Dr.-Ing. Katharina Glock

Stellv. Abteilungsleiter

Werdegang

Katharina Glock studierte Wirtschaftsingenieurwesen am Karlsruher Institut für Technologie und am Institut polytechnique de Grenoble. Seit 2016 arbeitet sie am FZI in der Abteilung Logistics Systems Engineering des Forschungsbereichs IPE. 2020 promovierte sie am Karlsruher Institut für Technologie (KIT) summa cum laude zum Thema 'Emergency rapid mapping with drones'.

Am FZI befasst Katharina Glock sich als Projektmitarbeiterin und Projektleiterin mit datengetriebener Entscheidungsfindung in verschiedenen Domänen, von der Logistik bis zur Ersterkundung im Katastrophenschutz. Ihr aktueller Forschungsschwerpunkt liegt in der Entwickung algorithmischer Lösungen für komplexe Systeme in einer dynamischen Umwelt.

Publikationen

Zeitungs- oder Zeitschriftenartikel (2)

1. Mission planning for emergency rapid mapping with dronesDetails

   Katharina Glock, Anne Meyer, 2020

2. A disruption management system for automotive inbound networks: concepts and challengesDetails

   Anne Meyer, Suad Sejdovic, Katharina Glock, Matthias Bender, Natalja Kleiner, Dominik Riemer, 2017

Konferenzbeitrag (7)

1. Decentralized dynamic task allocation and route planning for autonomous delivery vehicles in urban areasDetails

   Katharina Glock, Anne Meyer, Martin Pouls, 2019

2. Micro Rapid Mapping: Automatic UAV-based Remote Sensing for Chemical EmergenciesDetails

   Katharina Glock, Anne Meyer, Bodo Bernsdorf, Sascha Woditsch, 2018

3. Correlated Orienteering for Planning Emergency Surveillance Flights of Unmanned Aerial VehiclesDetails

   Katharina Glock, Anne Meyer, Guido Tack, 2017

4. BigGIS - Einsatz von Flugrobotern zur Entscheidungsunterstützung bei Bränden Details

   Bodo Bernsdorf, Julian Bruns, Katharina Glock, 2017

5. Extensions to the Correlated Orienteering Problem for Emergency Surveillance Details

   Katharina Glock, Anne Meyer, Guido Tack, 2017

6. New techniques for Constraint Programming based heuristics for VRPInfoDetails

   Katharina Glock, Anne Meyer, Guido Tack, 2016

   In order to provide flexible services for solving vehicle routing problems (VRP), logistics professionals should be enabled to easily adapt the planning tool even without having a background in optimization. Constraint Programming (CP) combines an expressive modelling language and problem-independent search methods, thus making it a promising candidate for a planning engine for such a flexible service. Users can add or remove constraints without having to modify either the core model formulation or the solution methods. Large Neighbourhood Search (LNS) proposed by Shaw (1998) is the most frequently used heuristic for solving VRP in a CP framework. We introduce the basic solution architecture with particular consideration of CP specific features. We furthermore present current research combining LNS and Monte-Carlo tree search (MCTS), an algorithm originally proposed for decision making in non-deterministic games that aims at balancing the exploration of a search tree and the exploitation of sub-trees that are likely to contain good solutions. We apply MCTS for constructing the initial solution as well as for solving the sub-problems created by the LNS. Integrated into the CP framework, MCTS provides a robust search algorithm that adjusts itself to the properties of the problem at hand, thus ensuring the flexibility of the search framework. Results are promising with respect to quality and runtime for several problem classes such as the VRP with time windows, the VRP with pickup and delivery, and the periodic VRP with time windows.

7. A Monte Carlo Large Neighbourhood Search for Vehicle Routing ProblemsInfoDetails

   Katharina Glock, Anne Meyer, Guido Tack, 2016

   Optimization tools addressing large scale combinatorial optimization problems in a real-world setting need to be easily adaptable to application specific requirements. Solution procedures need to be fast and robust towards these adaptations. To this end, we propose a hybrid approach combining Monte Carlo Tree Search (MCTS) and Large Neighbourhood Search (LNS) in a CP framework. The integration of local search and a self-adjusting tree-based search strategy offers a flexible and efficient solution approach that can adapt itself to the problem at hand. First results for the vehicle routing problem (VRP) indicate that the proposed Monte Carlo Large Neighbourhood Search (MCLNS) yields better results than related CP-based approaches.

Thesis (1)

1. Emergency rapid mapping with drones: models and solution approaches for offline and online mission planningDetails

   Katharina Glock, 2020

Betreute Thesis (7)

1. Flugroutenmodellierung zur drohnengestützten Schadensermittlung im KatastrophenfallDetails

   Mathieu Teicht, 2020

2. Analysis of Handling Quality in Air FreightDetails

   Johannes Pesch, 2019

3. Forecasting non-compliant air freight shipmentsDetails

   Katja Habitzreither, 2019

4. Dezentrale Auftragssteuerung für autonome Zustellfahrzeuge im urbanen RaumDetails

   Felix Schreyer, 2019

5. Integrating dynamic programming and relaxed problem representations for solving the vehicle routing problem with profitsDetails

   Wei Su, 2018

6. Constraint-programming based solution of vehicle routing problems with time synchronisation using simple temporal network.Details

   Vladimir Solovyev, 2017

7. A CP-based return in time algorithm for the vehicle routing problemDetails

   Konstantin Zangerle, 2017

Whitepaper (1)

1. Planning profitable tours for field sales forces: A unified view on sales analytics and mathematical optimizationInfoDetails

   Anne Meyer, Katharina Glock, Frank Radaschewski

   Field sales forces play an important role in direct marketing, especially for companies offering complex products, services, or solutions in the business-to-business context. A key task of sales representatives in operational planning is to select the most promising customers to visit within the next days. On an operative horizon, a key task for sales representatives is to select the most promising customers to visit within the next days. A strongly varying set of scoring methods predicting or approximating the expected response exists for this customer selection phase. However, in the case of field sales forces, the final customer selection is strongly interrelated to the tour planning decisions. To this end, we formalize variants of the profitable sales representatives tour problem as a multi-period team orienteering problem, thereby providing a unified view on the customer scoring and the tour planning phase. In an extensive computational study on real-world instances from the retail industry, we systematically examine the impact of the aggregation level and the content of information provided by a scoring method and the sensitivity of the proposed models concerning prediction errors. We show that the selection of a customer scoring and tour planning variant depends on the available data. Furthermore, we work out where to put effort in the data acquisition and scoring phase to get better operational tours.

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Kontakt

Telefon: +49 721 9654-828
E-Mail: kglock@dont-want-spam.fzi.de

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