Jan Fabian Ehmke

The COVID 19 pandemic highlighted the importance and difficulty of allocating scarce containment resources based on accurate predictions of disease spread. While extensive research has focused on descriptive and predictive analytics to support decision making, much less attention has been given to prescriptive analytics that connects pandemic forecasts with resource allocation decisions. This review examines existing analytics research and identifies future opportunities for more integrated approaches to pandemic resource management.

Railways are among the most environmentally friendly means of transport, making circulation planning essential for cost-effective and reliable operations. This task involves assigning and sequencing scheduled trips and empty runs for railway vehicles based on given timetables. In the literature, circulation planning problems are represented by several networks, formulated using different models, solved by various methods, and aimed at different objectives. Often, researchers study practical problems from specific partners with a strong focus on the use case. Considering these aspects, this tutorial explains and compares two widely used graph structures and the corresponding mathematical formulations for the fundamental rolling stock circulation planning problem with a homogeneous fleet (i.e., one single type of traction units). A detailed problem description is followed by a comprehensive guideline on how to build a connection-based and a space-time network. We use a real-world case study from Austria and provide 16 instances of different sizes and structures for the basic problem variant online. We assess networks and models in terms of complexity, efficiency, and suitability for extensions. Additionally, we provide managerial insights by evaluating real-world solutions and the trade-off between minimizing traction units and costs from repositioning. This paper contributes to railway fleet management, offering valuable guidance for both theoretical advancements and practical implementations in the field.

This paper addresses the optimization of resource allocation and infrastructure planning in bike-sharing systems, particularly inspired by dynamic demand patterns as observed during the COVID-19 pandemic. We introduce a stochastic mathematical model that considers varying demand scenarios to enhance system performance and resource utilization. The research objectives are to fulfill the total travel demand across scenarios and compute the network's capacity to satisfy demand, thereby enhancing the system's efficiency and meeting users' diverse travel needs. The main contributions of this paper include presenting a stochastic mathematical model for bike-sharing station allocation and path network design, which optimizes resource allocation and infrastructure planning. Through a case study on the Vienna bike-sharing system, the model demonstrates practical applicability and effectiveness, offering insights for improving efficiency and service quality. The sensitivity analysis reveals that as costs for bicycle docks and station building increase, fulfilled demand decreases, emphasizing the crucial role of cost management in meeting demand efficiently.

Attended home deliveries (AHDs) are characterized by dynamic customer acceptance and narrow customer‐specific delivery time windows. Both impede efficient routing and thus make AHDs very costly. In this article, we explore how established horizontal collaborative transportation planning methods can be adapted to render AHDs more efficient. The general idea is to enable request reallocation between multiple collaborating carriers after the order capture phase. We use an established centralized reallocation framework that allows participating carriers to submit delivery requests for reallocation. We extend this framework for AHD specifics such as the dynamic arrival of customer requests and information about delivery time windows. Using realistic instances based on the city of Vienna, we quantify the collaboration savings by solving the underlying routing and reallocation problems. We show that narrow time windows can lower the savings obtainable by the reallocation by up to 15%. Therefore, we suggest enhancing the decision processes of request selection and request bundling using information about delivery time windows. Our findings demonstrate that adapting methods of request selection and bundle generation to environments with narrow time windows can increase collaboration savings by up to 25% and 35%, respectively in comparison to methods that work well only when no time windows are imposed.