2019), Health Care Management Science: HCMS, Vol. 22 (1), pp. 140–155
Dynamically accepting and scheduling patients for home healthcare
Strauss, A., Demirbilek, M., Branke, J. (2019), Health Care Management Science: HCMS, Vol. 22 (1), pp. 140–155
Supply Chain Management Group
Unconstraining methods for revenue management systems under small demand
Kourentzes, N., Li, D., 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), Journal of Revenue and Pricing Management, Vol. 18 (1), pp. 27-48
Supply Chain Management Group
A review of choice-based revenue management: theory and methods
Strauss, A., Klein, R., Steinhardt, C. (2018), European Journal of Operational Research: EJOR, Vol. 271 (2), pp. 375-387
A review of choice-based revenue management: theory and methods
Strauss, A., Klein, R., Steinhardt, C. (2018), European Journal of Operational Research: EJOR, Vol. 271 (2), pp. 375-387
Supply Chain Management Group
Future research directions in demand management
Currie, C. S. M., Dokka, T., Harvey, J., Strauss, A. (2018), Journal of Revenue and Pricing Management, Vol. 17 (6), pp. 459–462
Future research directions in demand management
Currie, C. S. M., Dokka, T., Harvey, J., Strauss, A. (2018), Journal of Revenue and Pricing Management, Vol. 17 (6), pp. 459–462
Supply Chain Management Group
Revenue management in the digital economy
Strauss, A., Aydin, N. (2018), Impact, Vol. 2018 (2), pp. 39-42
Revenue management in the digital economy
Strauss, A., Aydin, N. (2018), Impact, Vol. 2018 (2), pp. 39-42
Supply Chain Management Group
Air traffic flow management slot allocation to minimize propagated delay
Ivanov, N., Netjasov, F., Jovanovic, R., Starita, S., Strauss, A. (2017), Transportation Research Part A: Policy and Practice, Vol. 95, pp. 183-197
Air traffic flow management slot allocation to minimize propagated delay
Ivanov, N., Netjasov, F., Jovanovic, R., Starita, S., Strauss, A. (2017), Transportation Research Part A: Policy and Practice, Vol. 95, pp. 183-197
Supply Chain Management Group
An approximate dynamic programming approach to attended home delivery management
Yang, X., Strauss, A. (2017), European Journal of Operational Research: EJOR, Vol. 263 (3), pp. 935-945
An approximate dynamic programming approach to attended home delivery management
Yang, X., Strauss, A. (2017), European Journal of Operational Research: EJOR, Vol. 263 (3), pp. 935-945
Supply Chain Management Group
Tractable consideration set structures for assortment optimization and network revenue management
Strauss, A., Talluri, K. (2017), Production and Operations Management: POMS, Vol. 26 (7), pp. 1359-1368
Tractable consideration set structures for assortment optimization and network revenue management
Strauss, A., Talluri, K. (2017), Production and Operations Management: POMS, Vol. 26 (7), pp. 1359-1368
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CADENZA project
Research project –
CADENZA has started
The CADENZA project has been successfully launched on June 1, 2020 under the direction of Prof. Dr. Arne Strauss.
The CADENZA project, short for "Advanced Capacity and Demand Management for European Network Performance Optimization", aims to develop a detailed trajectory management concept for the European flight network.
With a share of approximately 360,000 euros of the total project costs of 2 million euros, the Mercator Chair of Prof. Dr. Arne Strauss at WHU will lead the development of innovative methodological approaches to the emerging issues as well as scalable optimization approaches. The project runs until December 2022.
In addition to WHU - Otto Beisheim School of Management, the University of Belgrade, the University of Worms and the Universitat Politècnica de Catalunya as well as Eurocontrol are participating in the project.
News & stories
Read our latest news –
Find out more about our Chair's activities.
Jan-Rasmus Künnen and Arne Strauss talk about -The value of flexible flight-to-route assignments in pre-tactical air traffic management
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Contact
Get in touch with us –
We look forward to hearing from you.
WHU – Otto Beisheim School of Management
Mercator Chair of Demand Management & Sustainable Transport
Hellenstraße 18; Entrance D'Esterstraße; Building X
56179 Vallendar