Mercator Endowed Chair of Demand Management & Sustainable Transport


Special Issue on Data-driven Demand and Supply Management for Online-to-Offline Logistic Services

Special Issue on Data-driven Demand and Supply
Management for Online-to-Offline Logistic Services
Special Issue Editors:
Jan Fabian Ehmke, Robert Klein,
Claudius Steinhardt, Arne Strauss
Submission deadline: March 31th, 2022

Over the last decade, many new online-to-offline logistics services have emerged that have generated significant interest in the research community and the public. These services include, for example, attended home delivery, bike sharing, crowd shipping, same-day delivery, and ride hailing. Today, services like Instacart, Amazon Flex / PrimeNow, Share Now, and Uber are commonplace and represent an essential part of the modern on-demand lifestyle.
The services under consideration are often characterized by the term “online-to-offline”, as online platforms provide the necessary interface to book a service such as delivery, acquire a delivery person, or match a passenger and a driver. In contrast, the actual service provision takes place offline, usually by operating a number of vehicles. They can only be profitable and sustainable if service providers effectively manage supply and demand.
From a methodological point of view, managing such services is both exciting and challenging, as it usually requires taking into account the behavior of actors such as customers and drivers within sequential and stochastic decision-making processes. Consider, for example, the case of attended home delivery. Here, providers have to decide on fulfillment options for incoming requests without knowing exactly the number of future customers, their preferences, and their locations.
The special issue collects high-quality, peer-reviewed papers that address the research front in onlineto- offline logistics, both from a theoretical and an application perspective. The focus is on the development and application of data-driven OR methods that ideally integrate approaches from the fields of predictive and prescriptive analytics, potentially including machine learning and artificial intelligence. We are particularly interested in approaches that actively consider the behavior of the actors involved.
Specific applications of interest include, but are not limited to:
-Last-mile logistics & e-fulfillment (Attended home delivery, same-day delivery, crowd shipping, etc.)
-Mobility-on-demand (Bike sharing, car sharing, ride hailing, ride pooling, etc.)
-Field operations & personal care (Personal shopper services, technician and maintenance
scheduling, home health services, etc.)

Methods of interest include, but are not limited to:
-Choice-based optimization
-Approximate dynamic programming
-Reinforcement Learning
-Machine Learning
-Mathematical Programming
-Stochastic Optimization
Submission Guidelines and Review Process:
Papers must be submitted at under the category “data-driven Demand and Supply Management for Online-to-Offline Logistic Services” by March 31, 2022.
All papers submitted to this special issue should report original work and contribute to the journal OR Spectrum by using a quantitative research paradigm and OR methods. According to the aims of OR Spectrum, high quality papers are sought that match the scope of the journal, demonstrate rigor in the application of state-of-the-art OR techniques, and promise to impact the future work of the OR community.
Papers will be screened by the Editor-in-Chief and one Special Issue Editor. If the paper is deemed to be of sufficient quality, it will be peer-reviewed according to the standards of OR Spectrum by at least two experienced reviewers. We will adopt a rapid and fair review process, striving to provide reviews within three months of submission. Accepted papers will be available online prior to publication of the special issue.

Special Issue Editors:
Jan Fabian Ehmke, University of Wien
Robert Klein, University of Augsburg
Claudius Steinhardt, Bundeswehr University Munich (UniBW M)
Arne Strauss, WHU – Otto Beisheim School of Management