Constraint logic programming (CLP), which combines the complementary strengths of the artificial intelligence (AI) and OR approaches, is introduced as a new tool for formalizing constraint satisfaction problems that include both qualitative and quantitative constraints. CLP(R), one CLP language, is used to contrast the CLP approach with mixed integer programming (MIP). Three relative advantages of CLP over MIP are analyzed: representational efficiency for domain-specific knowledge; partial solutions; and ease of model revision. A case example of constraint satisfaction problems is implemented by MIP and CLP(R) for comparison of the two approaches. The results exhibit the representational economics of CLP with computational efficiency comparable to that of MIP.
|Title of host publication||Proceedings of the 26th Hawaii International Conference on System Sciences, HICSS 1993|
|Publisher||IEEE Computer Society|
|Number of pages||10|
|Publication status||Published - 1993|
|Event||26th Hawaii International Conference on System Sciences, HICSS 1993 - Wailea, United States|
Duration: 1993 Jan 8 → …
|Name||Proceedings of the Annual Hawaii International Conference on System Sciences|
|Conference||26th Hawaii International Conference on System Sciences, HICSS 1993|
|Period||93/1/8 → …|
Bibliographical notePublisher Copyright:
© 1993 IEEE.
All Science Journal Classification (ASJC) codes