Dynamic production scheduling model under due date uncertainty in precast concrete construction
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Taehoon | - |
dc.contributor.author | Kim, Yong-woo | - |
dc.contributor.author | Cho, Hunhee | - |
dc.date.accessioned | 2021-08-30T21:30:11Z | - |
dc.date.available | 2021-08-30T21:30:11Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020-06-01 | - |
dc.identifier.issn | 0959-6526 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/55076 | - |
dc.description.abstract | Precast concrete structures (PCs) are widely used in the construction industry to reduce project delivery times and improve quality. On-time delivery of PCs is critical for successful project completion because the processes involving precast concrete are the critical paths in most cases. However, existing models for scheduling PC production are not adequate for use in dynamic environments where construction projects have uncertain construction schedules because of various reasons such as poor labor productivity, inadequate equipment, and poor weather. This research proposes a dynamic model for PC production scheduling by adopting a discrete-time simulation method to respond to due date changes in real time and by using a new dispatching rule that considers the uncertainty of the due dates to minimize tardiness. The model is validated by simulation experiments based on various scenarios with different levels of tightness and due date uncertainty. The results of this research will contribute to construction project productivity with a reliable and economic precast concrete supply chain. (C) 2020 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.subject | MULTIPLE PRODUCTION | - |
dc.subject | DEMAND VARIABILITY | - |
dc.subject | SUPPLY CHAIN | - |
dc.subject | SHOP | - |
dc.subject | MANAGEMENT | - |
dc.subject | MINIMIZE | - |
dc.subject | LINES | - |
dc.title | Dynamic production scheduling model under due date uncertainty in precast concrete construction | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Taehoon | - |
dc.contributor.affiliatedAuthor | Cho, Hunhee | - |
dc.identifier.doi | 10.1016/j.jclepro.2020.120527 | - |
dc.identifier.scopusid | 2-s2.0-85079560259 | - |
dc.identifier.wosid | 000522383500058 | - |
dc.identifier.bibliographicCitation | JOURNAL OF CLEANER PRODUCTION, v.257 | - |
dc.relation.isPartOf | JOURNAL OF CLEANER PRODUCTION | - |
dc.citation.title | JOURNAL OF CLEANER PRODUCTION | - |
dc.citation.volume | 257 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalWebOfScienceCategory | Green & Sustainable Science & Technology | - |
dc.relation.journalWebOfScienceCategory | Engineering, Environmental | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.subject.keywordPlus | MULTIPLE PRODUCTION | - |
dc.subject.keywordPlus | DEMAND VARIABILITY | - |
dc.subject.keywordPlus | SUPPLY CHAIN | - |
dc.subject.keywordPlus | SHOP | - |
dc.subject.keywordPlus | MANAGEMENT | - |
dc.subject.keywordPlus | MINIMIZE | - |
dc.subject.keywordPlus | LINES | - |
dc.subject.keywordAuthor | Precast concrete production | - |
dc.subject.keywordAuthor | Dynamic simulation | - |
dc.subject.keywordAuthor | Uncertainty | - |
dc.subject.keywordAuthor | Production scheduling | - |
dc.subject.keywordAuthor | Dispatching rule | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.