All Publications
The concept of autonomous combined transport utilizes autonomous vehicles for simultaneous passenger and goods transportation and is associated with a reduction in road traffic through bundling effects and economies of scale. Yet, its potential benefits particularly hinge on penetration and utilization rates. To date, few studies have explored user acceptance of systems that integrate passenger and freight flows. In this context, a research gap that is particularly evident pertains to the user acceptance of different concepts within autonomous combined transport. Extending the unified theory of acceptance and use of technology (UTAUT2), this paper examines the effects of six psychological constructs on the behavioral intention to use an autonomous combined transport system. Surveying 1040 respondents from Germany, two distinct combined transport concepts—scheduled and on-demand—were examined to identify key acceptance factors and operational peculiarities. Results from structural equation modelling show that the acceptance of autonomous combined transport systems depends on both, the operational concept as well as the purpose of use, with socio-demographic characteristics featuring different indirect effects per concept. Moreover, we find that performance expectancy, effort expectancy, price value, personal attitude, and trust are significant predictors of behavioral intention across both concepts, while the average order frequency of a potential user has a negative indirect impact on behavioral intention. Results show that an extended UTAUT2 model can conceptualize factors influencing autonomous combined transport acceptance, emphasizing the importance of investigating a user’s behavioral intention based on the specific operational concept.
Food supply systems are national critical infrastructures embedded in changing and uncertain environments. Hence, testing and evaluating them in their ability to meet food supply is key to reduce vulnerability to shortages. This paper presents an optimization approach to assess the resilience of nationwide food supply systems using the N-1 contingency criteria, which investigates whether the isolation of one region from the transport network destabilizes the food supply. To this end, we build a multi-regional multi-commodity large-scale model for food flow networks. Then, we implement a constraint optimization problem to find the management of food flows along the supply chain stages that minimize shortage, costs and penalties induced by the disruption for both the isolated and connected system. Lastly, resilience is quantified with established metrics. A numerical case study illustrates the proposed method, revealing which regions are critical to maintain the stability of the national food supply.
High-tech systems are typically produced in two stages: (1) production of components using specialized equipment and staff and (2) system assembly/integration. Component production capacity is subject to fluctuations, causing a high risk of shortages of at least one component, which results in costly delays. Companies hedge this risk by strategic investments in excess production capacity and in buffer inventories of components. To optimize these, it is crucial to characterize the relation between component shortage risk and capacity and inventory investments. We suppose that component production capacity and produce demand are normally distributed over finite time intervals, and we accordingly model the production system as a symmetric fork-join queueing network with N statistically identical queues with a common arrival process and independent service processes. Assuming a symmetric cost structure, we subsequently apply extreme value theory to gain analytic insights into this optimization problem. We derive several new results for this queueing network, notably that the scaled maximum of N steady-state queue lengths converges in distribution to a Gaussian random variable. These results translate into asymptotically optimal methods to dimension the system. Tests on a range of problems reveal that these methods typically work well for systems of moderate size.
We develop an inventory control policy for perishable products considering both random demand and random lead time. We consider a B2C retail environment where excess demand is lost. The policy dynamically determines the optimal replenishment quantity under a service level constraint in every period, allowing for order-crossing, a widely disregarded characteristic in the literature. Regarding perishability, we compare the two most extreme issuing policies, first-expired-first-out (FEFO) and last-expired-first-out (LEFO), and evaluate our policy to existing inventory policies for perishables that typically ignore lead time uncertainty.
We obtain several interesting findings. First, we show that ignoring lead time uncertainty and planning based on the expected lead time significantly undershoots the target service level. Even planning with the maximum lead time, under LEFO, the achieved service level would still fall considerably below the target, which the lost-sales structure can explain. On the other hand, under FEFO, the achieved service level would overshoot the target service level, which leads to unnecessary waste. Second, a more reliable lead time can significantly reduce waste, especially under LEFO. Third, our model allows us to distinguish between past, present, and future lead time uncertainty and thus to consider partial lead time information. We show the value of lead time information on outstanding orders. Fourth, we evaluate the impact of a fast but unreliable delivery option and a slow but reliable delivery option on the retailer’s average waste and ordering process. We find that the optimal choice depends on the demand characteristics.
Shifting the food system to a more sustainable one requires changes on both sides of the supply chain, with the consumer playing a key role. Therefore, understanding the factors that positively correlate with increased organic food sales over time for an entire population can help guide policymakers, industry, and research to increase this transition further. Using a statistical approach, we developed a spatial pooled cross-sectional model to analyze factors that positively correlate with an increased demand for organic food sales over 20 years (1999–2019) for an entire region (the city-state of Hamburg, Germany), accounting for spatial effects through the spatial error model, spatially lagged X model, and spatial Durbin error model. The results indicated that voting behavior strongly correlated with increased organic food sales over time. Specifically, areas with a higher number of residents that voted for a political party with a core focus on environmental issues, the Greens and the Left Party in Germany. However, there is a stronger connection with the more “radical” Left Party than with the “mainstream” Green Party, which may provide evidence for the attitude-behavior gap, as Left Party supporters are very convinced of their attitudes (pro-environment) and behavior thus follows. By including time and space, this analysis is the first to summarize developments over time for a metropolitan population while accounting for spatial effects and identifying areas for targeted marketing that need further motivation to increase organic food sales.
The logistician at a major humanitarian food aid organization (HFAO) is facing the decision whether to source globally or locally. The logistician is overseeing the distribution of a common fortified food product, a maize-soy blend (MSB), to a refugee camp in Africa. The main task of this case study is to evaluate the two potential sourcing options (globally versus locally), potentially by conducting a life cycle assessment of the two potential food supply chains. One possibility is to get the product locally, from the same country or region in Africa. The alternative would be to go global and receive the product from a supplier in Europe. The HFAO is concerned about their environmental impact. Advantages and disadvantages of local vs global procurement must be considered and carefully weighted, to identify which one is more sustainable. After all, the HFAO wants to champion sustainability, and add socioeconomic and environmental value to its aid operations.
Motivation and literature
In recent years, interest in intermodal rail transport has increased among academics. This is mainly triggered by the European Union's ambition to decarbonize transport. The ambitions are described in the white paper on transport. One of the main goals is to shift thirty per cent of long-distance road freight over 300 kilometers to rail or waterborne transport by 2030 and more than fifty per cent by 2050 (European Commission, 2011). Intermodal transport plays an important role in execution. Several case studies have been done on intermodal transport within a specific region (Frémont and Franc, 2010 and Arnold et al., 2004), but only one empirical study is available with a holistic overview of intermodal transport in Europe. Because the transport market is an open market with many continuous changes in transport flows, the study is not up-to-date anymore. In addition, NUTS1-level statistics are available from 2020 on intermodal rail transport (UIC, 2020). However, there is a lack of data at NUTS2 level and NUTS3 level for microanalysis. In this study, we update the study by de Langen (2017) and try to fill the gap at NUTS2 and NUTS3 levels.
Objective
The main objective of this research is to propose a market overview of all intermodal rail connections in Europe on NUTS2-level and NUTS3-level as of March 2023. The market overview and dataset can serve as a starting point to define new public statistics on multimodal transport and for research by other scholars.
Outline and structure
The first step in the market-analysis is to analyze the market structure focused on the possible roles within the intermodal transport environment. For this purpose, 10 to 15 interviews are conducted with various market players in the intermodal transport environment. Based on the structure and the interviews, market players can be identified to map the market. In the second step, it is first determined which information is used to create a database with all rail connections of intermodal transport. Subsequently, it is examined which sources are publicly available that meet the criteria. This determines which measurable characteristics there are to build a database. The sources are described and it is explained how the information can be used. The third step then explains how the data can be verified using public information from railway network operators in individual countries in Europe and using secondary sources. The results section provides a broad overview of the market structure and available connections. The results show characteristics of the entire market and a division into market segments. A geographical analysis om NUTS3 level and market shares of the identified market players by operator, rail carriers and terminal operators are provided. Subsequently, the results and limitations of the study are discussed. This leads to recommendations for the use of the data and for further research. Finally, the database with 1000+ datasets (intermodal connections), including characteristics, will be published in the appendix.
This study aims to provide a complete overview of intermodal rail connections in Germany and identify the market players involved in their operation. The lack of a comprehensive overview is attributed to the difficulty of summarizing empirical data of intermodal rail transport, combined with the many rapid changes in the dynamic open market. The study uses a dataset compiled through online research and interviews with market players. The identified market players include intermodal operators, railway carriers, terminals, and ports.
The case revolves around transport planning under difficult circumstances. The participants must schedule and coordinate the transport of a container from the South of Germany to the port of Hamburg. The transport can take place in two ways, with a total of four possible routes via direct road transport or intermodal rail transport from three different hinterland terminals. The transport routes are compared based on three criteria, (1) transport price, (2) emissions, and (3) reliability. The case contains a role play in which the participants learn the advantages and disadvantages of direct road transport compared to intermodal rail transport. For the planning process an online tool is provided. Four fixed routes are programmed into the tool. The routes consist of various transport parts with a fixed transport route, a fixed transport duration, and fixed emission values. The participants can use the tool to plan the transport by planning departure times, and planning buffers between the parts of the transport. The case is structured in such a way that all routes have advantages and disadvantages, so that it cannot be said that there is one correct answer. The case should stimulate discussion among participants.
In 2020, the world started a fight against a pandemic that has severely disrupted commercial and humanitarian supply chains. Humanitarian organizations (HOs), like the World Food Programme (WFP), adjusted their programs in order to manage this pandemic. One such program is cash and voucher assistance (CVA), which is used to bolster beneficiaries' freedom of choice regarding their consumption. In this vein, WFP supports local retailers to provide CVA to beneficiaries who do not have access to a functioning market. However, the operations of these stores can suffer from a very high transmission risk of COVID-19 unless preventive measures are put in place to reduce it. This paper discusses strategies that retailers and HOs can enact to maximize their service and dignity levels while minimizing transmission risk under a CVA program during a pandemic. We argue that HOs providing CVA programs can improve their assistance during a pandemic by implementing strategies that impact the retailing operations of their retailers.
Unlike consumer goods industry, a high-tech manufacturer (OEM) often amortizes new product development costs over multiple generations, where demand for each generation is based on advance orders (i.e., known demand) and additional uncertain demand. Also, due to economic regulatory reasons, high-tech OEMs usually source from a single supplier. Relative to the high retail price, the costs for a supplier of producing high-tech components are low. Consequently, incentives are misaligned: the OEM faces relatively high under-stock costs and the supplier faces high over-stock costs.
In this paper, we examine supply contracts that are intended to align the incentives between a high-tech OEM and a supplier so that the supplier will invest adequate and yet non-verifiable capacity to meet the OEM’s demand. When focusing on a single generation, the manufacturer can coordinate a decentralized supply chain and extract all surplus by augmenting a traditional wholesale price contract with a “contingent penalty” should the supplier fail to fulfill the OEM’s demand. When the resulting penalty is too high to be enforceable, we consider a new class of “contingent renewal” wholesale price contracts with a stipulation: the OEM will renew the contract with the incumbent supplier for the next generation only when the supplier can fulfill the demand for the current generation. By using non-renewal as an implicit penalty, we show that the contingent renewal contract can coordinate the supply chain. While the OEM can capture the bulk of the supply chain profit, this innovative contract cannot enable the OEM to extract the entire surplus.
A high-tech manufacturer often produces products that consist of many modules. These modules are either sourced from one of its suppliers or produced in-house. In this paper, we study the common case of an assembly system in which one module is sourced from a supplier with a fixed lead-time, while the other module is produced by the manufacturer itself in a make-to-order production system. Since unavailability of one of the modules has costly consequences for the production of the end-product, it is important to coordinate between the ordering policy for one module and the production of the other. We propose an order policy for the lead-time module with base-stock levels depending on the number of outstanding orders in the production system of the in-house produced module. We prove monotonicity properties of this policy and show optimality. Furthermore, we conduct a computational experiment to evaluate how the costs of this policy compare to those of a policy with fixed base-stock levels and show that average savings of up to 17% are attained.
To address climate change, transport policy tries to accelerate the electrification of vehicles. The impact of policy measures taken is difficult to predict especially in areas like commercial passenger transport where few research exists. In this study we estimate the impact of higher availability of charging infrastructure on the electrification potential of vehicles used in commercial passenger transport. For Hamburg we estimate that the electrification potential could increase by about 35%. We base this analysis on a company survey on vehicle usage patterns in commercial passenger transport and a ranked choice model to quantify the relationship between company sectors and tour patterns. This enables us to estimate the impact on electrification potential for overall Hamburg. The methodological contribution of this paper is to demonstrate a statistically viable approach to extrapolate insights from a behavioural survey to an overall region in the context of commercial passenger transport.
Freight transport and logistics are the backbone for business and society. This becomes especially obvious in times of crisis such as the Corona crisis or the war in Ukraine. While necessary, applied research in this area is particularly difficult since data access is limited, and heterogeneity of players and problems is high. These might be the reasons why less research has been conducted compared to neighboring fields such as passenger transport. This Themed Volume has brought together multiple new contributions in the field of freight transport and logistics. The Volume is multidisciplinary with articles using different methodologies including empirical work, statistical analysis, simulation, or optimization.
Many articles originate from contributions to the 2019 European Transport Conference (ETC). After being selected they went through a thorough review process together with other papers submitted to this Themed Volume. The review process for the Themed Volume took place over the period 2020 to 2022.
The papers cover multiple domains and include almost all modes of transport. Especially remarkable are four papers in the domain of rail freight, an area in which there is a dearth of papers. A number of articles cover two of the current big topics: new technologies and sustainability. This Themed Volume also includes contributions in “classical” topics of freight transport and logistics namely transport planning, carrier and location choice.
The Themed Volume comprises 16 articles. Nine of the articles take a mcro-perspective, looking at problems and analysis at organisational level. The other seven articles take a macro-perspective, detailing transport system observations of relevance for policy decisions.
Discussions on sustainability in freight transportation often result in the claim that more goods should be transported via rail instead of by truck. The introduction of the CO2 emission tax from 2021 onward in Germany might positively influence the economic competitiveness of the railway sector. Complete trains (direct trains from point to point) were already rather efficient and attractive before. However, single wagon networks struggle and constantly lose market share. For mixed load cargo (shipments with one to several pallets) the single wagon network is not used at all anymore. One initiative, so-called 'Railports', aim to make the latter more efficient and attractive to the market and therefore maybe help to accelerate the modal shift. In this case students should gain insights into the complexity of investment decisions in an environment of a natural monopoly, a publicly sponsored, regulated company and the threat of regulated access to properties/infrastructure with high initial investment costs. Students should identify and discuss the possibly opposing positions of society and the investor as well as the point of view of competitors. A fictive hearing in the form of a role-play should improve the students’ negotiation skills and their argumentation skills from different perspectives.
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.
Freight Transport Modeling in Emerging Countries examines freight transport models developed in emerging countries including Turkey, South Africa, India, Chile, and more. It provides a toolbox of successful freight transport model applications, alternative data collection methods, and evaluation techniques for the development of future policies. The book offers solutions for issues related to the urban, national, and international transportation of goods and examines new advances in freight transport models and data collection techniques and their applications in emerging countries.
Emerging countries have unique transport-related policies, regulatory structures, logistics systems, and long-term uncertainties that hinder their economic development. This book tackles these issues by examining decision-making models for locating logistics sites such as ports and distribution centers, modeling urban freight movements in megacities and port cities, using existing datasets to get information when data is not available, implementing policies related to the national and international movements of goods, and more.
Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from farm to fork, however, these methodologies have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in the area adjacent to their home location. This paper aims to fill this gap by introducing a gravity-based approach to model food-flows from supermarkets to consumers and demonstrating how models of consumer shopping behavior can be used to improve computational methodologies to infer the source of an outbreak of foodborne disease. To demonstrate our approach, we develop and calibrate a gravity model of German retail shopping behavior at the postal-code level. Modeling results show that on average about 70 percent of all groceries are sourced from non-home zip codes. The value of considering shopping behavior in computational approaches for inferring the source of an outbreak is illustrated through an application example to identify a retail brand source of an outbreak. We demonstrate a significant increase in the accuracy of a network-theoretic source estimator for the outbreak source when the gravity model is included in the food supply network compared with the baseline case when contaminated individuals are assumed to shop only in their home location. Our approach illustrates how gravity models can enrich computational inference models for identifying the source (retail brand, food item, location) of an outbreak of foodborne disease. More broadly, results show how gravity models can contribute to computational approaches to model consumer shopping interactions relating to retail food environments, nutrition, and public health.
Im SMECS-Projekt wurde durch Anwendung von KI-Verfahren des Maschinellen Lernens ein IT-System entwickelt, welches dynamisch die Ankunftszeit (ETA) von Containertransporten im intermodalen Vorlauf der maritimen Transportkette prognostiziert. Das System erkennt proaktiv Konflikte bei der Einhaltung des geplanten Transportverlaufes vom Warenversender bis zum Seehafen und befähigt die Anwender zu einer zielgerichteten und effizienten Durchführung geeigneter Störungsmaßnahmen durch die Vorgabe von akteursspezifischen Handlungsempfehlungen.
This paper presents a calibrated dynamic multi-scale multi-regional input–output (MSMRIO) model of the German food supply system based on real data. The model comprises 51 commodity groups from farm to fork differentiating three different temperature ranges as well as living animals. Spatially, it works on an aggregate level of 402 regions within Germany as well as its 50 most important trading nations. It determines the commodity flows and the additionally needed transport capacity in case of disruptions. Showing how changes in production, inventories, sourcing, and consumption affect commodity flows, the model uncovers vulnerabilities and makes risk evaluation possible.
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).
(Article ID 20180624) 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.
