Selected Publications
Balster, Andreas, Ole Hansen, Hanno Friedrich and André Ludwig (2020): An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning, Business & Information Systems Engineering.
Abstract: Transparency in transport processes is becoming increasingly important for transport companies to improve internal processes and to be able to compete for customers. One important element to increase transparency is reliable, up-to-date and accurate arrival time prediction, commonly referred to as estimated time of arrival (ETA). ETAs are not easy to determine, especially for intermodal freight transports, in which freight is transported in an intermodal container, using multiple modes of transportation. This computational study describes the structure of an ETA prediction model for intermodal freight transport networks (IFTN), in which schedule-based and non-schedule-based transports are combined, based on machine learning (ML). For each leg of the intermodal freight transport, an individual ML prediction model is developed and trained using the corresponding historical transport data and external data. The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail. Consequently, the outcome of this research allows decision makers to proactively communicate disruption effects to actors along the intermodal transportation chain. These actors can then initiate measures to counteract potential critical delays at subsequent stages of transport. This approach leads to increased process efficiency for all actors in the realization of complex transport operations and thus has a positive effect on the resilience and profitability of IFTNs.
Horn, Abigail L. and Hanno Friedrich (2019): Locating the source of large-scale outbreaks of foodborne disease, Journal of The Royal Society Interface, 16 (151).
Abstract: In today’s globally interconnected food system, outbreaks of foodborne disease can spread widely and cause considerable impact on public health. We study the problem of identifying the source of emerging large-scale outbreaks of foodborne disease; a crucial step in mitigating their proliferation. To solve the source identification problem, we formulate a probabilistic model of the contamination diffusion process as a random walk on a network and derive the maximum-likelihood estimator for the source location. By modelling the transmission process as a random walk, we are able to develop a novel, computationally tractable solution that accounts for all possible paths of travel through the network. This is in contrast to existing approaches to network source identification, which assume that the contamination travels along either the shortest or highest probability paths. We demonstrate the benefits of the multiple-paths approach through application to different network topologies, including stylized models of food supply network structure and real data from the 2011 Shiga toxin-producing Escherichia coli outbreak in Germany. We show significant improvements in accuracy and reliability compared with the relevant state-of-the-art approach to source identification. Beyond foodborne disease, these methods should find application in identifying the source of spread in network-based diffusion processes more generally, including in networks not well approximated by tree-like structure.
Joseph, Sarah, Irene Peters and Hanno Friedrich (2019): Can Regional Organic Agriculture Feed the Regional Community? A Case Study for Hamburg and North Germany, Ecological Economics, 164 (106342).
Abstract: We compute degrees of food self-sufficiency for regions in North Germany with the city state of Hamburg at the centre, given different diets (the German average diet versus increasing substitution of legumes for meat) and production methods (conventional versus organic). Triangulating data of statistical databases, literature, and our own collection, we compute land footprints per capita and multiply by regional populations. Our findings indicate that there is great potential to feed the regional community surrounding Hamburg solely with regionally, organically grown foods, but this result depends on (1) composition of diets — specifically, the per capita meat consumption – and (2) agricultural area available in the defined region. On the basis of simplifying assumptions, the computation indicates an approximation of what is possible.
Ottemöller, Ole and Hanno Friedrich (2019): Modelling change in supply-chain-structures and its effect on freight transport demand, Transportation Research Part E: Logistics and Transportation Review, 121: 23-42.
Abstract: The paper introduces a model to determine possible impacts of changes in supply chain structures on freight transport demand. Examples are centralisation or vertical (des)integration within supply chains. The model first generates a population of establishments and commodity flows in space which is then manipulated according to different scenarios. It uses methods from transport planning and optimisation as well as scenario technique. To demonstrate its applicability a centralisation in food supply chain structures in Germany is analysed. The results show that a more educated discussion is needed for such changes since the range of possible impacts is large.
Hansen, Ole, Hanno Friedrich and Sandra Transchel (2019): An inventory management approximation for estimating aggregated regional food stock levels, International Journal of Production Research, 175 (1): 1-17.
Abstract: Food is an important resource in disaster management, and food stock levels hold significance for disaster mitigation research and practice. The presence or absence of food stocks is a vulnerability indicator of a region. A large part of overall food stock, before a disaster strikes, is held by private companies (retailers, wholesalers and food producers). However, there is little-to-no information on the food stock levels of commercial companies, and no approach exists to derive such information. We develop an approximation model based on essential inventory management principles and available data sources to estimate aggregated food stock levels in supply networks. The model is applied in a case example that features dairy product stock levels in the German state of Saxonia. The resulting overall stock levels are normalised, and their usability is showcased in a simple vulnerability analysis. Disaster managers are provided with a model that can be used estimate otherwise unavailable data and facilitates investigations into the regional resilience of an area. The limitations of our study are based on the aggregated nature of the supply network structure and data usage (i.e. in the model, we do not consider any seasonality or trend effects).