Abstract
AbstractThis chapter outlines some successful frameworks for combinatorial optimization. It is meant as a brief introduction into terminology and outlines the basic principles of the frameworks which are applied in the subsequent case studies. The three frameworks that will be discussed, integer linear programming (ILP), finite domain constraint programming (CP) and local search are well established, comprise of a variety of techniques, and many successful applications have been reported. ILP and CP can be considered the state-of-the-art of general-purpose optimization methods, whereas local search should be seen as an approach that can be tailored to many different optimization problems by adapting its conceptual components to the respective problem context. We also discuss successful local search methods for solving propositional satisfiability problems.KeywordsLocal SearchInteger Linear ProgrammingConstraint ProgrammingConstraint Satisfaction ProblemLinear RelaxationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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