DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Duyeon | ko |
dc.contributor.author | Kim, Hyunjung | ko |
dc.date.accessioned | 2023-01-05T02:00:55Z | - |
dc.date.available | 2023-01-05T02:00:55Z | - |
dc.date.created | 2022-12-29 | - |
dc.date.created | 2022-12-29 | - |
dc.date.issued | 2022-12-12 | - |
dc.identifier.citation | Winter Simulation Conference 2022, pp.3309 - 3317 | - |
dc.identifier.issn | 0891-7736 | - |
dc.identifier.uri | http://hdl.handle.net/10203/303992 | - |
dc.description.abstract | A dynamic job shop scheduling problem where jobs are transported by automated guided vehicles (AGVs) is considered to minimize the mean flow time. This problem is first modeled with a timed Petri net (TPN) which is widely used for modeling and analyzing discrete event systems. A firing rule of transitions in a TPN is modified to derive more efficient schedules by considering jobs that have not arrived yet and restricting the unnecessary movement of the AGVs. We propose a Monte Carlo Tree Search (MCTS)-based algorithm for the problem, which searches for schedules in advance within a time given limit. The proposed method shows better performance than combinations of other dispatching rules. | - |
dc.language | English | - |
dc.publisher | INFORMS-SIM, ACM/SIGSIM, IISE | - |
dc.title | MONTE CARLO TREE SEARCH-BASED ALGORITHM FOR DYNAMIC JOB SHOP SCHEDULING WITH AUTOMATED GUIDED VEHICLES | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85147413412 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 3309 | - |
dc.citation.endingpage | 3317 | - |
dc.citation.publicationname | Winter Simulation Conference 2022 | - |
dc.identifier.conferencecountry | SI | - |
dc.identifier.conferencelocation | Marina Bay Sands Hotel | - |
dc.identifier.doi | 10.1109/WSC57314.2022.10015352 | - |
dc.contributor.localauthor | Kim, Hyunjung | - |
dc.contributor.nonIdAuthor | Kim, Duyeon | - |
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