Research

Due to increasing population in metropolitan areas, intelligent planning and control of urban mobility and freight transportation is becoming more important. Traditionally, research in the area of city logistics has investigated the trade-off between efficiency and reliability of urban deliveries. Extending well-known city logistics approaches, the Research Group Business Analytics focuses on the preparation and aggregation of large databases for dynamic and stochastic methods of transportation planning and control, e.g., optimal path-finding and vehicle routing. We follow a holistic approach stretching from data collection via data aggregation with data mining to extended approaches of applied operations research. We are also working on better planning and control of mobility and transportation services such as car and bike sharing services.

Anticipative Management of Dynamic Transportation Services

• Investigated by: Jarmo Haferkamp, Marlin Ulmer, Jan Fabian Ehmke

Dynamic ride-sharing services operate large fleets of vehicles to fulfill incoming trip requests on demand. Due to the short-term nature of requests, relocation of idle vehicles is crucial for their success. The objective for relocation policies is to offset unbalanced distributed trip origins and destinations to avoid demand surpluses, i.e. the rejection of requests. Along with potential demand surpluses, relocation decisions have to consider further decision criteria such as travel distances or other relocations. Our aim is to learn – on a tactical level – how to weight these decision criteria, while at the operational level deciding on the exploitation of myopic or anticipatory information to achieve a balanced and robust ride-sharing system.

Customer Acceptance in Attended Home Deliveries

• Investigated by: Charlotte Köhler, Ann Melissa Campbell, Catherine CleophasJan Fabian Ehmke

Home delivery services require the attendance of the customer during delivery. Hence, retailers and customers mutually agree on a delivery time window in the booking process. However, when a customer requests a time window, it is not clear how much accepting the ongoing request significantly reduces the availability of time windows for future customers. We explore using historical order data to manage scarce delivery capacities efficiently and flexibly. 

Recent publications

Flexible time window management for attended home deliveries

Also see

efulfillmentresearch.org

Collaborative Transportation for Attended Home Deliveries

• Investigated by: Steffen Elting, Jan Ehmke, Margaretha Gansterer

Increasing congestion and concerns about harmful emissions in urban areas may soon motivate politics to install a collaboration platform for freight carriers to reduce the amount of delivery vehicles within cities. Moreover, by exchanging time window-based customer requests, carriers can gain additional profit. Combining methods of Operations Research and Game Theory, this project will investigate the interaction of dynamic customer acceptance and horizontal, auction-based carrier collaboration. The research goals are (1) to quantify the effect of dynamism in an auction-based horizontal carrier collaboration, (2) to determine an optimal methodological combination of delivery time slot management on the one hand and a combinatorial auction mechanism on the other hand, and finally (3) to investigate potential for strategic carrier behavior in both of these approaches.

Complex Customer Preferences in Multimodal Travel Itineraries

• Investigated by: Thomas Horstmannshoff, Jan Fabian Ehmke

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 traveler 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 traveler preferences will be investigated.

Recent publications

• Creation of Individualized Sets of Multimodal Travel Itineraries

Integrated Routing and Packing

• Investigated by: Corinna Krebs, Jan Fabian Ehmke

Given automated order systems, detailed characteristics of items and vehicles enable the detailed planning of deliveries including more efficient and safer loading of distribution vehicles. Many vehicle routing approaches ignore the corresponding complex loading constraints. We focus on the comprehensive evaluation of loading constraints in the context of combined Capacitated Vehicle Routing Problem and 3D Loading (3L-CVRP) and its extension with time windows (3L-VRPTW).

Recent publications

Axle Weights in combined Vehicle Routing and Container Loading Problems

Machine-Learning Based Estimation of Fuel Consumption in Green Logistics

• Investigated by: Johann Hillmann, Jan Fabian Ehmke

As of now, the transport of goods via on-road vehicles contributes a significant share to the overall emissions of CO2 in the EU. Accordingly, significant efforts have been undertaken in recent years to incorporate detailed emissions models into traffic optimization objective functions, such as vehicle routing problems. The quality of available emissions models with regard to estimating emissions precisely and planning environmentally-friendly routes, often prove to be too imprecise and static for the task, creating a need for new, enhanced models. We follow a data-driven approach and develop a more precise, robust, and generalizable emissions model by incorporating publicly available fleet-based fuel consumption data together with machine-learning techniques. This information is not only relevant for planning of logistics service providers, but also for environmentally conscious customers. Because of that, the ability to plan deliveries sustainably and transparently may provide an important service element to companies to secure competitiveness. How sustainable delivery options are best communicated to the customer and whether this improves the efficiency of planning will also be explored in this project.

Potentials of Automated Transport Systems

• Investigated by: Rico Kötschau, Jarmo Haferkamp, Jan Fabian Ehmke

The technical progress on self-driving and connected vehicles is expected to lead to a significant change in the realization and organization of passenger and freight transportation. Aims of the project are: (1) to derive requirements for self-driving vehicles, (2) to estimate and evaluate their impact on transportation systems as well as environment and safety, (3) and to develop visions of future behavioral and organizational forms caused by self-driving vehicles.

A key aspect in achieving these aims is the development and investigation of solution approaches for the strategic and operational planning problems of self-driving and connected car based services.

Recent publications

• Combining Simulation and Optimisation to Design Reliable Transportation Services with Autonomous Fleets
• Effectiveness of demand and fulfillment control in dynamic fleet management of ride-sharing systems

Dynamic Vehicle Routing with Stochastic Customer Requests

• Investigated by Ninja Söffker, Marlin Ulmer, Dirk Mattfeld 

Many service providers offer customers to serve their request on the same day. In the case of a pickup or service request, the new customer can be integrated in the currently conducted vehicle routes. The service provider therefore needs to decide both about the acceptance or rejection of the new request as well as about the modification of the current routes while aiming on maximizing the number of customers or profit generated over each day of service. Our aim is to learn how to make these decisions in anticipation of potential future customer requests. To this end, we investigate an approximate dynamic programming approach with an adaptive state space partitioning.

Recent publications

• Adaptive State Space Partitioning for Dynamic Decision Processes 

• Stochastic Dynamic Vehicle Routing in the Light of Prescriptive Analytics: A Review