University of Bremen - Winter 2025/2026

60 University of Bremen - Winter 2025/2026 https://www.uni-bremen.de/en/cslog/teaching/winter-2025-2026 This is the homepage of the working group CSLog - "Informatikmethoden zur adaptiven Steuerung in Logistik und Produktion" lead by Prof. Dr. Nicole Negow. en University of Bremen Sat, 14 Mar 2026 10:17:37 +0100 Sat, 14 Mar 2026 10:17:37 +0100 University of Bremen content-685551 Fri, 06 Mar 2026 14:40:33 +0100 Algorithmentheorie https://www.uni-bremen.de/en/cslog/teaching/winter-2025-2026#c685551 <h4>Wintersemester 2025/26, 3 SWS, 4,5 CP 03-BE-699.11 (03-IBGT-AT), <a class="external-link" href="https://elearning.uni-bremen.de/dispatch.php/course/details?sem_id=206768d917fb6a58e83f65ea9d56b4c3&amp;again=yes" target="_blank" title="Opens external link in new window">Link zu StudIP</a></h4> Algorithmen bilden eine der wichtigsten Grundlagen der Informatik. Anschaulich beschreibt ein Algorithmus eine Vorgehensweise um ein Problem zu lösen. Somit bilden Algorithmen eine Grundlage der Programmierung, sind aber unabhängig von der konkreten Programmiersprache und Umsetzung. Algorithmen sind so vielfältig wie ihre Anwendungen, darum ist es umso wichtiger die fundamentalen Prinzipien des effizienten Algorithmenentwurfs und in den wichtigsten Problembereichen die grundlegenden Lösungsverfahren zu kennen. Die Vorlesung hat zum Ziel diese fundamentalen Prinzipien des Algorithmenentwurfs zu vermitteln. Die Prinzipien werden anhand klassischer Algorithmen für wichtige Probleme illustriert und eingeübt. Auf der theoretischen Seite werden die Grundlagen abstrakter Maschinenmodelle, formale Korrektheitsbeweise und Laufzeitanalyse vermittelt. Das erworbene Wissen ermöglicht es den Studierenden für ein gegebenes algorithmisches Problem verschiedene Lösungsansätze bezüglich ihrer Effizienz zu beurteilen, den am besten geeigneten Ansatz zur Lösung auszuwählen und seine Korrektheit zu beweisen. • Algorithmenparadigmen: Greedy, Divide-and-Conquer, Dynamische Programmierung • Sortierverfahren • Grundlegende Begriffe der Graphentheorie • Graphenprobleme: Kürzeste-Wege, minimale aufspannende Bäume, maximale Netzwerkflüsse Dozentin:&nbsp; <a href="mailto:nicole.megow@uni-bremen.de" title="Öffnet ein Fenster zum Versenden der E-Mail">Prof. Dr. Nicole Megow</a> Content content-685547 Fri, 06 Mar 2026 14:40:33 +0100 Operations Research https://www.uni-bremen.de/en/cslog/teaching/winter-2025-2026#c685547 <h4>Wintersemester 2025/2026, 6CP 03-IBAT-OR, <a class="external-link" href="https://elearning.uni-bremen.de/dispatch.php/course/details?sem_id=d71b0ec13738900460bba05adfe122cf&amp;again=yes" target="_blank" title="Opens external link in new window">Link zu StudIP</a></h4> Moderne betriebliche Informationssysteme nutzen verschiedene quantitative Verfahren aus der Informatik und Mathematik, um Planungs- und Entscheidungsprozesse zu unterstützen. So lassen sich viele praktische Fragestellungen als (ganzzahlige) lineare Optimierungsprobleme formulieren, zum Beispiel Warenfluss und Planung von Produktionsprozessen in der Logistik, Portfoliotheorie und Risikomanagement in der Finanzwelt sowie Netzwerkdesign und Routing in der Telekommunikation. Die Vorlesung gibt eine Einführung in die Modellierung betrieblicher Entscheidungsprobleme und grundlegende Methoden der linearen und ganzzahligen linearen Optimierung. Themen sind: Mathematische Modellierung praktischer Fragestellungen (Entscheidungs-, Planungs- und Optimierungsprobleme), Struktur und Geometrie linearer Programme, Simplex- und Ellipsoidmethode, Dualität; Einführung grundlegender kombinatorische Algorithmen für Graphen- und Netzwerkflussprobleme; Methoden zum Lösen ganzzahliger linearer Probleme, unter anderem dynamische Programmierung, Branch-and Bound Methoden und Greedy Verfahren. Dozentinnen:&nbsp; <a href="mailto:nicole.megow@uni-bremen.de" title="Öffnet ein Fenster zum Versenden der E-Mail">Prof. Dr. Nicole Megow</a>, <a class="e-mail-link" href="mailto:morell@uni-bremen.de" title="Opens a window for sending an e-mail">Dr. Sarah Morell</a> Tutorin:&nbsp;<a class="e-mail-link" href="mailto:morell@uni-bremen.de" title="Opens a window for sending an e-mail">Dr. Sarah Morell</a> Zeiten und Format:&nbsp; <ul> <li>Vorlesung&nbsp;donnerstags von 12 bis 14 Uhr im Raum MZH 1090,</li> <li>Übungen mittwochs, wahlweise von 10 bis 12 Uhr oder 14 bis 16 Uhr im Raum MZH&nbsp;1470.&nbsp;</li> </ul> Die erste Veranstaltung ist die Vorlesung am 16.10.2025. Leistungsnachweis: Es werden wöchentlich Übungsblätter veröffentlicht, die in Kleingruppen bearbeitet werden müssen und deren Abgabe verpflichtend ist. Zur Prüfungszulassung müssen mindestens 50% der insgesamt erreichbaren&nbsp;Punkte aller Übungsblätter erreicht werden. Eine Klausur ist im Februar geplant, weitere Informationen folgen in Kürze. Literatur: <ul> <li>[W] Winston, A.: Operations Research, Algorithms and Applications, Whiley &amp; Sons, Duxbury Press, 2003.</li> <li>[NSW] Nickel, Stein, Waldmann: Operations Research, Springer Gabler, 2. Auflage, 2014.</li> <li>[DDKS] Domschke, W.; Drexl, A.; Klein, R.; Scholl, A.: Einführung in Operations Research, 5. Auflage, Springer, 2015.</li> <li>[BT] Bertsimas, Tsitsiklis: Introduction to Linear Optimization, Athena Scientific, 1997.</li> <li>[GKT] Guenin, Könemann, Tuncel: A Gentle Introduction to Optimization, Cambridge University Press, 2014.</li> </ul> Content content-686492 Fri, 06 Mar 2026 14:40:33 +0100 Algorithms and Uncertainty https://www.uni-bremen.de/en/cslog/teaching/winter-2025-2026#c686492 <h4>Wintersemester 2025/26,&nbsp;6 or 9CP 03-IMAT-AU (03-ME-602.99c), Link zu StudIP</h4> A key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data.&nbsp; In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class&nbsp;Algorithms and Uncertainty&nbsp;will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of&nbsp;online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ&nbsp;robust optimization&nbsp;or&nbsp;stochastic optimization.&nbsp;In robust optimization, the aim is to optimize the worst-case of&nbsp;all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Lecturer:&nbsp;<abbr title="Professorin">Prof. </abbr> <abbr title="Doktor"> Dr.</abbr> Franziska Eberle Format: lectures twice a week with integrated, interactive exercise sessions&nbsp;(6 CP), and an additional seminar style&nbsp;course work (+3 Extra CP) Examination:&nbsp;individual oral exam; as admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. Content content-685545 Fri, 06 Mar 2026 14:40:33 +0100 Project OPTIMIZE https://www.uni-bremen.de/en/cslog/teaching/winter-2025-2026#c685545 <h3>Algorithmen und Optimierung für Nachhaltigkeit</h3> <h4>Wintersemester 2025/26 (Oktober 25 - März 26), 15CP 03-IBPJ-OPTI Bachelorprojekt für die Studiengänge Informatik und Wirtschaftsinformatik</h4> Dieses Projekt widmet sich praxisnahen Herausforderungen im Bereich der Nachhaltigkeit und untersucht, wie Algorithmen und Optimierungstechniken zur Lösung globaler Probleme beitragen können. Es stehen drängende Fragestellungen wie nachhaltige Transportlogistik, effiziente Lebensmittellogistik und die Optimierung von Energieverbrauch in Haushalten und Unternehmen im Fokus. Ziel des Projekts ist es, reale Probleme zu analysieren, mathematische Modelle zu entwickeln, passende Algorithmen zu entwerfen und diese zu implementieren und zu evaluieren, um nachhaltige und zukunftsorientierte Lösungen in verschiedenen Anwendungsbereichen zu fördern. Dozenten:&nbsp; <a class="internal-link" href="t3://page?uid=18311" title="Öffnet internen Link in aktuellem Fenster"><abbr title="Professorin">Prof.&nbsp;</abbr><abbr title="Doktor">Dr.</abbr> Nicole Megow</a> und <a class="internal-link" href="t3://page?uid=68004" title="Öffnet internen Link in aktuellem Fenster"><abbr title="Doktor"> Dr.</abbr> Moritz Buchem</a> Studip: <a class="external-link" href="https://elearning.uni-bremen.de/dispatch.php/course/details?sem_id=2dcdc72285ac203048b1ab243913c9f7&amp;again=yes" target="_blank" title="Öffnet externen Link in neuem Fenster">Link for Details</a> Overview (engl.): This project focuses on practical challenges in the field of sustainability and explores how algorithms and optimization techniques can contribute to solving global problems. The focus is on pressing issues such as sustainable transportation logistics, efficient food logistics, and optimizing energy consumption in households and businesses. The goal of the project is to analyze real-world problems, develop mathematical models, design suitable algorithms, and implement and evaluate these solutions to promote sustainable and forward-looking approaches across various application areas. Content content-467727 Wed, 04 Mar 2026 22:36:40 +0100 Algorithms and Uncertainty https://www.uni-bremen.de/en/cslog/teaching/winter-2025-2026#c467727 <h4>Wintersemester 2022/23,&nbsp;6 or 9CP 03-IMAT-AU (03-ME-602.99c),&nbsp;<a class="externalLink" href="https://elearning.uni-bremen.de/dispatch.php/course/details?sem_id=2b0aea46ae664c9038cb224b23b0bbae&amp;again=yes" target="_blank" title="Opens external link in new window">StudIP</a></h4> A key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning. However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data.&nbsp; In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class Algorithms and Uncertainty&nbsp;will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models. Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario. In the cases where previous history can be used to model the upcoming data, we often employ robust optimization&nbsp;or stochastic optimization.&nbsp;In robust optimization, the aim is to optimize the worst-case of&nbsp;all possible realizations of the input data. Hence, this model is rather conservative. In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value. Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions&nbsp;in order to improve guaranteed algorithms. Lecturers: <a class="internalLink" href="t3://page?uid=18311" title="Opens internal link in current window">Prof. Dr. Nicole Megow</a>,&nbsp;<a class="internalLink" href="t3://page?uid=48411" title="Opens internal link in current window">Dr. Felix Hommelsheim</a> Format: lectures twice a week with integrated, interactive exercise sessions&nbsp;(6 CP), additional seminar style&nbsp;course work (9 CP) Examination:&nbsp;individual oral exam; as admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term. &nbsp; Content