Katharina Glock

Wissenschaftliche Mitarbeiterin

Werdegang

Katharina Glock studierte Wirtschaftsingenieurwesen am Karlsruher Institut für Technologie und am Institut polytechnique de Grenoble. Zu ihren Studienschwerpunkten gehörten Produktions- und Logistikmanagement, Operations Research und Netzwerkökonomie. Seit 2016 arbeitet sie am FZI in der Abteilung Logistics Systems Engineering des Forschungsbereichs IPE. Hier beschäftigt sie sich insbesondere mit Problem der kombinatorischen Optimierung.

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.

Betreute Thesis (6)

1. Analysis of Handling Quality in Air FreightDetails

   Johannes Pesch, 2019

2. Forecasting non-compliant air freight shipmentsDetails

   Katja Habitzreither, 2019

3. Dezentrale Auftragssteuerung für autonome Zustellfahrzeuge im urbanen RaumDetails

   Felix Schreyer, 2019

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

   Wei Su, 2018

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

   Vladimir Solovyev, 2017

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

   Konstantin Zangerle, 2017

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Kontakt

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

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