@inproceedings{47427, author = {{Schryen, Guido and Marrone, Mauricio and Yang, Jiaqi}}, booktitle = {{Proceedings of the 57th Hawaii International Conference on System Science (HICSS 2024)}}, title = {{{Adopting Generative AI for Literature Reviews: An Epistemological Perspective}}}, year = {{2024}}, } @inproceedings{47429, author = {{Betke, Hans and Sperling, Martina and Schryen, Guido and Sackmann, Stefan}}, booktitle = {{Proceedings of the 57th Hawaii International Conference on System Science (HICSS 2024)}}, title = {{{A Design Theory for Spontaneous Volunteer Coordination Systems in Disaster Response}}}, year = {{2024}}, } @article{50301, author = {{Schryen, Guido}}, journal = {{Journal of Parallel and Distributed Computing}}, title = {{{Speedup and efficiency of computational parallelization: A unifying approach and asymptotic analysis}}}, year = {{2024}}, } @article{52092, author = {{Stumpe, Miriam}}, issn = {{2352-1465}}, journal = {{Transportation Research Procedia}}, pages = {{402--409}}, publisher = {{Elsevier BV}}, title = {{{A new mathematical formulation for the simultaneous optimization of charging infrastructure and vehicle schedules for electric bus systems}}}, doi = {{10.1016/j.trpro.2024.02.051}}, volume = {{78}}, year = {{2024}}, } @article{42179, author = {{Burmeister, Sascha Christian and Schryen, Guido}}, journal = {{Energy Systems}}, publisher = {{Springer}}, title = {{{Distribution Network Optimization: Predicting computation times to design scenario analysis for network operators}}}, doi = {{10.1007/s12667-023-00572-5}}, year = {{2023}}, } @article{44361, author = {{Schryen, Guido and Sperling, Martina}}, journal = {{Computers & Operations Research}}, number = {{September}}, title = {{{Literature Reviews in Operations Research: A New Taxonomy and a Meta Review}}}, volume = {{157}}, year = {{2023}}, } @article{44383, author = {{Dieter, Peter and Caron, Matthew and Schryen, Guido}}, journal = {{European Journal of Operational Research (EJOR)}}, number = {{1}}, pages = {{283--300}}, title = {{{Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework}}}, doi = {{https://doi.org/10.1016/j.ejor.2023.04.043}}, volume = {{311}}, year = {{2023}}, } @article{45816, author = {{Dieter, Peter and Stumpe, Miriam and Ulmer, Marlin Wolf and Schryen, Guido}}, journal = {{Transportation Research Part D}}, title = {{{Anticipatory Assignment of Passengers to Meeting Points for Taxi-Ridesharing}}}, volume = {{121}}, year = {{2023}}, } @inbook{46867, author = {{Dieter, Peter}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031436116}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{A Regret Policy for the Dynamic Vehicle Routing Problem with Time Windows}}}, doi = {{10.1007/978-3-031-43612-3_14}}, year = {{2023}}, } @article{47431, author = {{Burmeister, Sascha Christian and Guericke, Daniela and Schryen, Guido}}, journal = {{Flexible Services and Manufacturing Journal}}, title = {{{A Memetic NSGA-II for the Multi-Objective Flexible Job Shop Scheduling Problem with Real-time Energy Tariffs}}}, doi = {{10.1007/s10696-023-09517-7}}, year = {{2023}}, } @article{45112, author = {{Beverungen, Daniel and Kundisch, Dennis and Mirbabaie, Milad and Müller, Oliver and Schryen, Guido and Trang, Simon Thanh-Nam and Trier, Matthias}}, journal = {{Business & Information Systems Engineering}}, number = {{4}}, pages = {{463 -- 474}}, title = {{{Digital Responsibility – a Multilevel Framework for Responsible Digitalization}}}, doi = {{https://doi.org/10.1007/s12599-023-00822-x}}, volume = {{65}}, year = {{2023}}, } @misc{48335, author = {{Knorr, Lukas and Jungeilges, André and Pfeifer, Florian and Burmeister, Sascha Christian and Meschede, Henning}}, publisher = {{4. Aachener Ofenbau- und Thermoprozess-Kolloquium}}, title = {{{Regenerative Energien für einen effizienten Betrieb von Presshärtelinien}}}, year = {{2023}}, } @article{23415, author = {{Sperling, Martina and Schryen, Guido}}, journal = {{European Journal of Operational Research (EJOR)}}, number = {{2}}, pages = {{690 -- 705}}, title = {{{Decision Support for Disaster Relief: Coordinating Spontaneous Volunteers}}}, volume = {{299}}, year = {{2022}}, } @inproceedings{21093, abstract = {{Requirements for energy distribution networks are changing fast due to the growing share of renewable energy, increasing electrification, and novel consumer and asset technologies. Since uncertainties about future developments increase planning difficulty, flexibility potentials such as synergies between the electricity, gas, heat, and transport sector often remain unused. In this paper, we therefore present a novel module-based concept for a decision support system that helps distribution network planners to identify cross-sectoral synergies and to select optimal network assets such as transformers, cables, pipes, energy storage systems or energy conversion technology. The concept enables long-term transformation plans and supports distribution network planners in designing reliable, sustainable and cost-efficient distribution networks for future demands.}}, author = {{Kirchhoff, Jonas and Burmeister, Sascha Christian and Weskamp, Christoph and Engels, Gregor}}, booktitle = {{Energy Informatics and Electro Mobility ICT}}, editor = {{Breitner, Michael H. and Lehnhoff, Sebastian and Nieße, Astrid and Staudt, Philipp and Weinhardt, Christof and Werth, Oliver}}, title = {{{Towards a Decision Support System for Cross-Sectoral Energy Distribution Network Planning}}}, year = {{2021}}, } @article{20212, abstract = {{Ideational impact refers to the uptake of a paper's ideas and concepts by subsequent research. It is defined in stark contrast to total citation impact, a measure predominantly used in research evaluation that assumes that all citations are equal. Understanding ideational impact is critical for evaluating research impact and understanding how scientific disciplines build a cumulative tradition. Research has only recently developed automated citation classification techniques to distinguish between different types of citations and generally does not emphasize the conceptual content of the citations and its ideational impact. To address this problem, we develop Deep Content-enriched Ideational Impact Classification (Deep-CENIC) as the first automated approach for ideational impact classification to support researchers' literature search practices. We evaluate Deep-CENIC on 1,256 papers citing 24 information systems review articles from the IT business value domain. We show that Deep-CENIC significantly outperforms state-of-the-art benchmark models. We contribute to information systems research by operationalizing the concept of ideational impact, designing a recommender system for academic papers based on deep learning techniques, and empirically exploring the ideational impact of the IT business value domain. }}, author = {{Prester, Julian and Wagner, Gerit and Schryen, Guido and Hassan, Nik Rushdi}}, journal = {{Decision Support Systems}}, keywords = {{Ideational impact, citation classification, academic recommender systems, natural language processing, deep learning, cumulative tradition}}, number = {{January}}, title = {{{Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach}}}, volume = {{140}}, year = {{2021}}, } @article{20844, abstract = {{Review papers are essential for knowledge development in IS. While some are cited twice a day, others accumulate single digit citations over a decade. The magnitude of these differences prompts us to analyze what distinguishes those reviews that have proven to be integral to scientific progress from those that might be considered less impactful. Our results highlight differences between reviews aimed at describing, understanding, explaining, and theory testing. Beyond the control variables, they demonstrate the importance of methodological transparency and the development of research agendas. These insights inform all stakeholders involved in the development and publication of review papers.}}, author = {{Wagner, Gerit and Prester, Julian and Roche, Maria and Schryen, Guido and Benlian, Alexander and Paré, Guy and Templier, Mathieu}}, journal = {{Information & Management}}, keywords = {{Literature review, review papers, scientometric, scientific impact, citation analysis}}, number = {{3}}, title = {{{Which Factors Affect the Scientific Impact of Review Papers in IS Research? A Scientometric Study}}}, volume = {{58}}, year = {{2021}}, } @article{23494, author = {{Stumpe, Miriam and Rößler, David and Schryen, Guido and Kliewer, Natalia}}, journal = {{EURO Journal on Transportation and Logistics}}, title = {{{Study on Sensitivity of Electric Bus Systems under Simultaneous Optimization of Charging Infrastructure and Vehicle Schedules}}}, doi = {{https://doi.org/10.1016/j.ejtl.2021.100049}}, volume = {{10}}, year = {{2021}}, } @article{17934, author = {{Wagner, Gerit and Prester, Julian and Schryen, Guido}}, journal = {{Communications of the Association for Information Systems}}, number = {{1}}, title = {{{Exploring the Scientific Impact of Information Systems Design Science Research}}}, volume = {{48}}, year = {{2021}}, } @techreport{17019, abstract = {{The scientific impact of research papers is multi-dimensional and can be determined quantitatively by means of citation analysis and qualitatively by means of content analysis. Accounting for the widely acknowledged limitations of pure citation analysis, we adopt a knowledge-based perspective on scientific impact to develop a methodology for content-based citation analysis which allows determining how papers have enabled knowledge development in subsequent research (knowledge impact). As knowledge development differs between research genres, we develop a new knowledgebased citation analysis methodology for the genre of standalone literature reviews (LRs). We apply the suggested methodology to the IS business value domain by manually coding 22 LRs and 1,228 citing papers (CPs) and show that the results challenge the assumption that citations indicate knowledge impact. We derive implications for distinguishing knowledge impact from citation impact in the LR genre. Finally, we develop recommendations for authors of LRs, scientific evaluation committees and editorial boards of journals how to apply and benefit from the suggested methodology, and we discuss its efficiency and automatization.}}, author = {{Schryen, Guido and Wagner, Gerit and Benlian, Alexander}}, keywords = {{Scientific impact, knowledge impact, content-based citation analysis, methodology}}, title = {{{Distinguishing Knowledge Impact from Citation Impact: A Methodology for Analysing Knowledge Impact for the Literature Review Genre}}}, year = {{2020}}, } @inproceedings{17055, abstract = {{Understanding a new literature corpus can be a grueling experience for junior scholars. Nevertheless, corresponding guidelines have not been updated for decades. We contend that the traditional strategy of skimming all papers and reading selected papers afterwards needs to be revised. Therefore, we design a new strategy that guides the overall exploratory process by prioritizing influential papers for initial reading, followed by skimming the remaining papers. Consistent with schemata theory, starting with in-depth reading allows readers to acquire more substantial prior content schemata, which are representa-tive for the literature corpus and useful in the following skimming process. To this end, we develop a prototype that identifies the influential papers from a set of PDFs, which is illustrated in a case study in the IT business value domain. With the new strategy, we envision a more efficient process of exploring unknown literature corpora.}}, author = {{Wagner, Gerit and Empl, Philipp and Schryen, Guido}}, booktitle = {{28th European Conference on Information Systems (ECIS 2020)}}, keywords = {{Reading and skimming, Exploring literature, Review methodology, Design science research, Schemata theory}}, location = {{Marrakesh, Morocco}}, title = {{{Designing a Novel Strategy for Exploring Literature Corpora}}}, year = {{2020}}, }