Constraint logic programming for qualitative and quantitative constraint satisfaction problems

AI and Operational Research OR approaches have complementary strengths: AI in domain-specific knowledge representation and Operational Research OR in efficient mathematical computation. Constraint Logic Programming (CLP), which combines these complementary strengths of the AI and Operational Research OR approach, is introduced as a new tool to formalize a special class of constraint satisfaction problems that include both qualitative and quantatitive constraints. The CLP approach is contrasted with the Mixed Integer Programming (MIP) method from a model-theoretic view. Three relative adavntages of CLP over MIP are analyzed: (1) representational economies for domain-specific heuristics; (2) partial solutions; and (3) ease of model revision. A case example of constraint satisfaction problems is implemented by MIP and CLP for comparison of the two approaches. The results exhibit those relative advantages of CLP with computational efficiency comparable to MIP.