CONSTRAINT LOGIC PROGRAMMING FOR QUALITATIVE AND QUANTITATIVE CONSTRAINT SATISFACTION PROBLEMS

AI and OR approaches have complementary strengths: AI in domain-specif ic knowledge representation and OR in efficient mathematical computati on. Constraint Logic Programming (CLP), which combines these complemen tary strengths of the AI and OR approach, is introduced as a new tool to formalize a special class of constraint satisfaction problems that include both qualitative and quantitative constraints. The CLP approac h is contrasted with the Mixed Integer Programming (MIP) method from a model-theoretic view. Three relative advantages of CLP over MIP are a nalyzed: (1) representational economies for domain-specific heuristics , (2) partial solutions, and (3) ease of model revision. A case exampl e of constraint satisfaction problems is implemented by MIP and CLP fo r comparison of the two approaches. The results exhibit those relative advantages of CLP with computational efficiency comparable to MIP.