Authors:
David Woller
1
;
2
and
Miroslav Kulich
1
Affiliations:
1
Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Jugoslávských partyzánů 1580/3, 160 00 Praha 6, Czech Republic
;
2
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Karlovo náměstí 13, 121 35 Praha 2, Czech Republic
Keyword(s):
Adaptive Large Neighborhood Search, Metaheuristics, Combinatorial Optimization, Maintanance Scheduling, ROADEF 2020.
Abstract:
Transmission maintenance scheduling (TMS) is an important optimization problem in the electricity distribution industry, with numerous variants studied and methods proposed over the last three decades. The ROADEF challenge 2020 addresses a novel version of the TMS problem, which stands out by having multiple time-dependent properties, constraints, and a risk-based aggregate objective function. Therefore, the problem is more complex than the previous formulations, and the existing methods are not directly applicable. This paper presents a method based on the Adaptive Large Neighborhood Search metaheuristic. The method is compared with the best-known solutions from the challenge qualification phase, in which more than 70 teams participated. The result shows that the method yields consistent performance over the whole dataset, as the method finds the best-known solutions for half of the dataset and finds solutions consistently within 5‰ gap.