TY  - JOUR
A1  - Lesch, Veronika
A1  - König, Maximilian
A1  - Kounev, Samuel
A1  - Stein, Anthony
A1  - Krupitzer, Christian
T1  - Tackling the rich vehicle routing problem with nature-inspired algorithms
JF  - Applied Intelligence
N2  - In the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.
KW  - logistics
KW  - rich vehicle routing problem
KW  - ant-colony optimization
KW  - genetic algorithm
KW  - real-world application
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-268942
SN  - 1573-7497
VL  - 52
ER  - 
TY  - JOUR
A1  - Kaiser, Dennis
A1  - Lesch, Veronika
A1  - Rothe, Julian
A1  - Strohmeier, Michael
A1  - Spieß, Florian
A1  - Krupitzer, Christian
A1  - Montenegro, Sergio
A1  - Kounev, Samuel
T1  - Towards Self-Aware Multirotor Formations
JF  - Computers
N2  - In the present day, unmanned aerial vehicles become seemingly more popular every year, but, without regulation of the increasing number of these vehicles, the air space could become chaotic and uncontrollable. In this work, a framework is proposed to combine self-aware computing with multirotor formations to address this problem. The self-awareness is envisioned to improve the dynamic behavior of multirotors. The formation scheme that is implemented is called platooning, which arranges vehicles in a string behind the lead vehicle and is proposed to bring order into chaotic air space. Since multirotors define a general category of unmanned aerial vehicles, the focus of this thesis are quadcopters, platforms with four rotors. A modification for the LRA-M self-awareness loop is proposed and named Platooning Awareness. The implemented framework is able to offer two flight modes that enable waypoint following and the self-awareness module to find a path through scenarios, where obstacles are present on the way, onto a goal position. The evaluation of this work shows that the proposed framework is able to use self-awareness to learn about its environment, avoid obstacles, and can successfully move a platoon of drones through multiple scenarios.
KW  - self-aware computing
KW  - unmanned aerial vehicles
KW  - multirotors
KW  - quadcopters
KW  - intelligent transportation systems
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200572
SN  - 2073-431X
VL  - 9
IS  - 1
ER  -