@inbook{14890, author = {{Kuhlemann, Stefan and Sellmann, Meinolf and Tierney, Kevin}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030300470}}, issn = {{0302-9743}}, title = {{{Exploiting Counterfactuals for Scalable Stochastic Optimization}}}, doi = {{10.1007/978-3-030-30048-7_40}}, year = {{2019}}, } @article{14540, author = {{Schryen, Guido and Kliewer, Natalia and Borndörfer, Ralf and Koch, Thorsten}}, journal = {{OR News}}, pages = {{34--35}}, title = {{{High-Performance Business Computing – Parallel Algorithms and Implementations for Solving Problems in Operations Research and Data Analysis}}}, volume = {{65}}, year = {{2019}}, } @inproceedings{5675, abstract = {{When responding to natural disasters, professional relief units are often supported by many volunteers which are not affiliated to humanitarian organizations. The effective coordination of these volunteers is crucial to leverage their capabilities and to avoid conflicts with professional relief units. In this paper, we empirically identify key requirements that professional relief units pose on this coordination. Based on these requirements, we suggest a decision model. We computationally solve a real-world instance of the model and empirically validate the computed solution in interviews with practitioners. Our results show that the suggested model allows for solving volunteer coordination tasks of realistic size near-optimally within short time, with the determined solution being well accepted by practitioners. We also describe in this article how the suggested decision support model is integrated in the volunteer coordination system which we develop in joint cooperation with a disaster management authority and a software development company.}}, author = {{Rauchecker, Gerhard and Schryen, Guido}}, booktitle = {{Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management}}, keywords = {{Coordination of spontaneous volunteers, volunteer coordination system, decision support, scheduling optimization model, linear programming}}, location = {{Rochester, NY, USA}}, title = {{{Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief}}}, year = {{2018}}, } @inproceedings{5681, author = {{Prester, Julian and Wagner, Gerit and Schryen, Guido}}, booktitle = {{Proceedings of the 2018 International Conference on Information Systems (ICIS 2018)}}, location = {{San Francisco, CA, USA}}, title = {{{Classifying the Ideational Impact of IS Review Articles: A Natural Language Processing Based Approach}}}, year = {{2018}}, } @book{6164, author = {{Schillinger, Rolf and Schryen, Guido}}, isbn = {{978-3-88246-374-3}}, publisher = {{University of Regensburg}}, title = {{{Security in Highly Connected IT Systems – Results of the Bavarian Research Alliance FORSEC}}}, doi = {{10.5283/epub.36264}}, year = {{2018}}, } @article{5586, abstract = {{The need to protect resources against attackers is reflected by huge information security investments of firms worldwide. In the presence of budget constraints and a diverse set of assets to protect, organizations have to decide in which IT security measures to invest, how to evaluate those investment decisions, and how to learn from past decisions to optimize future security investment actions. While the academic literature has provided valuable insights into these issues, there is a lack of empirical contributions. To address this lack, we conduct a theory-based exploratory multiple case study. Our case study reveals that (1) firms? investments in information security are largely driven by external environmental and industry-related factors, (2) firms do not implement standardized decision processes, (3) the security process is perceived to impact the business process in a disturbing way, (4) both the implementation of evaluation processes and the application of metrics are hardly existent and (5) learning activities mainly occur at an ad-hoc basis.}}, author = {{Weishäupl, Eva and Yasasin, Emrah and Schryen, Guido}}, journal = {{Computers & Security}}, keywords = {{Information Security Investments, Multiple Case Study, Organizations, Single Loop Learning, Double Loop Learning}}, pages = {{807 -- 823}}, publisher = {{Elsevier}}, title = {{{Information Security Investments: An Exploratory Multiple Case Study on Decision-Making, Evaluation and Learning}}}, volume = {{77}}, year = {{2018}}, } @inproceedings{5600, author = {{Schuster, Richard and Wagner, Gerit and Schryen, Guido}}, booktitle = {{Proceedings of the 2018 International Conference on Information Systems (ICIS 2018)}}, location = {{San Francisco, CA, USA}}, title = {{{Information Systems Design Science Research and Cumulative Knowledge Development: An Exploratory Study}}}, year = {{2018}}, } @inbook{14856, author = {{Hallmann, Corinna and Burmeister, Sascha Christian and Wissing, Michaela and Suhl, Leena}}, booktitle = {{Communications in Computer and Information Science}}, isbn = {{9783319962702}}, issn = {{1865-0929}}, title = {{{Heuristics and Simulation for Water Tank Optimization}}}, doi = {{10.1007/978-3-319-96271-9_5}}, year = {{2018}}, } @inbook{14861, author = {{Hallmann, Corinna and Kuhlemann, Stefan}}, booktitle = {{Operations Research Proceedings}}, isbn = {{9783319899190}}, issn = {{0721-5924}}, title = {{{Model Generator for Water Distribution Systems}}}, doi = {{10.1007/978-3-319-89920-6_34}}, year = {{2018}}, } @inproceedings{5692, abstract = {{We consider Max-min Share (MmS) fair allocations of indivisible chores (items with negative utilities). We show that allocation of chores and classical allocation of goods (items with positive utilities) have some fundamental connections but also differences which prevent a straightforward application of algorithms for goods in the chores setting and viceversa. We prove that an MmS allocation does not need to exist for chores and computing an MmS allocation - if it exists - is strongly NP-hard. In view of these non-existence and complexity results, we present a polynomial-time 2-approximation algorithm for MmS fairness for chores. We then introduce a new fairness concept called optimal MmS that represents the best possible allocation in terms of MmS that is guaranteed to exist. We use connections to parallel machine scheduling to give (1) a polynomial-time approximation scheme for computing an optimal MmS allocation when the number of agents is fixed and (2) an effective and efficient heuristic with an ex-post worst-case analysis.}}, author = {{Aziz, Haris and Rauchecker, Gerhard and Schryen, Guido and Walsh, Toby}}, booktitle = {{Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)}}, location = {{San Francisco, CA, USA}}, number = {{1}}, pages = {{1--7}}, title = {{{Algorithms for Max-Min Share Fair Allocation of Indivisible Chores}}}, volume = {{31}}, year = {{2017}}, }