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Item type:Publication, Access status: with files , The energy aggregator problem – a holistic mixed-integer linear programming approachIn this paper, a new mixed integer linear programming (MILP) model for the day-ahead operation of energy aggregators (EA) is developed. Synergies between the different types of flexibility and energy trading options enable EAs in decentralized and renewable energy systems to provide economic benefits to participating households but require a detailed consideration of technological properties and constraints of the respective types of resources. Therefore, the main contribution of this work is the development of a new EA model (EAM), which combines a holistic perspective with a high level of technical detail to better address the complexity of the EA decision. Most importantly, power-to-heat systems are integrated with their inherent thermal relations between heat pumps, heater rods and heat storages. In combination with other energy resources such as photovoltaic systems, electric vehicles, household battery storages and time-shiftable loads, households are modeled as systems with interdependent electrical power and heat flows. Moreover, three different trading levels (wholesale, local markets and internal trading) are taken into account. The model application to a case study with up to 111 individually modeled prosumer households in a summer and a winter scenario reveals high synergetic potential of EAs resulting from the flexibility of multiple trading options in combination with the flexibility of various energy resources. The results validate the efficacy of the model, as significant economic benefits for households are realized in comparison to a base case of non-aggregated households, showing that the three trading levels significantly contribute to these benefits. Further analyses give insights into the interdependent synergetic relations between different flexible resources, underlining the importance of a holistic optimization approach that explicitly takes these relations into account. For future research, the EAM is proposed as a base model to depict the behavior of EAs.Publicationtype: Journal Article40 32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Anticipating customer substitution: a data-driven, distance-based approach for out-of-stock product configurationsPurpose: When facing capacity bottlenecks, manufacturers of configurable, multi-variant products may adjust the product mix to uphold the scheduled output. However, maintaining market attractiveness by choosing the right product configurations as substitutes is a non-trivial task as it involves anticipating the substitution behaviour of customers. Substitution behaviour models currently used in quantitative production planning models for configurable products are either based on domain knowledge of experts, which makes them bias-prone, or they require extensive market research. The purpose of this study is to present a data-driven approach. Design/methodology/approach: Based on data science concepts, distance measures are applied to derive distances between different product configurations from historical order data. Different design options for such a distance measure are discussed regarding configurable products and tested with automotive industry data. Furthermore, the study shows ways to validate the distance results. Findings: The experiments show that the presented distance measure represents the expected customer substitution behaviour quite well. A context-sensitive distance measure including rank information of ordinal product features is most suitable for the automotive data sets. Originality/value: This study presents a new approach to model the substitution behaviour of customers. The attractiveness of a potential substitute is represented by a distance from the customer’s first-choice configuration. The presented distance measure provides an inexpensive tool using existing data instead of expensive market research. Thus, it supports the integration of substitution into quantitative production planning models that deal with a large variety of configurable products.Publicationtype: Journal Article28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A typology of the collaboration willingness of carriers in hinterland logisticsPurpose: A large part of maritime container supply chain costs is generated by carriers in port hinterland logistics. Carriers which operate in the hinterland are under pressure to reduce costs and increase profitability, and they face challenges of fierce price competition. This study aims to explore how collaboration is perceived and implemented by carriers in truck container logistics in the port hinterland as a way to tackle these issues. Design/methodology/approach: This study adopts a qualitative multiple case study approach. Qualitative interviews with carriers in the port hinterland of Hamburg, Germany, were conducted and analyzed using grounded theory. Findings: The study reveals two collaboration types in the hinterland, based on the different carriers' interpretation of market conditions as changeable or as given, driving their collaboration mindsets and strategic actions: The developer, who has a proactive collaboration mindset and practices strategic maneuvers toward changing poor market conditions through collaboration, and the adapter, who has a defensive collaboration mindset and perceives market conditions as given and constraining collaboration. Research limitations/implications: The qualitative results will help researchers better understand how collaboration practices depend on the carriers' subjective interpretations and perceptions of the market. Practical implications: Based on the findings, managers of carriers gain an understanding of the different types of actors in their market and the relevance of acknowledging these types. Consequently, they can design appropriate strategic measures toward collaboration. Originality/value: The findings for the first time provide exploratory insights of carriers' mindsets.Publicationtype: Journal Article27 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Robust berth scheduling using machine learning for vessel arrival time prediction(Springer, 2023-03); ; ; ; In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach.Publicationtype: Journal ArticleScopus© Citations 12 102 185 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Collaboration benefits in port hinterland transportationPort hinterland transportation with trucks is an important part of the maritime supply chain as a significant part of supply chain costs are generated here, e.g. due to empty container transportation. Horizontal collaboration among carriers offers potential for cost reduction, but needs to be set up in a way which is fair and advantageous in the long-run for all carriers involved. In this work, a new model is developed which takes these and other realistic requirements, e.g. the use of different container types and of empty container depots, into account. The results allow to quantify the benefits of collaboration for different collaborating group sizes and under different fairness mechanisms.Publicationtype: Conference Paper24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Optimization of container transportation in port hinterland with trucks : quantifying the effect of cooperation(2022-09-22); ; ; PURPOSE The presentation gives an overview of the possibilities to increase profits by horizontal cooperation in hinterland container logistics with trucks, specifically through sharing customer orders. METHODOLOGY A new optimization approach is presented based on results of a comprehensive literature review and a qualitative pre-study study. The approach considers the sharing of customer orders to be governed by a predefined set of trusted collaboration partners with access to the customer orders. Based on the results of the literature review and an empirical pre-study, this mechanism was identified as a prerequisite for a stable and long-term-oriented cooperation. FINDINGS The analysis shows that different degrees of cooperation, i.e. different sizes of trust groups and different intensity of order exchange, as well as different fairness constraints have a strong and varying impact on the efficiency of the system, e.g. in terms of profit increase as well as with respect to the distribution of orders and profits. The interrelations between these aspects are studied and quantified. ORIGINALITY The results allow to quantify the effect of cooperation in the port hinterland. The influence of the size of trust groups as well as different compensation mechanisms and their combination on long-term cooperation is shown.Publicationtype: Conference Presentation58 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Aggregator-based optimization of the deployment of decentralized flexibility under consideration of technological and economic restrictionsThe energy system transformation towards decentralized and renewable energy resources (DERs) is inevitable in order to mitigate climate change. New operational concepts arise as a consequence of structural system changes and the ongoing digitization of energy systems. Research in the field of transactive energy gives increasing importance to energy markets, not only from an economic perspective, but also as part of an approach for efficiently and resiliently operating grids. Aggregators help to bring the DERs’ potential for flexibility to the market. However, an aggregator’s decision on how to employ a large number of different kinds of flexible resources, considering the respective technological properties and also economic conditions of the flexibility providers, is a complex problem. In this work, mixed-integer linear programming is used to develop and implement an optimization model for this problem. The model is applied in a case study consisting of a distribution grid with a large number of prosumer households with load and generation profiles and individual flexible assets. The new research contribution lies in the high level of detail and the combined consideration of different flexibility types such as heat pumps, electric vehicles, battery storages and time-shiftable loads. The integration of a flexibility framework which regulates the aggregators’ degrees of freedom in deploying their customers’ flexibility is a further innovation compared to existing literature. The numerical results from the case study give insights regarding the utility that the different types of flexibility provide to the aggregator, to the households and to the overall grid resilience. Further analyses validate the efficiency of the flexibility framework.Publicationtype: Conference Presentation133 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Evacuation based extension of the system optimal dynamic traffic assignment problemThe system optimal dynamic traffic assignment (SODTA) is a well-known problem for evacuation planning. While the SODTA provides an easy-to-use framework for traffic assignment, due to its linearity and natural integer variables, in its original form the SODTA also has several shortcomings with respect to its usefulness for realistic evacuation cases. These shortcomings include a) a lack of consideration of non-controllable evacuees and induced background traffic, b) different destination categories as well as evacuees with individual destination preferences, which lead to the well-known FIFO-problem, are not taken into account, c) a lack of consideration of the impact of individual evacuee behaviour. In this presentation a comprehensive extension of the SODTA is introduced which considers a) the occurrence of non-controllable background traffic, b) the appearance of differing evacuee characteristics regarding destination preferences while prohibiting the occurrence of the well-known holding-back effect adjusted to multiple destinations and c) providing simulation results in order to assess the impacts of evacuee behaviour and the FIFO problem. An illustrative case study as well as recommended algorithms for the parametrisation of background traffic and destination preferences are presented. The extension of the SODTA is evaluated regarding its usefulness and also its limitations for evacuation planning purposes. Further, the induced error resulting from the FIFO-problem is evaluated and discussed regarding its implications on the real life application of the newly developed model extensions.Publicationtype: Conference Presentation89 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Optimization of container transportation in port hinterland with trucks : approaches to cooperationLogistics in the hinterland of ports account for up to 80% of the total cost of the maritime logistics chain. Therefore, optimizing hinterland logistics offers large potential for using infrastructure resources more efficiently, for reducing emissions and costs, and for providing high quality hinterland services to increase competitiveness. Cooperation is one promising approach here. A mathematical model for the operative optimization of container transportation in port hinterland with trucks, considering cooperation, is presented. It is discussed how costs in hinterland container logistics with trucks can be reduced by using horizontal cooperation, i.e. by sharing customer orders, and how stable and long-term-oriented cooperation can be established. A setting is considered in which each trucking company has its own customer orders, some of which it is willing to share or pass on to others, but only at a certain price or compensation. To enable and promote stable and long-term cooperation, fairness is important; therefore, different fairness constraint groups are modelled and implemented. Moreover, different constellations of so-called “trust groups”, i.e. groups within which an exchange of orders may take place, are studied. The effects of cooperation under these different conditions are quantified, compared and analyzed. The analysis shows that different degrees of cooperation, i.e. different sizes of trust groups and different intensity of order exchange, as well as different fairness constraints have a strong and varying impact on the efficiency of the system, e.g. in terms of cost and emission reduction as well as with respect to the distribution of orders and profits among the trucking companies.Publicationtype: Conference Presentation24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Booking limits vs. bid-prices : a comparison of revenue management strategies for make-to-order productionDue to strong competition, there is a trend towards individualized products, leading to large numbers of product variants. To handle this situation, many companies apply the make-to-order principle where production only starts after an order has been accepted. However, due to limited capacity not every order can be fulfilled and hence, decisions regarding order acceptance have to be made. The objectives pursued by a manufacturing company in this situation are usually twofold: On the one hand, companies aim to maximize (short-term) profits. On the other hand, the goal of providing good service in particular to valuable, returning customers is crucial with respect to long-term business success. These two objectives are usually in conflict as valuable customers often get price discounts to tie them to the company. In this talk, booking limit and bid-price based revenue management strategies for order acceptance are presented and compared. Both strategy types use a two-stage acceptance process that combines two mixed-integer linear programming models. In the first stage, a preliminary acceptance decision is made by booking limits/bid-prices. In the second stage, subsequent production scheduling is considered to ensure that only orders are accepted which can be completed no later than their deadline. Moreover, aspired service levels for different customer groups are included in both models, to take not only the profit, but also the customer value into account. The performance of the different strategies is compared in various case studies, with the well-known first-come-first-served policy serving as a benchmark. It can be shown that the suggested revenue management strategies work successfully and are able to establish a good trade-off between the conflicting objectives.Publicationtype: Conference Presentation67