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.",