@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} } @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} } @article{KellnerUtz2019, author = {Kellner, Florian and Utz, Sebastian}, title = {Sustainability in supplier selection and order allocation: combining integer variables with Markowitz portfolio theory}, series = {Journal of Cleaner Production}, volume = {214}, journal = {Journal of Cleaner Production}, doi = {10.1016/j.jclepro.2018.12.315}, pages = {462 -- 474}, year = {2019}, abstract = {This research presents a decision support methodology for the multi-criteria supplier selection and order allocation problem. The proposed approach supports purchasing managers in assembling mid-term supplier portfolios while making them aware of the trade-offs between the supplier sustainability, the purchasing costs, and the overall supply risk. First, we propose a multi-objective optimization model with three objectives: to maximize the supplier sustainability, to select the supplier portfolio with the lowest purchasing costs, and to minimize the supply risk. Our model extends existing mathematical approaches that follow the portfolio theory fathered by H. Markowitz by integrating the aspect 'risk' into the supplier selection problem. Secondly, since we allow for integer variables in our model—in contrast to the classical Markowitz portfolio theory—we use the ε-constraint method to visualize the efficient surface. The possibility of considering the non-dominated set of supplier portfolios is advantageous for purchasing managers as they gain a picture of the different optimal supplier portfolios and are able to analyze the trade-offs between the different purchasing goals before making a decision. Finally, we illustrate the applicability of the proposed methodology in a real-world supplier selection and order allocation case from the automotive industry. In the example case, we identify 1754 optimal supplier portfolios that may be assembled based on the eight available suppliers. Our analyses show that each optimal portfolio consists of two suppliers, with one specific supplier being included in each portfolio. Furthermore, four suppliers are not part of any optimal solution.}, language = {en} } @article{KellnerLienlandUtz2019, author = {Kellner, Florian and Lienland, Bernhard and Utz, Sebastian}, title = {An a posteriori decision support methodology for solving the multi-criteria supplier selection problem}, series = {European Journal of Operational Research}, volume = {272}, journal = {European Journal of Operational Research}, number = {2}, doi = {10.1016/j.ejor.2018.06.044}, pages = {505 -- 522}, year = {2019}, abstract = {This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate 'risk' into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company's strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.}, 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} } @article{KellnerLienlandUtz2021, author = {Kellner, Florian and Lienland, Bernhard and Utz, Sebastian}, title = {A multi-criteria decision-making approach for assembling optimal powertrain technology portfolios in low GHG emission environments}, series = {Journal of Industrial Ecology}, volume = {25}, journal = {Journal of Industrial Ecology}, number = {6}, doi = {10.1111/jiec.13148}, pages = {1412 -- 1429}, year = {2021}, abstract = {Environmental regulations force car manufacturers to renew the powertrain technology portfolio offered to the customer to comply with greenhouse gas (GHG) emission targets. In turn, automotive companies face the task of identifying the "right" powertrain technology portfolio consisting of, for example, internal combustion engines and electric vehicles, because the selection of a particular powertrain technology portfolio affects different company targets simultaneously. What makes this decision even more challenging is that future market shares of the different technologies are uncertain. Our research presents a new decision-support approach for assembling optimal powertrain technology portfolios while making decision-makers aware of the trade-offs between the achievable profit, 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 with multi-objective optimization. In an application case, we feed the outlooks of selected market studies 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. Our findings indicate that for the proposed GHG restriction at work in 2030 in the European Union, no optimal powertrain technology portfolio with less than 35\% of vehicles equipped with an electric motor exists.}, language = {en} }