Constraint logic programming for qualitative and quantitative constraint satisfaction problems

Ho Geun Lee, Ronald M. Lee, Gang Yu

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


AI and OR approaches have complementary strengths: AI in domain-specific knowledge representation and OR in efficient mathematical computation. Constraint Logic Programming (CLP), which combines these complementary 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 approach is contrasted with the Mixed Integer Programming (MIP) method from a model-theoretic view. Three relative advantages 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.

Original languageEnglish
Pages (from-to)67-83
Number of pages17
JournalDecision Support Systems
Issue number1
Publication statusPublished - 1996 Jan

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management


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