Doctoral and Postdoctoral Projects

Considering Complex Customer Preferences in Multimodal Travel Itineraries
Easier access for travelers to a variety of individual mobility services via mobility applications and the integration of innovative means of transport have led to an increase in multimodal travel behavior in recent years. In this context, the combination of different transportation services within a certain period of time (mostly one week), or in particular within one trip, is referred to as multimodal mobility. In this research project, optimization approaches are developed which take complex individual customer preferences into account when orchestrating travel itineraries. In particular, the identification of a diversified and adequate amount of different travel itineraries under consideration of individual customer preferences will be discussed. Contact person: Thomas Horstmannshoff
Dynamic Fleet Management in Ride-Sharing Systems Worldwide increasing congestion in urban traffic networks and the associated air pollution have led to a growing interest in innovative shared mobility solutions. Among these are on-demand ride-sharing systems, which promise to improve efficiency by bundling travelers on the way from their origin to their destination. This allows for lower fares compared to individual taxi services and enables a more convenient travel experience compared to the traditional local public transport through smaller transport cabins and direct trips. Within this research project, various aspects of dynamic fleet management are investigated, crucial for a successful ride-sharing system. The aim is to gain a comprehensive understanding of the planning problems involved, as well as to develop high-performance solution approaches that meet real-world requirements. Contact person: Jarmo Haferkamp
Opportunities for Machine Learning in Urban Logistics There has been a paradigm-shift in urban logistic services in the last years; global interconnectedness, urbanization, ubiquitous information streams, and increased service-orientation raise the need for anticipatory real-time decision making. A striking example are logistic service providers: Service promises, like same-day or restaurant meal delivery, dial-a-ride, and emergency repair, force logistic service providers to anticipate future demand, adjust to real-time traffic information, or even incorporate unknown crowdsourced drivers to fulfill customer expectations. Data-driven, anticipatory approaches are required to overcome the challenges of such services. They promise to improve customer satisfaction through accurate predictions (e.g., via supervised learning), enhanced fleet control (e.g., via reinforcement learning), and identification of demand patterns and delivery scenarios (e.g., via unsupervised learning). Within this research project, we combine recent advances in machine learning with established methods from operations research to tackle present-day challenges in urban logistics. Contact person: Florentin Hildebrandt
Stochastic Optimisation of Urban Delivery Systems with Micro Hubs To compete with e-commerce giants such as Amazon, many local businesses start to offer fast same-day delivery, often within a few hours after an order was placed. Deliveries are conducted by local delivery fleets. However, the narrow delivery times and the geographical spread of pickup and delivery locations result in a lack of consolidation opportunities. This can be remedied by so-called micro hubs, which can serve as transhipment centres for parcels in urban delivery. Drivers can store parcels from adjacent shops for redistribution. They also can pick up parcels from different shops for joint delivery to customers in the same region. Thus, micro hubs can increase consolidation opportunities and may also enable the use of smaller, green and clean vehicles for first and last mile delivery. Within this project, optimisation models incorporating consolidation centres in the pickup and delivery system of urban same day delivery are developed. Further, different solution approaches will be investigated to cope for the uncertainty in demand at time of planning. Contact person: Charlotte Ackva
Stochastic Dynamic Workforce Scheduling for Urban Home Services Home services in urban areas play a vital role for business operations in sectors like home health care, parcel delivery, maintenance activities and more. Among these are specialized service providers that deploy their workforce to customers’ homes, addressing a diverse range of customer needs such as equipment installation, repairs and on-site maintenance. Service providers often face challenges due to a lack of crucial information, such as the exact point in time when customers will request services, their geographical location, or the complexity of each service request. These uncertainties can result in customer revisits (rework) due to failed or incomplete services, unpredictable service times or delayed customer support. From an operational perspective, the trade-off between two key challenges mainly affects the performance: determining sophisticated worker-customer combinations to ensure high-quality services (assignment) and devising efficient customer sequences for each worker to finalize tours (routing). Within this research project, we establish different solution methods for workforce scheduling problems that incorporate stochastic and dynamic elements. By means of combinatorial optimization methods, anticipatory and learning techniques, we aim to integrate multiple sources of stochasticity and future dynamics in the present decision-making progress. Contact person: Jonas Stein
Service Quality, Consistency, and Equity in Stochastic and Dynamic Distribution Networks Description: In our contemporary era, the role of logistics, which deals with the flow of goods, information, and services, as one of the pillars of the modern and globally interconnected supply chain has become more and more important. In a logistics system, transportation is one of the decision areas which has a huge impact on the overall performance of the system. The rapid increase in urbanization and technological advancement has made the management of transportation systems more complex. Decisions such as route planning have become multifaceted. The amount of cost saved or profit made has been the traditional metric for evaluating the performance of a transportation system. While this is still true, there are other factors that are intangible but have an impact on costs or profits and consequently, on the performance and sustainability of the system in the medium- or long-term. Such metrics include service quality, consistency, and, equity. In the context of single- and multi-echelon distribution networks of goods and agricultural produce, this project looks into the decision-making processes of pickup and delivery problems that take into account these three metrics in presence of uncertainty and dynamism in the availability of data. Solution policies are suggested as a combination of stochastic programming/modeling techniques and combinatorial optimization methods. Contact person: Dr. Shohre Zehtabian
Integration of Preferences in Decision-Making In today's dynamic business environment, companies are increasingly pressured to stand out not just in terms of profit margins but also through innovative workplace strategies. Consequently, optimizing operational processes while accommodating diverse preferences of employees and customers has become increasingly crucial. Recognizing and addressing employee preferences, such as flexible working hours and tasks tailored to their skill level and abilities, not only enhances job satisfaction and productivity but also fosters a more harmonious work environment. Similarly, considering customer preferences, such as service within desired time windows, can significantly enhance service quality and overall satisfaction levels. Meeting these objectives requires sophisticated planning and decision-support tools. This project explores innovative solutions to identify and integrate diverse preferences into workforce and route planning. Specifically, we will investigate the influence of incorporating employee preferences spanning i.e., task types, work areas, and equitable workload distribution, ensuring optimal resource allocation. These approaches will be explored across various sectors, including last-mile delivery and the complex field of home health care routing and scheduling. Contact person: Caroline Ihloff
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