@inproceedings{KellnerSchroeder2022, author = {Kellner, Florian and Schr{\"o}der, Nadine}, title = {Location Planning \& Analysis Using Uncertain Data}, series = {2021 International Conference on Decision Aid Sciences and Application (DASA)}, booktitle = {2021 International Conference on Decision Aid Sciences and Application (DASA)}, doi = {10.1109/DASA53625.2021.9682323}, pages = {720 -- 723}, year = {2022}, abstract = {In today's big data era, a humongous amount of data are collected from various sources. In many cases, these data are incomplete, imprecise, and uncertain. An illustrative example is the OpenStreetMap project, where users all over the world contribute data on a more or less precise and complete level. This research shows whether these data are suited to support management decisions. A real-world example demonstrates the extent to which location decisions of a fast-food restaurant chain can be reproduced using techniques from the field of advanced analytics. The problem deals with classifying potential locations and comparing the predicted locations with the actual ones. The data used for this example are retrieved from the OpenStreetMap project. We find that the OpenStreetMap data are generally suitable for predicting location decisions. However, the choice of the data analytics technique is crucial. In our illustrative example case, boosted trees resulted in the best forecast, thereby outperforming neural networks, classic trees, and logit models.}, language = {en} } @inproceedings{KellnerLienlandUtz2020, author = {Kellner, Florian and Lienland, Bernhard and Utz, Sebastian}, title = {Optimal engine technology mix in a low carbon economy}, series = {2020 International Conference on Decision Aid Sciences and Application (DASA)}, booktitle = {2020 International Conference on Decision Aid Sciences and Application (DASA)}, doi = {10.1109/DASA51403.2020.9317094}, pages = {95 -- 98}, year = {2020}, abstract = {Environmental regulations force automotive companies to modify the powertrain technology portfolio offered to the customer to comply with greenhouse gas (GHG) emission targets. Automotive companies, in turn, are faced with the decision of finding the right powertrain technology portfolio because the selection of a particular technology portfolio affects different company targets at the same time. What makes this decision even more interesting is the fact that future market shares of the different technologies are uncertain. With its numerous objectives, this challenge requires multi-criteria decision-making techniques to identify the optimal powertrain technology portfolio. The objective of this research is to present a new decision support approach for assembling optimal powertrain technology portfolios while making decision-makers aware of the trade-offs between the achievable market share, the market share risk, and the GHG emissions generated by the selected vehicle fleet. The proposed approach combines `a posteriori' decision-making, multi-objective optimization, and the Markowitz portfolio theory. In an application case, the outlooks of selected market studies are fed into the proposed decision support system. The result is a visualization and analysis of the current real-world decision-making problem faced by many automotive companies. Interesting findings of this research include that for the assumed GHG restrictions in place in 2030, there exists no optimal powertrain technology portfolio that is not composed of at least 20\% of electric vehicles.}, language = {en} } @inproceedings{Kellner2015, author = {Kellner, Florian}, title = {Analysing the greenhouse gas sensitivity of distribution networks}, series = {Global Cleaner Production and Sustainable Consumption Conference}, booktitle = {Global Cleaner Production and Sustainable Consumption Conference}, year = {2015}, language = {en} } @inproceedings{Kellner2017, author = {Kellner, Florian}, title = {Visual decision support for the multi-objective supplier selection problem}, series = {International Conference on Multiple Objective Programming and Goal Programming (MOPGP)}, booktitle = {International Conference on Multiple Objective Programming and Goal Programming (MOPGP)}, year = {2017}, language = {en} } @inproceedings{Kellner2017, author = {Kellner, Florian}, title = {Multi-objective supplier portfolio configuration under supply risk and sustainability considerations}, series = {International Conference on Operations Research (OR2017)}, booktitle = {International Conference on Operations Research (OR2017)}, year = {2017}, language = {en} } @inproceedings{Kellner2017, author = {Kellner, Florian}, title = {Supplier portfolio optimization}, series = {Ipsera Conference Hungary 2017}, booktitle = {Ipsera Conference Hungary 2017}, year = {2017}, language = {en} } @inproceedings{Kellner2013, author = {Kellner, Florian}, title = {GHG network analysis FMCG}, series = {Logistikmanagement 2013}, booktitle = {Logistikmanagement 2013}, year = {2013}, language = {en} } @inproceedings{Kellner2010, author = {Kellner, Florian}, title = {Changes in the field of FMCG distribution logistics and their impacts on the cost optimal distribution network - A simulation based case study}, series = {15th European Logistics Association Doctorate Workshop}, booktitle = {15th European Logistics Association Doctorate Workshop}, year = {2010}, language = {en} } @inproceedings{KellnerUtz2023, author = {Kellner, Florian and Utz, Sebastian}, title = {Using Inverse Optimization to Discover Sustainability Priorities in Purchasing Decisions}, series = {International Conference on Operations Research 2023}, booktitle = {International Conference on Operations Research 2023}, year = {2023}, language = {en} } @inproceedings{KellnerUtz2023, author = {Kellner, Florian and Utz, Sebastian}, title = {Exploring Sustainability Priorities in Purchasing Decisions Based on Inverse Optimization}, series = {NAMA Conference 2023}, booktitle = {NAMA Conference 2023}, year = {2023}, language = {en} }