Professor
Forschungsgruppe Quantitative Methoden in der Logistik
Leonardo-Campus 3
48149
Münster
Short-URL: erc.is/p/meisel
Leonardo-Campus 3
48149
Münster
Merfeld, T., & Meisel, S. (2022). The Role and Value of Service Orchestration in Smart Grid Prosumer Service Systems. In Beck, R., Petcu, D., & Fotache, M. (Eds.), ECIS 2022 Research Papers (pp. 1–16). Atlanta, GA: Association for Information Systems.
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Merfeld, T., & Meisel, S. (2022). Economic real-time energy trading services for electric vehicles with uncertain mobility demand. Journal of Cleaner Production, 340(130639).
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Wölck, M., & Meisel, S. (2022). Branch-and-Price Approaches for Real-Time Vehicle Routing with Picking, Loading, and Soft Time Windows. INFORMS Journal on Computing, 34(4), 2192–2211.
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Meisel, S., & Merfeld, T. (2020). Assessing the Financial Value of Real-Time Energy Trading Services for Privately Owned Non-Commercial Electric Vehicles. Transportation Research Part D: Transport and Environment, 80.
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Meisel, S. (2019). All Dynamic Decision Problems are Created with Equal Structure. In Bergener, K., Räckers, M., & Stein, A. (Eds.), The Art of Structuring (pp. 113–123). Cham, CH: Springer.
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Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019). Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm. In Deb, K., Goodman, E., Coello, C. C. A., Klamroth, K., Miettinen, K., Mostaghim, S., & Reed, P. (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (pp. 516–528). Lecture Notes in Computer Science: Vol. 11411. Springer International Publishing.
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Priekule, L., & Meisel, S. (2019). Lookahead Contraction Policies for Bayesian Ranking and Selection with Pairwise Comparisons (Best Contributed Theory Paper Finalist). In Mustafee, N., Bae, K.-H., Lazarova-Molnar, S., Rabe, M., Szabo, C., Haas, P., & Son, Y.-J. (Eds.), Proceedings of the 2019 Winter Simulation Conference (pp. 3380–3391). Piscataway, NJ: Institute of Electrical and Electronics Engineers, Inc.
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Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018). Local Search Effects in Bi-Objective Orienteering. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18), Kyoto, Japan, 585–592.
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Ball, R., Branke, J., & Meisel, S. (2018). Optimal Sampling for Simulated Annealing under Noise. INFORMS Journal on Computing, 30(1), 200–215.
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Meisel, S., & Merfeld, T. (2018). Economic Incentives for the Adoption of Electric Vehicles: A Classification and Review of E-Vehicle Services. Transportation Research Part D: Transport and Environment, 2018(65), 264–287.
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Meisel, S., & Powell, W. (2017). Dynamic Decision Making in Energy Systems with Storage and Renewable Energy Sources. In Fichtner, W., Heuveline, V., & Leibfried, T. (Eds.), Advances in Energy System Optimization (pp. 87–101). Springer.
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Priekule, L., & Meisel, S. (2017). A Bayesian Ranking and Selection Problem with Pairwise Comparisons. In Chan, V., D'Ambrogio, A., Zacharewicz, G., & Mustafee, N. (Eds.), Proceedings of the 2017 Winter Simulation Conference (pp. 2149–2160). Piscataway, NJ: Institute of Electrical and Electronics Engineers, Inc.
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Collado, R., Meisel, S., & Priekule, L. (2017). Risk-Averse Stochastic Path Detection. European Journal of Operational Research, 260(1), 195–211.
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Meisel, S., & Merfeld, T. (2016). Efficient use of Renewable Energy with Storage and E-mobility Service. In Proceedings of the Karlsruhe Service Summit Research Workshop 2016, Karlsruhe, Deutschland.
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Powell, W., & Meisel, S. (2016). Tutorial on Stochastic Optimization in Energy I: Modeling and Policies. IEEE Transactions on Power Systems, 31(2), 1459–1467.
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Powell, W., & Meisel, S. (2016). Tutorial on Stochastic Optimization in Energy II: An Energy Storage Illustration. IEEE Transactions on Power Systems, 31(2), 1468–1475.
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Meisel, S. (2016). Approximate Dynamic Programming. In Gronau, N., Becker, J., Sinz, , E, , Suhl, L., & Leimeister, J.-M. (Eds.), Enzyklopädie der Wirtschaftsinformatik (pp. NN). GITO Verlag.
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Grimme, C., Meisel, S., Trautmann, H., Rudolph, G., & Wölck, M. (2015). Multi-Objective Analysis of Approaches to Dynamic Routing Of a Vehicle. In Proceedings of the European Conference On Information Systems, Münster, Germany.
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Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., & Trautmann, H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. In Proceedings of the Genetic and Evolutionary Computation Conference, Madrid, Spain, 425–432.
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Meisel, S., & Wölck, M. (2015). Evaluating Idle Time Policies for Real-Time Routing of a Service Vehicle. In Proceedings of the 23rd European Conference on Information Systems, Münster, Deutschland.
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Meisel, S. (2015). Antizipierende Optimierung. WISU — Das Wirtschaftsstudium, 8-9/15.
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Ricker, V., Meisel, S., & Mattfeld, D. (2012). Optimierung von stationsbasierten Bike-Sharing Systemen. In Proceedings of the Multikonferenz Wirtschaftsinformatik 2012, Braunschweig.
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Ehmke, J., Meisel, S., & Mattfeld, D. (2012). Floating Car Based Travel Times for City Logistics. Transportation Research Part C: Emerging Technologies, 21(1), 338–352.
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Meisel, S. (2011). Anticipatory Optimization for Dynamic Decision Making. Operations Research/Computer Science Interfaces: Vol. 51. New York: Springer.
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Meisel, S., Suppa, U., & Mattfeld, D. (2011). Serving Multiple Urban Areas with Stochastic Customer Requests. In Kreowski, H.-J., Scholz-Reiter, B., & Thoben, K.-D. (Eds.), Dynamics in Logistics (pp. 59–68). Springer Verlag.
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Suppa, U., Meisel, S., & Mattfeld, D. (2011). Flottenmanagement für Vor-Ort-Kundendienste. In Siepermann, C., & Eley, M. (Eds.), Logistik — Gestern, heute, morgen (pp. 107–122).
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Suppa, U., Meisel, S., & Mattfeld, D. (2011). GRASP based Vehicle Routing for After Sales Services. In Proceedings of the MIC 2011, Udine, Italien.
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Ehmke, J., Meisel, S., & Mattfeld, D. (2010). Floating Car Data Based Analysis of Urban Travel Times for the Provision of Traffic Quality. In Barceló, J., & Kuwahara, M. (Eds.), Traffic Data Collection and its Standardization (pp. 129–150). International Series in Operations Research & Management Science: Vol. 144. New York: Springer Verlag.
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Meisel, S., & Mattfeld, D. (2010). Synergies of Operations Research and Data Mining. European Journal of Operational Research, 206(1), 1–10.
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Meisel, S., Suppa, U., & Mattfeld, D. (2009). GRASP based Approximate Dynamic Programming for Dynamic Routing of a Vehicle. In Voss, S., & Caserta, M. (Eds.), MIC 2009 Post-Conference Volume (pp. 1–9). Springer Verlag. (online first)
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Meisel, S., Suppa, U., & Mattfeld, D. (2009). Serving Multiple Urban Areas with Stochastic Customer Requests. In Proceedings of the International Conference on Dynamics in Logistics 2009, Bremen.
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Meisel, S., Suppa, U., & Mattfeld, D. (2009). GRASP based Approximate Dynamic Programming for Dynamic Routing of a Vehicle. In Proceedings of the VIII Metaheuristic International Conference 2009, Hamburg.
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Ehmke, J., Meisel, S., Engelmann, S., & Mattfeld, D. (2009). Data Chain Management for Planning in City Logistics. International Journal of Data Mining, Modelling and Management, 1(4), 335–356.
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Ehmke, J., & Meisel, S. (2008). Ermittlung dynamischer Fahrzeiten für die City-Logistik. In Mattfeld, D., Voss, S., Suhl, L., & Günther, H.-O. (Eds.), Informations- und Kommunikationssysteme in Supply Chain Management, Logistik und Transport (pp. 107–122). Paderborn.
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Ehmke, J., Meisel, S., & Mattfeld, D. (2008). Floating Car Data Based Analysis of Urban Travel Times for the Provision of Traffic Quality. In Proceedings of the International Workshop on Traffic Data Collection and its Standardization IWTDCS 08, Barcelona.
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Branke, J., Meisel, S., & Schmidt, C. (2008). Simulated Annealing in the Presence of Noise. Journal of Heuristics, 14(6), 627–654.
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Ehmke, J., & Meisel, S. (2008). Charakterisierung des städtischen Straßenverkehrs mit Floating Car Data und Data Mining. Straßenverkehrstechnik, 52(10), 613–620.
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Meisel, S., & Mattfeld, D. (2007). Integration of Data Mining and Operations Research. In Wang, J. (Ed.), Encyclopedia of Data Warehousing and Mining (2., pp. 1046–1052).
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Meisel, S., & Mattfeld, D. (2007). Synergies of Data Mining and Operations Research. In Proceedings of the HICCS 40, Hawaii.
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