While recent efforts to combine optimization and constraint satisfaction have received considerable attention, little has been said about using logic in optimization as the key to unifying the two fields. Logic–Based Methods for Optimization develops for the first time a comprehensive conceptual framework for integrating optimization and constraint satisfaction, then goes a step further and shows how extending logical inference to optimization allows for more powerful as well as flexible modeling and solution techniques. Designed to be easily accessible to industry professionals and academics in both operations research and artificial intelligence, the book provides a wealth of examples as well as elegant techniques and modeling frameworks ready for implementation. Timely, original, and thought–provoking, Logic–Based Methods for Optimization:
∗ Demonstrates the advantages of combining the techniques in problem solving
∗ Offers tutorials in constraint satisfaction/constraint programming and logical inference
∗ Clearly explains such concepts as relaxation, cutting planes, nonserial dynamic programming, and Bender′s decomposition
∗ Reviews the necessary technologies for software developers seeking to combine the two techniques
∗ Features extensive references to important computational studies
∗ And much more
The Logic of Propositions.
The Logic of Discrete Variables.
The Logic of 0–1 Inequalities.
Classical Boolean Methods.
Logic–Based Branch and Bound.
Logic–Based Benders Decomposition.
Nonserial Dynamic Programming.