[{"year":"2009","page":"23-38","intvolume":"        41","citation":{"ama":"Trautmann H, Mehnen J. Preference-based Pareto optimization in certain and noisy environments. <i>Engineering Optimization</i>. 2009;41(1):23-38. doi:<a href=\"https://doi.org/10.1080/03052150802347926\">10.1080/03052150802347926</a>","chicago":"Trautmann, Heike, and Jörn Mehnen. “Preference-Based Pareto Optimization in Certain and Noisy Environments.” <i>Engineering Optimization</i> 41, no. 1 (2009): 23–38. <a href=\"https://doi.org/10.1080/03052150802347926\">https://doi.org/10.1080/03052150802347926</a>.","ieee":"H. Trautmann and J. Mehnen, “Preference-based Pareto optimization in certain and noisy environments,” <i>Engineering Optimization</i>, vol. 41, no. 1, pp. 23–38, 2009, doi: <a href=\"https://doi.org/10.1080/03052150802347926\">10.1080/03052150802347926</a>.","apa":"Trautmann, H., &#38; Mehnen, J. (2009). Preference-based Pareto optimization in certain and noisy environments. <i>Engineering Optimization</i>, <i>41</i>(1), 23–38. <a href=\"https://doi.org/10.1080/03052150802347926\">https://doi.org/10.1080/03052150802347926</a>","mla":"Trautmann, Heike, and Jörn Mehnen. “Preference-Based Pareto Optimization in Certain and Noisy Environments.” <i>Engineering Optimization</i>, vol. 41, no. 1, Taylor &#38; Francis, 2009, pp. 23–38, doi:<a href=\"https://doi.org/10.1080/03052150802347926\">10.1080/03052150802347926</a>.","short":"H. Trautmann, J. Mehnen, Engineering Optimization 41 (2009) 23–38.","bibtex":"@article{Trautmann_Mehnen_2009, title={Preference-based Pareto optimization in certain and noisy environments}, volume={41}, DOI={<a href=\"https://doi.org/10.1080/03052150802347926\">10.1080/03052150802347926</a>}, number={1}, journal={Engineering Optimization}, publisher={Taylor &#38; Francis}, author={Trautmann, Heike and Mehnen, Jörn}, year={2009}, pages={23–38} }"},"issue":"1","title":"Preference-based Pareto optimization in certain and noisy environments","doi":"10.1080/03052150802347926","publisher":"Taylor & Francis","date_updated":"2023-10-16T13:59:31Z","volume":41,"author":[{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740","full_name":"Trautmann, Heike"},{"last_name":"Mehnen","full_name":"Mehnen, Jörn","first_name":"Jörn"}],"date_created":"2023-08-04T16:18:31Z","abstract":[{"text":" In this article a method for including a priori preferences of decision makers into multicriteria optimization problems is presented. A set of Pareto-optimal solutions is determined via desirability functions of the objectives which reveal experts’ preferences regarding different objective regions. An application to noisy objective functions is not straightforward but very relevant for practical applications. Two approaches are introduced in order to handle the respective uncertainties by means of the proposed preference-based Pareto optimization. By applying the methods to the original and uncertain Binh problem and a noisy single cut turning cost optimization problem, these approaches prove to be very effective in focusing on different parts of the Pareto front of the ori-ginal problem in both certain and noisy environments. ","lang":"eng"}],"status":"public","publication":"Engineering Optimization","type":"journal_article","language":[{"iso":"eng"}],"_id":"46417","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504"},{"doi":"10.1162/evco.2009.17.4.17403","title":"Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms","author":[{"last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"first_name":"T.","full_name":"Wagner, T.","last_name":"Wagner"},{"last_name":"Naujoks","full_name":"Naujoks, B.","first_name":"B."},{"first_name":"M.","full_name":"Preuss, M.","last_name":"Preuss"},{"full_name":"Mehnen, J.","last_name":"Mehnen","first_name":"J."}],"date_created":"2023-08-04T16:19:21Z","volume":17,"date_updated":"2024-06-10T11:55:57Z","citation":{"mla":"Trautmann, Heike, et al. “Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms.” <i>Evolutionary Computation</i>, vol. 17, no. 4, 2009, pp. 493–509, doi:<a href=\"https://doi.org/10.1162/evco.2009.17.4.17403\">10.1162/evco.2009.17.4.17403</a>.","short":"H. Trautmann, T. Wagner, B. Naujoks, M. Preuss, J. Mehnen, Evolutionary Computation 17 (2009) 493–509.","bibtex":"@article{Trautmann_Wagner_Naujoks_Preuss_Mehnen_2009, title={Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms}, volume={17}, DOI={<a href=\"https://doi.org/10.1162/evco.2009.17.4.17403\">10.1162/evco.2009.17.4.17403</a>}, number={4}, journal={Evolutionary Computation}, author={Trautmann, Heike and Wagner, T. and Naujoks, B. and Preuss, M. and Mehnen, J.}, year={2009}, pages={493–509} }","apa":"Trautmann, H., Wagner, T., Naujoks, B., Preuss, M., &#38; Mehnen, J. (2009). Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms. <i>Evolutionary Computation</i>, <i>17</i>(4), 493–509. <a href=\"https://doi.org/10.1162/evco.2009.17.4.17403\">https://doi.org/10.1162/evco.2009.17.4.17403</a>","ieee":"H. Trautmann, T. Wagner, B. Naujoks, M. Preuss, and J. Mehnen, “Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms,” <i>Evolutionary Computation</i>, vol. 17, no. 4, pp. 493–509, 2009, doi: <a href=\"https://doi.org/10.1162/evco.2009.17.4.17403\">10.1162/evco.2009.17.4.17403</a>.","chicago":"Trautmann, Heike, T. Wagner, B. Naujoks, M. Preuss, and J. Mehnen. “Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms.” <i>Evolutionary Computation</i> 17, no. 4 (2009): 493–509. <a href=\"https://doi.org/10.1162/evco.2009.17.4.17403\">https://doi.org/10.1162/evco.2009.17.4.17403</a>.","ama":"Trautmann H, Wagner T, Naujoks B, Preuss M, Mehnen J. Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms. <i>Evolutionary Computation</i>. 2009;17(4):493-509. doi:<a href=\"https://doi.org/10.1162/evco.2009.17.4.17403\">10.1162/evco.2009.17.4.17403</a>"},"intvolume":"        17","page":"493-509","year":"2009","issue":"4","publication_identifier":{"issn":["1063-6560"]},"language":[{"iso":"eng"}],"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46418","status":"public","abstract":[{"text":"In this paper, two approaches for estimating the generation in which a multi-objective evolutionary algorithm (MOEA) shows statistically significant signs of convergence are introduced. A set-based perspective is taken where convergence is measured by performance indicators. The proposed techniques fulfill the requirements of proper statistical assessment on the one hand and efficient optimisation for real-world problems on the other hand. The first approach accounts for the stochastic nature of the MOEA by repeating the optimisation runs for increasing generation numbers and analysing the performance indicators using statistical tools. This technique results in a very robust offline procedure. Moreover, an online convergence detection method is introduced as well. This method automatically stops the MOEA when either the variance of the performance indicators falls below a specified threshold or a stagnation of their overall trend is detected. Both methods are analysed and compared for two MOEA and on different classes of benchmark functions. It is shown that the methods successfully operate on all stated problems needing less function evaluations while preserving good approximation quality at the same time.","lang":"eng"}],"type":"journal_article","publication":"Evolutionary Computation"},{"year":"2008","place":"Naples, Italy","citation":{"mla":"Mehnen, J., and Heike Trautmann. “Robust Multi-Objective Optimisation of Weld Bead Geometry for Additive Manufacturing.” <i>Proceedings of the 6$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’08)</i>, edited by R Teti, Copyright C.O.C. Com. org. Conv., 2008.","bibtex":"@inproceedings{Mehnen_Trautmann_2008, place={Naples, Italy}, title={Robust Multi-objective Optimisation of Weld Bead Geometry for Additive Manufacturing}, booktitle={Proceedings of the 6$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’08)}, publisher={Copyright C.O.C. Com. org. Conv.}, author={Mehnen, J and Trautmann, Heike}, editor={Teti, R}, year={2008} }","short":"J. Mehnen, H. Trautmann, in: R. Teti (Ed.), Proceedings of the 6$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’08), Copyright C.O.C. Com. org. Conv., Naples, Italy, 2008.","apa":"Mehnen, J., &#38; Trautmann, H. (2008). Robust Multi-objective Optimisation of Weld Bead Geometry for Additive Manufacturing. In R. Teti (Ed.), <i>Proceedings of the 6$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’08)</i>. Copyright C.O.C. Com. org. Conv.","chicago":"Mehnen, J, and Heike Trautmann. “Robust Multi-Objective Optimisation of Weld Bead Geometry for Additive Manufacturing.” In <i>Proceedings of the 6$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’08)</i>, edited by R Teti. Naples, Italy: Copyright C.O.C. Com. org. Conv., 2008.","ieee":"J. Mehnen and H. Trautmann, “Robust Multi-objective Optimisation of Weld Bead Geometry for Additive Manufacturing,” in <i>Proceedings of the 6$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’08)</i>, 2008.","ama":"Mehnen J, Trautmann H. Robust Multi-objective Optimisation of Weld Bead Geometry for Additive Manufacturing. In: Teti R, ed. <i>Proceedings of the 6$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’08)</i>. Copyright C.O.C. Com. org. Conv.; 2008."},"title":"Robust Multi-objective Optimisation of Weld Bead Geometry for Additive Manufacturing","date_updated":"2023-10-16T14:00:12Z","publisher":"Copyright C.O.C. Com. org. Conv.","author":[{"full_name":"Mehnen, J","last_name":"Mehnen","first_name":"J"},{"first_name":"Heike","full_name":"Trautmann, Heike","id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282"}],"date_created":"2023-08-04T16:19:46Z","editor":[{"full_name":"Teti, R","last_name":"Teti","first_name":"R"}],"status":"public","publication":"Proceedings of the 6$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’08)","type":"conference","language":[{"iso":"eng"}],"_id":"46419","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504"},{"title":"A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing","date_updated":"2024-06-10T11:55:46Z","publisher":"Springer Berlin Heidelberg","date_created":"2023-08-04T16:20:35Z","author":[{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"last_name":"Ligges","full_name":"Ligges, Uwe","first_name":"Uwe"},{"first_name":"Jörn","full_name":"Mehnen, Jörn","last_name":"Mehnen"},{"last_name":"Preuss","full_name":"Preuss, Mike","first_name":"Mike"}],"year":"2008","place":"Berlin, Heidelberg","page":"825–836","citation":{"chicago":"Trautmann, Heike, Uwe Ligges, Jörn Mehnen, and Mike Preuss. “A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing.” In <i>Parallel Problem Solving from Nature – PPSN X</i>, edited by Günter Rudolph, Thomas Jansen, Nicola Beume, Simon Lucas, and Carlo Poloni, 825–836. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008.","ieee":"H. Trautmann, U. Ligges, J. Mehnen, and M. Preuss, “A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing,” in <i>Parallel Problem Solving from Nature – PPSN X</i>, 2008, pp. 825–836.","ama":"Trautmann H, Ligges U, Mehnen J, Preuss M. A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing. In: Rudolph G, Jansen T, Beume N, Lucas S, Poloni C, eds. <i>Parallel Problem Solving from Nature – PPSN X</i>. Springer Berlin Heidelberg; 2008:825–836.","apa":"Trautmann, H., Ligges, U., Mehnen, J., &#38; Preuss, M. (2008). A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing. In G. Rudolph, T. Jansen, N. Beume, S. Lucas, &#38; C. Poloni (Eds.), <i>Parallel Problem Solving from Nature – PPSN X</i> (pp. 825–836). Springer Berlin Heidelberg.","short":"H. Trautmann, U. Ligges, J. Mehnen, M. Preuss, in: G. Rudolph, T. Jansen, N. Beume, S. Lucas, C. Poloni (Eds.), Parallel Problem Solving from Nature – PPSN X, Springer Berlin Heidelberg, Berlin, Heidelberg, 2008, pp. 825–836.","bibtex":"@inproceedings{Trautmann_Ligges_Mehnen_Preuss_2008, place={Berlin, Heidelberg}, title={A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing}, booktitle={Parallel Problem Solving from Nature – PPSN X}, publisher={Springer Berlin Heidelberg}, author={Trautmann, Heike and Ligges, Uwe and Mehnen, Jörn and Preuss, Mike}, editor={Rudolph, Günter and Jansen, Thomas and Beume, Nicola and Lucas, Simon and Poloni, Carlo}, year={2008}, pages={825–836} }","mla":"Trautmann, Heike, et al. “A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing.” <i>Parallel Problem Solving from Nature – PPSN X</i>, edited by Günter Rudolph et al., Springer Berlin Heidelberg, 2008, pp. 825–836."},"publication_identifier":{"isbn":["978-3-540-87700-4"]},"language":[{"iso":"eng"}],"_id":"46420","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","editor":[{"first_name":"Günter","last_name":"Rudolph","full_name":"Rudolph, Günter"},{"last_name":"Jansen","full_name":"Jansen, Thomas","first_name":"Thomas"},{"first_name":"Nicola","full_name":"Beume, Nicola","last_name":"Beume"},{"first_name":"Simon","last_name":"Lucas","full_name":"Lucas, Simon"},{"first_name":"Carlo","last_name":"Poloni","full_name":"Poloni, Carlo"}],"abstract":[{"text":"A systematic approach for determining the generation number at which a specific Multi-Objective Evolutionary Algorithm (MOEA) has converged for a given optimization problem is introduced. Convergence is measured by the performance indicators Generational Distance, Spread and Hypervolume. The stochastic nature of the MOEA is taken into account by repeated runs per generation number which results in a highly robust procedure. For each generation number the MOEA is repeated a fixed number of times, and the Kolmogorow-Smirnov-Test is used in order to decide if a significant change in performance is gained in comparison to preceding generations. A comparison of different MOEAs on a problem with respect to necessary generation numbers becomes possible, and the understanding of the algorithm’s behaviour is supported by analysing the development of the indicator values. The procedure is illustrated by means of standard test problems.","lang":"eng"}],"status":"public","publication":"Parallel Problem Solving from Nature – PPSN X","type":"conference"},{"doi":"10.1109/CEC.2007.4424810","title":"Introducing user preference using Desirability Functions in Multi-Objective Evolutionary Optimisation of noisy processes","date_created":"2023-08-04T16:21:27Z","author":[{"last_name":"Mehnen","full_name":"Mehnen, Jorn","first_name":"Jorn"},{"first_name":"Heike","full_name":"Trautmann, Heike","id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282"},{"first_name":"Ashutosh","last_name":"Tiwari","full_name":"Tiwari, Ashutosh"}],"date_updated":"2023-10-16T14:00:44Z","citation":{"chicago":"Mehnen, Jorn, Heike Trautmann, and Ashutosh Tiwari. “Introducing User Preference Using Desirability Functions in Multi-Objective Evolutionary Optimisation of Noisy Processes.” In <i>2007 IEEE Congress on Evolutionary Computation</i>, 2687–94, 2007. <a href=\"https://doi.org/10.1109/CEC.2007.4424810\">https://doi.org/10.1109/CEC.2007.4424810</a>.","ieee":"J. Mehnen, H. Trautmann, and A. Tiwari, “Introducing user preference using Desirability Functions in Multi-Objective Evolutionary Optimisation of noisy processes,” in <i>2007 IEEE Congress on Evolutionary Computation</i>, 2007, pp. 2687–2694, doi: <a href=\"https://doi.org/10.1109/CEC.2007.4424810\">10.1109/CEC.2007.4424810</a>.","ama":"Mehnen J, Trautmann H, Tiwari A. Introducing user preference using Desirability Functions in Multi-Objective Evolutionary Optimisation of noisy processes. In: <i>2007 IEEE Congress on Evolutionary Computation</i>. ; 2007:2687-2694. doi:<a href=\"https://doi.org/10.1109/CEC.2007.4424810\">10.1109/CEC.2007.4424810</a>","apa":"Mehnen, J., Trautmann, H., &#38; Tiwari, A. (2007). Introducing user preference using Desirability Functions in Multi-Objective Evolutionary Optimisation of noisy processes. <i>2007 IEEE Congress on Evolutionary Computation</i>, 2687–2694. <a href=\"https://doi.org/10.1109/CEC.2007.4424810\">https://doi.org/10.1109/CEC.2007.4424810</a>","short":"J. Mehnen, H. Trautmann, A. Tiwari, in: 2007 IEEE Congress on Evolutionary Computation, 2007, pp. 2687–2694.","bibtex":"@inproceedings{Mehnen_Trautmann_Tiwari_2007, title={Introducing user preference using Desirability Functions in Multi-Objective Evolutionary Optimisation of noisy processes}, DOI={<a href=\"https://doi.org/10.1109/CEC.2007.4424810\">10.1109/CEC.2007.4424810</a>}, booktitle={2007 IEEE Congress on Evolutionary Computation}, author={Mehnen, Jorn and Trautmann, Heike and Tiwari, Ashutosh}, year={2007}, pages={2687–2694} }","mla":"Mehnen, Jorn, et al. “Introducing User Preference Using Desirability Functions in Multi-Objective Evolutionary Optimisation of Noisy Processes.” <i>2007 IEEE Congress on Evolutionary Computation</i>, 2007, pp. 2687–94, doi:<a href=\"https://doi.org/10.1109/CEC.2007.4424810\">10.1109/CEC.2007.4424810</a>."},"page":"2687-2694","year":"2007","publication_identifier":{"issn":["1941-0026"]},"language":[{"iso":"eng"}],"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46421","status":"public","abstract":[{"text":"Multi-objective evolutionary algorithms (MOEAs) are generally designed to find a well spread Pareto-front approximation. Often, only a small section of this front may be of practical interest. Desirability functions (DFs) are able to describe user preferences intuitively. Furthermore, DFs can be attached to any fitness function easily. This way, desirability functions can help in guiding MOEAs without introducing additional restrictions or changes to the algorithm. The application of noisy fitness functions is not straight forward but relevant to many real-world problems. Therefore, a variant of Harrington’s one-sided desirability function using expectations is introduced which takes noise into account. A deterministic strategy as well as the XSGA-II are used in combination with DF to solve a noisy Binh problem and a noisy cost estimation problem for turning processes.","lang":"eng"}],"type":"conference","publication":"2007 IEEE Congress on Evolutionary Computation"},{"status":"public","abstract":[{"text":"The concept of desirability is a means for complexity reduction of multivariate quality optimization. This paper provides a theoretical breakthrough regarding desirability indices, which application fields were formerly limited primarily by the lack of its distribution. Focussed are the distributions of Harrington’s desirability functions and different types of the desirability index.","lang":"eng"}],"type":"journal_article","publication":"Metrika","language":[{"iso":"eng"}],"user_id":"100740","_id":"46423","citation":{"chicago":"Trautmann, Heike, and C Weihs. “On the Distribution of the Desirability Index Using Harrington’s Desirability Function.” <i>Metrika</i> 63, no. 2 (2006): 207–213. <a href=\"https://doi.org/10.1007/s00184-005-0012-0\">https://doi.org/10.1007/s00184-005-0012-0</a>.","ieee":"H. Trautmann and C. Weihs, “On the Distribution of the Desirability Index using Harrington’s Desirability Function,” <i>Metrika</i>, vol. 63, no. 2, pp. 207–213, 2006, doi: <a href=\"https://doi.org/10.1007/s00184-005-0012-0\">10.1007/s00184-005-0012-0</a>.","ama":"Trautmann H, Weihs C. On the Distribution of the Desirability Index using Harrington’s Desirability Function. <i>Metrika</i>. 2006;63(2):207–213. doi:<a href=\"https://doi.org/10.1007/s00184-005-0012-0\">10.1007/s00184-005-0012-0</a>","apa":"Trautmann, H., &#38; Weihs, C. (2006). On the Distribution of the Desirability Index using Harrington’s Desirability Function. <i>Metrika</i>, <i>63</i>(2), 207–213. <a href=\"https://doi.org/10.1007/s00184-005-0012-0\">https://doi.org/10.1007/s00184-005-0012-0</a>","bibtex":"@article{Trautmann_Weihs_2006, title={On the Distribution of the Desirability Index using Harrington’s Desirability Function}, volume={63}, DOI={<a href=\"https://doi.org/10.1007/s00184-005-0012-0\">10.1007/s00184-005-0012-0</a>}, number={2}, journal={Metrika}, author={Trautmann, Heike and Weihs, C}, year={2006}, pages={207–213} }","short":"H. Trautmann, C. Weihs, Metrika 63 (2006) 207–213.","mla":"Trautmann, Heike, and C. Weihs. “On the Distribution of the Desirability Index Using Harrington’s Desirability Function.” <i>Metrika</i>, vol. 63, no. 2, 2006, pp. 207–213, doi:<a href=\"https://doi.org/10.1007/s00184-005-0012-0\">10.1007/s00184-005-0012-0</a>."},"intvolume":"        63","page":"207–213","year":"2006","issue":"2","doi":"10.1007/s00184-005-0012-0","title":"On the Distribution of the Desirability Index using Harrington’s Desirability Function","author":[{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740","full_name":"Trautmann, Heike"},{"last_name":"Weihs","full_name":"Weihs, C","first_name":"C"}],"date_created":"2023-08-04T16:22:48Z","volume":63,"date_updated":"2023-10-04T22:25:10Z"},{"editor":[{"first_name":"R","last_name":"Teti","full_name":"Teti, R"}],"status":"public","type":"conference","publication":"CIRP ICME ’06) — Proceedings of the 5$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering","language":[{"iso":"eng"}],"_id":"46422","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"place":"Ischia, Italy","year":"2006","citation":{"ieee":"J. Mehnen and H. Trautmann, “Integration of Expert’s Preferences in Pareto Optimization by Desirability Function Techniques,” in <i>CIRP ICME ’06) — Proceedings of the 5$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering</i>, 2006, pp. 293–298.","chicago":"Mehnen, J, and Heike Trautmann. “Integration of Expert’s Preferences in Pareto Optimization by Desirability Function Techniques.” In <i>CIRP ICME ’06) — Proceedings of the 5$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering</i>, edited by R Teti, 293–298. Ischia, Italy: C.O.C. Com. org. Conv. CIRP ICME ’06, 2006.","ama":"Mehnen J, Trautmann H. Integration of Expert’s Preferences in Pareto Optimization by Desirability Function Techniques. In: Teti R, ed. <i>CIRP ICME ’06) — Proceedings of the 5$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering</i>. C.O.C. Com. org. Conv. CIRP ICME ’06; 2006:293–298.","mla":"Mehnen, J., and Heike Trautmann. “Integration of Expert’s Preferences in Pareto Optimization by Desirability Function Techniques.” <i>CIRP ICME ’06) — Proceedings of the 5$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering</i>, edited by R Teti, C.O.C. Com. org. Conv. CIRP ICME ’06, 2006, pp. 293–298.","short":"J. Mehnen, H. Trautmann, in: R. Teti (Ed.), CIRP ICME ’06) — Proceedings of the 5$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering, C.O.C. Com. org. Conv. CIRP ICME ’06, Ischia, Italy, 2006, pp. 293–298.","bibtex":"@inproceedings{Mehnen_Trautmann_2006, place={Ischia, Italy}, title={Integration of Expert’s Preferences in Pareto Optimization by Desirability Function Techniques}, booktitle={CIRP ICME ’06) — Proceedings of the 5$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering}, publisher={C.O.C. Com. org. Conv. CIRP ICME ’06}, author={Mehnen, J and Trautmann, Heike}, editor={Teti, R}, year={2006}, pages={293–298} }","apa":"Mehnen, J., &#38; Trautmann, H. (2006). Integration of Expert’s Preferences in Pareto Optimization by Desirability Function Techniques. In R. Teti (Ed.), <i>CIRP ICME ’06) — Proceedings of the 5$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering</i> (pp. 293–298). C.O.C. Com. org. Conv. CIRP ICME ’06."},"page":"293–298","title":"Integration of Expert’s Preferences in Pareto Optimization by Desirability Function Techniques","date_updated":"2023-10-16T14:00:58Z","publisher":"C.O.C. Com. org. Conv. CIRP ICME ’06","author":[{"first_name":"J","full_name":"Mehnen, J","last_name":"Mehnen"},{"full_name":"Trautmann, Heike","id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"}],"date_created":"2023-08-04T16:22:05Z"}]