@article{63838, abstract = {{Industrial electrification is increasing to reduce fossil fuel dependence, alongside a growing share of volatile renewables. A secure and reliable energy supply is crucial for industry, leading to a shift from centralised to decentralised grid structures. DC microgrids becoming increasingly popular in industry, since they enable energy recuperation from braking, reduce components and cables, and integrate storage and local generation to manage supply interruptions or peak loads. EVs add further synergies by serving as mobile storage units, helping to store and redistribute locally generated renewable energy. This paper analyses how EV integration in droop-controlled DC grids can contribute to a more stable, low-emission and peak-reduced load profile to the supply grid through load shifting and bridge interruptions. A droop-controlled DC grid model has been developed, incorporating an EV charging park based on probability functions. Scalable scenarios allow for diverse condition analysis using an energy management system that utilises fuzzy logic and sequential MILP optimisation. It has been shown that a 7% improvement of coefficient represented grid-serving behaviour is possible by load shifting. It has also been demonstrated that an optimised EMS can reduce the demand-based CO2 emissions by 41kg for a representative day compared to a fuzzy logic EMS. At the same time peak load is decreased yielding a more constant residual load. These results highlight the potential of a controlled bidirectional charging infrastructure in DC grids and underscore the need to explicitly consider charging processes to ensure a residual load as constant as possible.}}, author = {{Rahlf, Henning Christoph and Knorr, Lukas and Althoff, Simon and Meschede, Henning}}, issn = {{2666-9552}}, journal = {{Smart Energy}}, keywords = {{DC-grid, Droop control, Grid-serving behaviour, Grid stability, Bidirectional charging, Sequential decision, MILP optimisation}}, publisher = {{Elsevier BV}}, title = {{{Analysis of bidirectional EV charging infrastructures within industrial DC grids}}}, doi = {{10.1016/j.segy.2026.100227}}, year = {{2026}}, } @inproceedings{64820, abstract = {{Political goals, emerging EU sustainability regulations, and industrial digitalization are driving the introduction of Digital Product Passports (DPPs) to enhance transparency, traceability, and compliance across product life cycles. However, the appropriate granularity of DPP integration across product architectures remains ambiguous. This paper introduces a structured, decision-oriented framework that links product structure, regulatory relevance, and information depth to define consistent DPP levels, supporting both industry implementation and future standardization.}}, author = {{Rohde, Katharina and Budde, Finn Lukas and Patrício, Bárbara and Ferreira, Tânia and Gonçalves, Ana and Ott, Manuel and Mozgova, Iryna}}, booktitle = {{Proceedings of the Design Society}}, keywords = {{digital product passport, product architecture, circular economy, information granularity, decision-making framework}}, location = {{Cavtat, Dubrovnik, Croatia}}, title = {{{Digital product passports and the challenge of product structure granularity: A decision-making framework for the level of DPP integration}}}, volume = {{6}}, year = {{2026}}, } @article{58650, abstract = {{Technical systems are characterized by increasing interdisciplinarity, complexity and networking. A product and its corresponding production systems require interdisciplinary multi-objective optimization. Sustainability and recyclability demands increase said complexity. The efficiency of previously established engineering methods is reaching its limits, which can only be overcome by systematic integration of extreme data. The aim of "hybrid decision support" is as follows: Data science and artificial intelligence should be used to supplement human capabilities in conjunction with existing heuristics, methods, modeling and simulation to increase the efficiency of product creation.}}, author = {{Gräßler, Iris and Pottebaum, Jens and Nyhuis, Peter and Stark, Rainer and Thoben, Klaus-Dieter and Wiederkehr, Petra}}, issn = {{2942-6170}}, journal = {{Industry 4.0 Science}}, keywords = {{AI, artificial intelligence, Data Science, decision support, extreme data, Künstliche Intelligenz, product creation, product development}}, number = {{1}}, publisher = {{GITO mbH Verlag}}, title = {{{Hybrid Decision Support in Product Creation - Improving performance with data science and artificial intelligence}}}, doi = {{10.30844/i4sd.25.1.18}}, volume = {{2025}}, year = {{2025}}, } @inbook{62701, abstract = {{Learning continuous vector representations for knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL firstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A final OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can effectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.}}, author = {{Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032060655}}, issn = {{0302-9743}}, keywords = {{Decision Tree, OWL Class Expression Learning, Description Logic, Knowledge Graph, Large Language Model, Verbalizer}}, location = {{Porto, Portugal}}, publisher = {{Springer Nature Switzerland}}, title = {{{Tree-Based OWL Class Expression Learner over Large Graphs}}}, doi = {{10.1007/978-3-032-06066-2_29}}, year = {{2025}}, } @misc{56282, abstract = {{Algorithmic bias has long been recognized as a key problem affecting decision-making processes that integrate artificial intelligence (AI) technologies. The increased use of AI in making military decisions relevant to the use of force has sustained such questions about biases in these technologies and in how human users programme with and rely on data based on hierarchized socio-cultural norms, knowledges, and modes of attention. In this post, Dr Ingvild Bode, Professor at the Center for War Studies, University of Southern Denmark, and Ishmael Bhila, PhD researcher at the “Meaningful Human Control: Between Regulation and Reflexion” project, Paderborn University, unpack the problem of algorithmic bias with reference to AI-based decision support systems (AI DSS). They examine three categories of algorithmic bias – preexisting bias, technical bias, and emergent bias – across four lifecycle stages of an AI DSS, concluding that stakeholders in the ongoing discussion about AI in the military domain should consider the impact of algorithmic bias on AI DSS more seriously.}}, author = {{Bhila, Ishmael and Bode, Ingvild}}, keywords = {{Algorithmic Bias, AI, Decision Support Systems, Autonomous Weapons Systems}}, publisher = {{ICRC Humanitarian Law & Policy Blog}}, title = {{{The problem of algorithmic bias in AI-based military decision support systems}}}, year = {{2024}}, } @article{48086, abstract = {{Individuals strive to make decisions that are consistent with not only their consumer preferences but also their psychological needs. However, they are confronted with complex, ambiguous or even false information. Ideologies and belief systems provide guidance when processing and evaluating information and give a coherent and comprehensible interpretation of reality. The first question is: why is an individual attracted to a particular ideology? Individuals choose ideologies that resonate with their subjective psychological needs and preferences. Second, how do individuals search for ideologies and find out which suit them best? We model an individual’s sequential information search for the best matching ideologies by applying Bayesian learning and utility optimization. Additional information enhances utility by reducing uncertainty. As a search is costly, the process may stop once an individual adopts an ideology even if the information set remains incomplete. Third, once they have chosen a particular ideology, individuals adhere to its rules and norms when making everyday decisions. Consumers not only physically consume, but they also act in accordance with their psychological needs.}}, author = {{Burs, Carina and Gries, Thomas and Müller, Veronika}}, issn = {{2158-3609}}, journal = {{Journal of Organizational Psychology}}, keywords = {{Economics, Ideology, Decision-making}}, number = {{1}}, publisher = {{North American Business Press}}, title = {{{The Choice of Ideology and Everyday Decisions}}}, doi = {{10.33423/jop.v23i1.6033}}, volume = {{23}}, year = {{2023}}, } @inproceedings{37312, abstract = {{Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners.}}, author = {{Leffrang, Dirk and Bösch, Kevin and Müller, Oliver}}, booktitle = {{Hawaii International Conference on System Sciences}}, keywords = {{Algorithm aversion, Time series, Decision making, Advice taking, Forecasting}}, title = {{{Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time}}}, year = {{2023}}, } @inproceedings{50118, abstract = {{Despite the widespread use of machine learning algorithms, their effectiveness is limited by a phenomenon known as algorithm aversion. Recent research concluded that unobserved variables can cause algorithm aversion. However, the impact of an unobserved variable on algorithm aversion remains unclear. Previous studies focused on situations where humans had more variables available than algorithms. We extend this research by conducting an online experiment with 94 participants, systematically varying the number of observable variables to the advisor and the advisor type. Surprisingly, our results did not confirm that an unobserved variable had a negative effect on advice-taking. Instead, we found a positive impact in an algorithm appreciation scenario. This study provides new insights into the paradoxical behavior in which people weigh advice more despite having fewer variables, as they correct for the advisor's errors. Practitioners should consider this behavior when designing algorithms and account for user correction behavior.}}, author = {{Leffrang, Dirk}}, booktitle = {{Wirtschaftsinformatik Conference}}, keywords = {{Algorithm aversion, Data, Decision-making, Advice-taking, Human-Computer Interaction}}, location = {{Paderborn}}, number = {{19}}, title = {{{The Broken Leg of Algorithm Appreciation: An Experimental Study on the Effect of Unobserved Variables on Advice Utilization}}}, year = {{2023}}, } @article{53216, author = {{Tavana, Madjid and Soltanifar, Mehdi and Santos-Arteaga, Francisco J.}}, issn = {{0254-5330}}, journal = {{Annals of Operations Research}}, keywords = {{Management Science and Operations Research, General Decision Sciences}}, number = {{2}}, pages = {{879--907}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Analytical hierarchy process: revolution and evolution}}}, doi = {{10.1007/s10479-021-04432-2}}, volume = {{326}}, year = {{2023}}, } @article{53223, author = {{Dellnitz, Andreas and Tavana, Madjid and Banker, Rajiv}}, issn = {{0254-5330}}, journal = {{Annals of Operations Research}}, keywords = {{Management Science and Operations Research, General Decision Sciences}}, number = {{2}}, pages = {{661--690}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{A novel median-based optimization model for eco-efficiency assessment in data envelopment analysis}}}, doi = {{10.1007/s10479-022-04937-4}}, volume = {{322}}, year = {{2023}}, } @inproceedings{56477, abstract = {{We describe a prototype of a Clinical Decision Support System (CDSS) that provides (counterfactual) explanations to support accurate medical diagnosis. The prototype is based on an inherently interpretable Bayesian network (BN). Our research aims to investigate which explanations are most useful for medical experts and whether co-constructing explanations can foster trust and acceptance of CDSS.}}, author = {{Liedeker, Felix and Cimiano, Philipp}}, keywords = {{Explainable AI, Clinical decision support, Bayesian network, Counterfactual explanations}}, location = {{Lissabon}}, title = {{{A Prototype of an Interactive Clinical Decision Support System with Counterfactual Explanations}}}, year = {{2023}}, } @inproceedings{29539, abstract = {{Explainable Artificial Intelligence (XAI) is currently an important topic for the application of Machine Learning (ML) in high-stakes decision scenarios. Related research focuses on evaluating ML algorithms in terms of interpretability. However, providing a human understandable explanation of an intelligent system does not only relate to the used ML algorithm. The data and features used also have a considerable impact on interpretability. In this paper, we develop a taxonomy for describing XAI systems based on aspects about the algorithm and data. The proposed taxonomy gives researchers and practitioners opportunities to describe and evaluate current XAI systems with respect to interpretability and guides the future development of this class of systems.}}, author = {{Kucklick, Jan-Peter}}, booktitle = {{Wirtschaftsinformatik 2022 Proceedings}}, keywords = {{Explainable Artificial Intelligence, XAI, Interpretability, Decision Support Systems, Taxonomy}}, location = {{Nürnberg (online)}}, title = {{{Towards a model- and data-focused taxonomy of XAI systems}}}, year = {{2022}}, } @article{53241, author = {{Khalili-Damghani, Kaveh and Tavana, Madjid and Ghasemi, Peiman}}, issn = {{0254-5330}}, journal = {{Annals of Operations Research}}, keywords = {{Management Science and Operations Research, General Decision Sciences}}, number = {{1}}, pages = {{103--141}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{A stochastic bi-objective simulation–optimization model for cascade disaster location-allocation-distribution problems}}}, doi = {{10.1007/s10479-021-04191-0}}, volume = {{309}}, year = {{2022}}, } @article{31691, abstract = {{Sustainable product engineering is becoming increasingly important. This includes the development of environmentally friendly products and the design for recycling. In this paper a holistic method for the assessment of solution alternatives is presented, in which the stakeholder perspectives along the generic product lifecycle are taken into account. Finally, a new visualization is presented. By visualizing the results in the integrated sustainability triangle, decision-makers in product development can holistically assess the sustainability of the solution alternatives.}}, author = {{Gräßler, Iris and Hesse, Philipp}}, issn = {{2732-527X}}, journal = {{Proceedings of the Design Society}}, keywords = {{sustainability, decision making, generic product lifecycle, design analysis, ecodesign}}, pages = {{1001--1010}}, publisher = {{Cambridge University Press (CUP)}}, title = {{{Approach to Sustainability-Based Assessment of Solution Alternatives in Early Stages of Product Engineering}}}, doi = {{10.1017/pds.2022.102}}, volume = {{2}}, year = {{2022}}, } @inproceedings{16933, abstract = {{The continuous innovation of its business models is an important task for a company to stay competitive. During this process, the company has to validate various hypotheses about its business models by adapting to uncertain and changing customer needs effectively and efficiently. This adaptation, in turn, can be supported by the concept of Software Product Lines (SPLs). SPLs reduce the time to market by deriving products for customers with changing requirements using a common set of features, structured as a feature model. Analogously, we support the process of business model adaptation by applying the engineering process of SPLs to the structure of the Business Model Canvas (BMC). We call this concept a Business Model Decision Line (BMDL). The BMDL matches business domain knowledge in the form of a feature model with customer needs to derive hypotheses about the business model together with experiments for validation. Our approach is effective by providing a comprehensive overview of possible business model adaptations and efficient by reusing experiments for different hypotheses. We implement our approach in a tool and illustrate the usefulness with an example of developing business models for a mobile application.}}, author = {{Gottschalk, Sebastian and Rittmeier, Florian and Engels, Gregor}}, booktitle = {{Proceedings of the 22nd IEEE International Conference on Business Informatics}}, keywords = {{Business Model Decision Line, Business Model Adaptation, Hypothesis-driven Adaptation, Software Product Line, Feature Model}}, location = {{Antwerp}}, publisher = {{IEEE}}, title = {{{Hypothesis-driven Adaptation of Business Models based on Product Line Engineering}}}, doi = {{10.1109/CBI49978.2020.00022}}, year = {{2020}}, } @inproceedings{48845, abstract = {{In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.}}, author = {{Bossek, Jakob and Grimme, Christian and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{decision making, dynamic optimization, evolutionary algorithms, multi-objective optimization, vehicle routing}}, pages = {{166–174}}, publisher = {{Association for Computing Machinery}}, title = {{{Dynamic Bi-Objective Routing of Multiple Vehicles}}}, doi = {{10.1145/3377930.3390146}}, year = {{2020}}, } @techreport{15367, abstract = {{n this paper, I review the empirical literature in the intersection of banks and corporate income taxation that emerged over the last two decades. To structure the included studies, I use a stakeholder approach and outline how corporate income taxation plays into the relation of banks and their four main stakeholders: bank regulators, customers, investors and tax authorities. My contribution to the literature is threefold: First, I contribute by providing, to the best of my knowledge, a first comprehensive review on this topic. Second, I point to areas for future research. Third, I deduce policy implications from the studies under review. In sum, the studies show that taxes distort banks’ pricing decisions, the relative attractiveness of debt and equity financing, the decision to report on or off the balance sheet and banks’ investment allocations. Empirical insights on how tax rules affect banks’ decision-making are helpful for policymakers to tailor suitable and sustainable tax legislation directed at banks. }}, author = {{Gawehn, Vanessa}}, keywords = {{corporate income taxes, banks, stakeholder approach, decision-making process}}, pages = {{34}}, publisher = {{SSRN}}, title = {{{Banks and Corporate Income Taxation: A Review}}}, 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}}, } @article{5671, abstract = {{Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers' decision processes in e-commerce shopping tasks.}}, author = {{Scholz, Michael and Dorner, Verena and Schryen, Guido and Benlian, Alexander}}, journal = {{European Journal of Operational Research}}, keywords = {{E-Commerce, Recommender System, Attribute Weights, Configuration System, Decision Support}}, number = {{1}}, pages = {{205 -- 215}}, publisher = {{Elsevier}}, title = {{{A configuration-based recommender system for supporting e-commerce decisions}}}, volume = {{259}}, year = {{2017}}, } @inproceedings{5678, abstract = {{Many academic disciplines - including information systems, computer science, and operations management - face scheduling problems as important decision making tasks. Since many scheduling problems are NP-hard in the strong sense, there is a need for developing solution heuristics. For scheduling problems with setup times on unrelated parallel machines, there is limited research on solution methods and to the best of our knowledge, parallel computer architectures have not yet been taken advantage of. We address this gap by proposing and implementing a new solution heuristic and by testing different parallelization strategies. In our computational experiments, we show that our heuristic calculates near-optimal solutions even for large instances and that computing time can be reduced substantially by our parallelization approach.}}, author = {{Rauchecker, Gerhard and Schryen, Guido}}, booktitle = {{Australasian Conference on Information Systems}}, keywords = {{scheduling, decision support, heuristic, high performance computing, parallel algorithms}}, pages = {{1--13}}, title = {{{High-Performance Computing for Scheduling Decision Support: A Parallel Depth-First Search Heuristic}}}, year = {{2015}}, }