- author = "Artur Ferreira Brum",
- title = "Automatic Algorithm Configuration for Flow Shop Scheduling Problems",
- school = "Instituto de Informatica, Universidade Federal do Rio Grande do Sul",
- year = "2020",
- address = "Porto Alegre, Brazil",
- month = aug,
- keywords = "genetic algorithms, genetic programming, Informatica, Automatic algorithm configuration, Flow shop scheduling problem, Iterated greedy algorithm, Iterated local search, Problema de agendamento em flow shop, Configuracao automatica dealgoritmos, Busca local iterada, Algoritmo guloso iterado",
-
URL = "
https://www.inf.ufrgs.br/site/eventos/evento/tese-de-doutorado-de-artur-ferreira-brum/",
-
URL = "http://hdl.handle.net/10183/213705",
-
URL = "https://lume.ufrgs.br/handle/10183/213705",
-
URL = "
https://www.lume.ufrgs.br/bitstream/handle/10183/213705/001118296.pdf",
- size = "130 pages",
- abstract = "Scheduling problems have been a subject of interest to the optimization researchers for many years. Flow shop problems, in particular, are one of the most widely studied scheduling problems due to their application to many production environments. A large variety of solution methods can be found in the literature and, since many flow shop problems are NP-hard, the most frequently found approaches are heuristic methods. Heuristic search methods are often complex and hard to design, requiring a significant amount of time and manual work to perform such a task, which can be tedious and prone to human biases. Automatic algorithm configuration (AAC) comprises techniques to automate the design of algorithms by selecting and calibrating algorithmic components. It provides a more robust approach which can contribute to improving the state of the art. In this thesis we present a study on the permutation and the non-permutation flow shop scheduling problems. We follow a grammar-based AAC strategy to generate iterated local search or iterated greedy algorithms. We implement several algorithmic components from the literature in a parameterised solver, and explore the search space defined by the grammar with a racing-based strategy. New efficient algorithms are designed with minimal manual effort and are evaluated against benchmarks from the literature. The results show that the automatically designed algorithms can improve the state of the art in many cases, as evidenced by comprehensive computational and statistical testing.",
-
notes = "
Supervisor: Marcus Peter Ritt",
