Constraint logic programming and mixed integer programming

Ho Geun Lee; Ronald M. Lee; Gang Yu

doi:10.1109/HICSS.1993.284354

Constraint logic programming and mixed integer programming

Ho Geun Lee, Ronald M. Lee, Gang Yu

Department of Business Administration

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 26th Hawaii International Conference on System Sciences, HICSS 1993
Publisher	IEEE Computer Society
Pages	543-552
Number of pages	10
ISBN (Electronic)	0818632305
DOIs	https://doi.org/10.1109/HICSS.1993.284354
Publication status	Published - 1993
Event	26th Hawaii International Conference on System Sciences, HICSS 1993 - Wailea, United States Duration: 1993 Jan 8 → …

Publication series

Name	Proceedings of the Annual Hawaii International Conference on System Sciences
Volume	3
ISSN (Print)	1530-1605

Conference

Conference	26th Hawaii International Conference on System Sciences, HICSS 1993
Country/Territory	United States
City	Wailea
Period	93/1/8 → …

Bibliographical note

Publisher Copyright:
© 1993 IEEE.

All Science Journal Classification (ASJC) codes

Engineering(all)

Access to Document

10.1109/HICSS.1993.284354

Cite this

Lee, H. G., Lee, R. M., & Yu, G. (1993). Constraint logic programming and mixed integer programming. In Proceedings of the 26th Hawaii International Conference on System Sciences, HICSS 1993 (pp. 543-552). Article 284354 (Proceedings of the Annual Hawaii International Conference on System Sciences; Vol. 3). IEEE Computer Society. https://doi.org/10.1109/HICSS.1993.284354

@inproceedings{65b34a9c6cb1460cb079d543389cdc9e,

title = "Constraint logic programming and mixed integer programming",

abstract = "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.",

author = "Lee, {Ho Geun} and Lee, {Ronald M.} and Gang Yu",

note = "Publisher Copyright: {\textcopyright} 1993 IEEE.; 26th Hawaii International Conference on System Sciences, HICSS 1993 ; Conference date: 08-01-1993",

year = "1993",

doi = "10.1109/HICSS.1993.284354",

language = "English",

series = "Proceedings of the Annual Hawaii International Conference on System Sciences",

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pages = "543--552",

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Lee, HG, Lee, RM & Yu, G 1993, Constraint logic programming and mixed integer programming. in Proceedings of the 26th Hawaii International Conference on System Sciences, HICSS 1993., 284354, Proceedings of the Annual Hawaii International Conference on System Sciences, vol. 3, IEEE Computer Society, pp. 543-552, 26th Hawaii International Conference on System Sciences, HICSS 1993, Wailea, United States, 93/1/8. https://doi.org/10.1109/HICSS.1993.284354

Constraint logic programming and mixed integer programming. / Lee, Ho Geun; Lee, Ronald M.; Yu, Gang.
Proceedings of the 26th Hawaii International Conference on System Sciences, HICSS 1993. IEEE Computer Society, 1993. p. 543-552 284354 (Proceedings of the Annual Hawaii International Conference on System Sciences; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Constraint logic programming and mixed integer programming

AU - Lee, Ho Geun

AU - Lee, Ronald M.

AU - Yu, Gang

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N2 - 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.

AB - 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.

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PB - IEEE Computer Society

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Constraint logic programming and mixed integer programming

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