Learning in Automated Manufacturing

The central purpose of this book is to acquaint the reader especially with the cases of local-search-based learning, as well as to introduce methods of contraint-based reasoning, both with respect to their use in automated manufacturing. Attention is restricted to job-shop scheduling, as well as to one-machine scheduling with sequence-dependent setup times. Additionally, some design and planning issues in flexible manufacturing systems are considered. General-purpose search methods - which in particular include methods from local search, such as simulated annealing, tabu search and genetic algorithms - are the basic ingredients of the proposed intelligent knowledge-based local decision rules in order to introduce problem-specific knowledge.