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.- 2.1 Numerical Results.- 2.2 Discussion.- 3. Bounded Genetic Local Search.- 3.1 Implementation Details and Numerical Results.- 3.2 Conclusions.- 4. Variable Depth Search Based Learning.- 4.1 Ejection Chains.- 4.2 Computational Results.- 4.3 Conclusions.- III. Job Shop Scheduling.- 1. Introduction Conventional and New Solution Techniques.- 2. Evolution Based Learning.- 2.1 Genetic Enumeration.- 2.2 Heuristics for the Job Shop Scheduling Problem.- 2.3 Learning by Population Genetics.- 2.4 Details of Implementation and Computational Results.- 2.5 Conclusions.- 3. Learning by Constraint Propagation.- 3.1 Constraint Propagation and Backtrack Search.- 3.2 The Job Shop Constraint Satisfaction Problem.- 3.3 An Immediate Selection Heuristic.- 3.4 Computational Results.- 3.5 Conclusions.- 4. Decomposition Based Learning.- 4.1 Opportunistic Scheduling Heuristics.- 4.2 Constraint Propagation, Local Consistency, and Genetilă™