Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91586
DC FieldValueLanguage
dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorZhang, Gen_US
dc.creatorWang, Gen_US
dc.creatorChen, CHen_US
dc.creatorCao, Xen_US
dc.creatorZhang, Yen_US
dc.creatorZheng, Pen_US
dc.date.accessioned2021-11-09T03:07:57Z-
dc.date.available2021-11-09T03:07:57Z-
dc.identifier.issn0736-5845en_US
dc.identifier.urihttp://hdl.handle.net/10397/91586-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.subjectSmart manufacturing service (SMS)en_US
dc.subjectEnergy consumptionen_US
dc.subjectSMS allocationen_US
dc.subjectAugmented Lagrangian coordination (ALC)en_US
dc.titleAugmented Lagrangian coordination for energy-optimal allocation of smart manufacturing servicesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume71en_US
dc.identifier.doi10.1016/j.rcim.2021.102161en_US
dcterms.abstractThe rapid development of information and communication technologies has triggered the proposition and implementation of smart manufacturing paradigms. In this regard, efficient allocation of smart manufacturing services (SMSs) can provide a sustainable manner for promoting cleaner production. Currently, centralized optimization methods have been widely used to complete the optimal allocation of SMSs. However, personalized manufacturing tasks usually belong to diverse production domains. The centralized optimization methods could hardly include related production knowledge of all manufacturing tasks in an individual decision model. Consequently, it is difficult to provide satisfactory SMSs for meeting customer's requirements. In addition, energy consumption is rarely considered in the SMS allocation process which is unfavorable for performing sustainable manufacturing. To address these challenges, augmented Lagrangian coordination (ALC), a novel distributed optimization method is proposed to deal with the energy-optimal SMS allocation problem in this paper. The energy-optimal SMS allocation model is constructed and decomposed into several loose-coupled and distributed elements. Two variants of the ALC method are implemented to formulate the proposed problem and obtain final SMS allocation results. A case study is employed to verify the superiority of the proposed method in dealing with energy-optimal SMS allocation problems by comparing with the centralized optimization method at last.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationRobotics and computer - integrated manufacturing, Oct. 2021, v. 71, 102161en_US
dcterms.isPartOfRobotics and computer - integrated manufacturingen_US
dcterms.issued2021-10-
dc.identifier.isiWOS:000663337200001-
dc.identifier.artn102161en_US
dc.description.validate202111 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1047-n03-
dc.identifier.SubFormID43844-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Nature Science Foundation of China (U2001201, 51875451, 51834006) and the 111 Project Grant (B13044)，the National Research Foundation (NRF) Singapore and Delta Electronics International (Singapore) Pte Ltd., under the Corporate Laboratory@ University Scheme (Ref. SCO-RP1; RCA-16/434) at Nanyang Technological University, Singapore.en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2023-10-31en_US
Appears in Collections:Journal/Magazine Article
Open Access Information
Status embargoed access
Embargo End Date 2023-10-31
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

59
Last Week
0
Last month
Citations as of Sep 24, 2023

SCOPUSTM   
Citations

9
Citations as of Sep 21, 2023

WEB OF SCIENCETM
Citations

7
Citations as of Sep 21, 2023

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.