TY - CHAP A1 - Euler, Ricardo A1 - Borndörfer, Ralf A1 - Puchert, Christian A1 - Takkula, Tuomo T1 - ULD Build-Up Scheduling with Logic-Based Benders Decomposition T2 - Integration of Constraint Programming, Artificial Intelligence, and Operations Research N2 - We study a complex planning and scheduling problem arising from the build-up process of air cargo pallets and containers, collectively referred to as unit load devices (ULD), in which ULDs must be assigned to workstations for loading. Since air freight usually becomes available gradually along the planning horizon, ULD build-ups must be scheduled neither too early to avoid underutilizing ULD capacity, nor too late to avoid resource conflicts with other flights. Whenever possible, ULDs should be built up in batches, thereby giving ground handlers more freedom to rearrange cargo and utilize the ULD's capacity efficiently. The resulting scheduling problem has an intricate cost function and produces large time-expanded models, especially for longer planning horizons. We propose a logic-based Benders decomposition approach that assigns batches to time intervals and workstations in the master problem, while the actual schedule is decided in a subproblem. By choosing appropriate intervals, the subproblem becomes a feasibility problem that decomposes over the workstations. Additionally, the similarity of many batches is exploited by a strengthening procedure for no-good cuts. We benchmark our approach against a time-expanded MIP formulation from the literature on a publicly available data set. It solves 15% more instances to optimality and decreases run times by more than 50% in the geometric mean. This improvement is especially pronounced for longer planning horizons of up to one week, where the Benders approach solves over 50% instances more than the baseline Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1007/978-3-031-60597-0_17 VL - 14742 SP - 259 EP - 276 ER - TY - CHAP A1 - Zittel, Janina A1 - Clarner, Jan-Patrick A1 - Tawfik, Christine A1 - Dykes, Maxwell A1 - Rivetta, Fabian A1 - Riedmüller, Stephanie T1 - A MULTI-OBJECTIVE OPTIMIZATION STRATEGY FOR DISTRICT HEATING PRODUCTION PORTFOLIO PLANNING T2 - Proceedings of 37th INTERNATIONAL CONFERENCE ON EFFICIENCY, COST, OPTIMIZATION, SIMULATION AND ENVIRONMENTAL IMPACT OF ENERGY SYSTEMS N2 - The imperative to decarbonize energy systems has intensified the need for efficient transformations within the heating sector, with a particular focus on district heating networks. This study addresses this challenge by proposing a comprehensive optimization approach evaluated on the district heating network of the Märkisches Viertel of Berlin. Our objective is to simultaneously optimize heat production with three targets: minimizing costs, minimizing CO2-emissions, and maximizing heat generation from Combined Heat and Power (CHP) plants for enhanced efficiency. To tackle this optimization problem, we employed a Mixed-Integer Linear Program (MILP) that encompasses the conversion of various fuels into heat and power, integration with relevant markets, and considerations for technical constraints on power plant operation. These constraints include startup and minimum downtime, activation costs, and storage limits. The ultimate goal is to delineate the Pareto front, representing the optimal trade-offs between the three targets. We evaluate variants of the 𝜖-constraint algorithm for their effectiveness in coordinating these objectives, with a simultaneous focus on the quality of the estimated Pareto front and computational efficiency. One algorithm explores solutions on an evenly spaced grid in the objective space, while another dynamically adjusts the grid based on identified solutions. Initial findings highlight the strengths and limitations of each algorithm, providing guidance on algorithm selection depending on desired outcomes and computational constraints. Our study emphasizes that the optimal choice of algorithm hinges on the density and distribution of solutions in the feasible space. Whether solutions are clustered or evenly distributed significantly influences algorithm performance. These insights contribute to a nuanced understanding of algorithm selection for multi-objective multi-energy system optimization, offering valuable guidance for future research and practical applications for planning sustainable district heating networks. Y1 - 2024 ER - TY - CHAP A1 - Reidelbach, Marco T1 - Towards a FAIR Documentation of Workflows and Models in Applied Mathematics N2 - Modeling-Simulation-Optimization workflows play a fundamental role in applied mathematics. The Mathematical Research Data Initiative, MaRDI, responded to this by developing a FAIR and machine-interpretable template for a comprehensive documentation of such workflows. MaRDMO, a Plugin for the Research Data Management Organiser, enables scientists from diverse fields to document and publish their workflows on the MaRDI Portal seamlessly using the MaRDI template. Central to these workflows are mathematical models. MaRDI addresses them with the MathModDB ontology, offering a structured formal model description. Here, we showcase the interaction between MaRDMO and the MathModDB Knowledge Graph through an algebraic modeling workflow from the Digital Humanities. This demonstration underscores the versatility of both services beyond their original numerical domain. Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2403.17778 ER - TY - JOUR A1 - Diederichs, Katja A1 - Krause, Celia A1 - Lemaire, Marina A1 - Reidelbach, Marco A1 - Windeck, Jürgen T1 - A Vision for Data Management Plans in the NFDI N2 - At present, data management plans (DMPs) are still often perceived as mere documents for funding agencies providing clarity on how research data will be handled during a funded project, but are not usually actively involved in the processes. However, they contain a great deal of information that can be shared automatically to facilitate active research data management (RDM) by providing metadata to research infrastructures and supporting communication between all involved stakeholders. This position paper brings together a number of ideas developed and collected during interdisciplinary workshops of the Data Management Planning Working Group (infra-dmp), which is part of the section Common Infrastructures of the National Research Data Infrastructure (NFDI) in Germany. We present our vision of a possible future role of DMPs, templates, and tools in the upcoming NFDI service architecture. Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.10570653 ER - TY - CHAP A1 - Schembera, Björn A1 - Wübbeling, Frank A1 - Kleikamp, Hendrik A1 - Biedinger, Christine A1 - Fiedler, Jochen A1 - Reidelbach, Marco A1 - Shehu, Aurela A1 - Schmidt, Burkhard A1 - Koprucki, Thomas A1 - Iglezakis, Dotothea A1 - Göddeke, Dominik T1 - Ontologies for Models and Algorithms in Applied Mathematics and Related Disciplines N2 - In applied mathematics and related disciplines, the modeling-simulation-optimization workflow is a prominent scheme, with mathematical models and numerical algorithms playing a crucial role. For these types of mathematical research data, the Mathematical Research Data Initiative has developed, merged and implemented ontologies and knowledge graphs. This contributes to making mathematical research data FAIR by introducing semantic technology and documenting the mathematical foundations accordingly. Using the concrete example of microfracture analysis of porous media, it is shown how the knowledge of the underlying mathematical model and the corresponding numerical algorithms for its solution can be represented by the ontologies. Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2310.20443 ER - TY - JOUR A1 - Schembera, Björn A1 - Wübbeling, Frank A1 - Koprucki, Thomas A1 - Biedinger, Christine A1 - Reidelbach, Marco A1 - Schmidt, Burkhard A1 - Göddeke, Dominik A1 - Fiedler, Jochen T1 - Building Ontologies and Knowledge Graphs for Mathematics and its Applications JF - Proceedings of the Conference on Research Data Infrastructure N2 - Ontologies and knowledge graphs for mathematical algorithms and models are presented, that have been developed by the Mathematical Research Data Initiative. This enables FAIR data handling in mathematics and the applied disciplines. Moreover, challenges of harmonization during the ontology development are discussed. Y1 - 2023 U6 - https://doi.org/10.52825/cordi.v1i.255 SN - 2941-296X VL - 1 PB - TIB Open Publishing ER - TY - JOUR A1 - Reidelbach, Marco A1 - Ferrer, Eloi A1 - Weber, Marcus T1 - MaRDMO Plugin BT - Document and Retrieve Workflows Using the MaRDI Portal JF - Proceedings of the Conference on Research Data Infrastructure N2 - MaRDMO, a plugin for the Research Data Management Organiser, was developed in the Mathematical Research Data Initiative to document interdisciplinary workflows using a standardised scheme. Interdisciplinary workflows recorded this way are published directly on the MaRDI portal. In addition, central information is integrated into the MaRDI knowledge graph. Next to the documentation, MaRDMO offers the possibility to retrieve existing interdisciplinary workflows from the MaRDI Knowledge Graph to allow the reproduction of the initial work and to provide scientists with new researchimpulses. Thus, MaRDMO creates a community-driven knowledge loop that could help to overcome the replication crisis. Y1 - 2023 U6 - https://doi.org/10.52825/cordi.v1i.254 SN - 2941-296X VL - 1 PB - TIB Open Publishing ER - TY - JOUR A1 - Benner, Peter A1 - Danabalan, Renita A1 - Gödekke, Dominik A1 - Kastner, Lars A1 - Krause, Tabea A1 - Mietchen, Daniel A1 - Reidelbach, Marco A1 - Schembera, Björn A1 - Schubotz, Moritz A1 - Sinn, Rainer A1 - Tabelow, Karsten T1 - Research Data Management Planning in Mathematics N2 - Research data are crucial in mathematics and all scientific disciplines, as they form the foundation for empirical evidence, by enabling the validation and reproducibility of scientific findings. Mathematical research data (MathRD) have become vast and complex, and their interdisciplinary potential and abstract nature make them ubiquitous in various scientific fields. The volume of data and the velocity of its creation are rapidly increasing due to advancements in data science and computing power. This complexity extends to other disciplines, resulting in diverse research data and computational models. Thus, proper handling of research data is crucial both within mathematics and for its manifold connections and exchange with other disciplines. The National Research Data Infrastructure (NFDI), funded by the federal and state governments of Germany, consists of discipline-oriented consortia, including the Mathematical Research Data Initiative (MaRDI). MaRDI has been established to develop services, guidelines and outreach measures for all aspects of MathRD, and thus support the mathematical research community. Research data management (RDM) should be an integral component of every scientific project, and is becoming a mandatory component of grants with funding bodies such as the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). At the core of RDM are the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This document aims to guide mathematicians and researchers from related disciplines who create RDM plans. It highlights the benefits and opportunities of RDM in mathematics and interdisciplinary studies, showcases examples of diverse MathRD, and suggests technical solutions that meet the requirements of funding agencies with specific examples. The document is regularly updated to reflect the latest developments within the mathematical community represented by MaRDI. Y1 - 2023 U6 - https://doi.org/10.5281/zenodo.10018245 ER - TY - JOUR A1 - Sikorski, Alexander T1 - Voronoi Graph - Improved raycasting and integration schemes for high dimensional Voronoi diagrams N2 - The computation of Voronoi Diagrams, or their dual Delauney triangulations is difficult in high dimensions. In a recent publication Polianskii and Pokorny propose an iterative randomized algorithm facilitating the approximation of Voronoi tesselations in high dimensions. In this paper, we provide an improved vertex search method that is not only exact but even faster than the bisection method that was previously recommended. Building on this we also provide a depth-first graph-traversal algorithm which allows us to compute the entire Voronoi diagram. This enables us to compare the outcomes with those of classical algorithms like qHull, which we either match or marginally beat in terms of computation time. We furthermore show how the raycasting algorithm naturally lends to a Monte Carlo approximation for the volume and boundary integrals of the Voronoi cells, both of which are of importance for finite Volume methods. We compare the Monte-Carlo methods to the exact polygonal integration, as well as a hybrid approximation scheme. Y1 - 2024 ER - TY - JOUR A1 - Schaible, Jonas A1 - Nouri, Bijan A1 - Höpken, Lars A1 - Kotzab, Tim A1 - Loevenich, Matthias A1 - Blum, Niklas A1 - Hammer, Annette A1 - Stührenberg, Jonas A1 - Jäger, Klaus A1 - Becker, Christiane A1 - Wilbert, Stefan T1 - Application of nowcasting to reduce the impact of irradiance ramps on PV power plants JF - EPJ Photovolt. Y1 - 2024 U6 - https://doi.org/10.1051/epjpv/2024009 VL - 15 SP - 15 ER -