Learning in Automated Manufacturing A Local Search Approach

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 constraint based reasoning, both with respect to their use in automated manufacturing. We restrict our attention 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 scheduling systems, enriched by a number of constraint-based local decision rules in order to introduce problem specific knowledge.

I. Local Search and Extensions.- 1. Introduction — Local Search.- 2. Infamous Scheduling Problems.- 3. Simulated Annealing.- 4. Tabu Search.- 5. Genetic Algorithms.- II. The Traveling Salesman Problem.- 1. Introduction and Survey.- 2. Effective Genetic Local Search.- 3. Bounded Genetic Local Search.- 4. Variable Depth Search Based Learning.- III. Job Shop Scheduling.- 1. Introduction — Conventional and New Solution Techniques.- 2. Evolution Based Learning.- 3. Learning by Constraint Propagation.- 4. Decomposition Based Learning.- IV. Flexible Manufacturing Systems.- 1. Clustering in Cellular Manufacturing.- 2. Factory Layout Planning.- 3. Workload Balancing.- Epilogue.- References.