DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Dongyun | - |
dc.contributor.author | Choi, Yeonjun | - |
dc.contributor.author | Moon, Kyungduk | - |
dc.contributor.author | Lee, Myungho | - |
dc.contributor.author | Lee, Kangbok | - |
dc.contributor.author | Pinedo, Michael L. | - |
dc.date.accessioned | 2023-11-02T07:22:02Z | - |
dc.date.available | 2023-11-02T07:22:02Z | - |
dc.date.created | 2023-10-25 | - |
dc.date.issued | 2023-06-01 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/119073 | - |
dc.description.abstract | We consider a steelmaking-continuous casting (SCC) scheduling problem in the steel industry, which is a variant of the hybrid flow shop scheduling problem subject to practical constraints. Recently, Hong et al. [Hong, J., Moon, K., Lee, K., Lee, K., Pinedo, M.L., International Journal of Production Research 60(2), 623-643 (2022)] developed an algorithm, called Iterated Greedy Matheuristic (IGM), in which a Mixed Integer Programming (MIP) model was proposed and its subproblems are iteratively solved to improve the solution. We propose a new constraint programming (CP) formulation for the SCC scheduling problem and develop an algorithm, called Iterated Greedy CP (IGC), which uses the framework of IGM but replaces the MIP model with our CP model. When we solve the CP subproblems iteratively, we also refine them by adding appropriate constraints, reducing the domains of the variables, and giving the variables hints derived from the current solution. From computational experiments in various settings, we show that IGC implemented with an open-source CP solver can be competitive with IGM running on a commercial MIP solver. | - |
dc.language | English | - |
dc.publisher | Université Côte d'Azur | - |
dc.relation.isPartOf | 20th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2023 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.title | Iterated Greedy Constraint Programming for Scheduling Steelmaking Continuous Casting | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.identifier.bibliographicCitation | 20th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2023, pp.477 - 492 | - |
dc.citation.conferenceDate | 2023-05-29 | - |
dc.citation.conferencePlace | FR | - |
dc.citation.conferencePlace | Nice, France | - |
dc.citation.endPage | 492 | - |
dc.citation.startPage | 477 | - |
dc.citation.title | 20th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2023 | - |
dc.contributor.affiliatedAuthor | Kim, Dongyun | - |
dc.contributor.affiliatedAuthor | Choi, Yeonjun | - |
dc.contributor.affiliatedAuthor | Moon, Kyungduk | - |
dc.contributor.affiliatedAuthor | Lee, Myungho | - |
dc.contributor.affiliatedAuthor | Lee, Kangbok | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
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