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    <title language="eng">Location Planning &amp; Analysis Using Uncertain Data</title>
    <abstract language="deu">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.</abstract>
    <parentTitle language="eng">2021 International Conference on Decision Aid Sciences and Application (DASA)</parentTitle>
    <identifier type="doi">10.1109/DASA53625.2021.9682323</identifier>
    <enrichment key="opus.import.data">@inproceedingsepub51513, pages = 720–723, journal = IEEE Xplore, booktitle = 2021 International Conference on Decision Aid Sciences and Application (DASA), title = Location Planning &amp; Analysis Using Uncertain Data, month = Januar, publisher = IEEE, year = 2022, author = Florian Kellner and Nadine Schröder, 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., url = https://epub.uni-regensburg.de/51513/</enrichment>
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    <author>Florian Kellner</author>
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    <title language="eng">Optimal engine technology mix in a low carbon economy</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2020 International Conference on Decision Aid Sciences and Application (DASA)</parentTitle>
    <identifier type="doi">10.1109/DASA51403.2020.9317094</identifier>
    <enrichment key="opus.import.data">@articleepub44436, pages = 95–98, journal = IEEE Xplore, note = 2020 International Conference on Decision Aid Sciences and Application (DASA), Sakheer, Bahrain, 2020. - 8-9 Nov. 2020 - elect. ISBN 978-1-7281-9677-0, title = Optimal engine technology mix in a low carbon economy, publisher = IEEE, year = 2020, author = Florian Kellner and Bernhard Lienland and Sebastian Utz, url = https://epub.uni-regensburg.de/44436/, 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.</enrichment>
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    <title language="eng">Analysing the greenhouse gas sensitivity of distribution networks</title>
    <parentTitle language="eng">Global Cleaner Production and Sustainable Consumption Conference</parentTitle>
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    <title language="eng">Visual decision support for the multi-objective supplier selection problem</title>
    <parentTitle language="eng">International Conference on Multiple Objective Programming and Goal Programming (MOPGP)</parentTitle>
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    <title language="eng">Multi-objective supplier portfolio configuration under supply risk and sustainability considerations</title>
    <parentTitle language="eng">International Conference on Operations Research (OR2017)</parentTitle>
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    <title language="eng">Supplier portfolio optimization</title>
    <parentTitle language="eng">Ipsera Conference Hungary 2017</parentTitle>
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    <title language="eng">GHG network analysis FMCG</title>
    <parentTitle language="eng">Logistikmanagement 2013</parentTitle>
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    <title language="eng">Changes in the field of FMCG distribution logistics and their impacts on the cost optimal distribution network - A simulation based case study</title>
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    <title language="eng">Using Inverse Optimization to Discover Sustainability Priorities in Purchasing Decisions</title>
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