Please use this identifier to cite or link to this item: https://doi.org/10.3390/su13105391
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dc.titleForecasting of disassembly waste generation under uncertainties using digital twinning-based hidden markov model
dc.contributor.authorYang, Yinsheng
dc.contributor.authorYuan, Gang
dc.contributor.authorCai, Jiaxiang
dc.contributor.authorWei, Silin
dc.date.accessioned2022-10-11T07:58:26Z
dc.date.available2022-10-11T07:58:26Z
dc.date.issued2021-05-12
dc.identifier.citationYang, Yinsheng, Yuan, Gang, Cai, Jiaxiang, Wei, Silin (2021-05-12). Forecasting of disassembly waste generation under uncertainties using digital twinning-based hidden markov model. Sustainability (Switzerland) 13 (10) : 5391. ScholarBank@NUS Repository. https://doi.org/10.3390/su13105391
dc.identifier.issn2071-1050
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/232083
dc.description.abstractDisassembly waste generation forecasting is the foundation for determining disassembly waste treatment and process formulation and is also an important prerequisite for optimizing waste management. The prediction of disassembly waste generation is a complex process which is affected by potential time, environment, and economy characteristic variables. Uncertainty features, such as disassembly amount, disassembly component status, and workshop scheduling, play an important role in predicting the fluctuation of disassembly waste generation. We therefore focus on revealing the trend of waste generation in disassembly remanufacturing that faces significant influences of technology and economic changes to achieve circular industry sustainable development. To dynamically predict the generation of disassembly waste under uncertainty, this work proposes a statistical method driven by a probabilistic model, which integrates the digital twinning, Gaussian mixture, and the hidden Markov model (DG-HMM). First, digital twinning technology is used for real-time data interaction between simulation prediction and decision evaluation. Then, the Gaussian mixture and HMM are used to dynamically predict the generation of disassembly waste. In order to effectively predict the amount of disassembly waste generation, real data collected from a disassembly enterprise are used to train and verify the model. Finally, the proposed model is compared with other general prediction models to illustrate the correctness and feasibility of the proposed model. The comparison results show that DG-HMM has better prediction accuracy for the actual disassembly waste generation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
dc.publisherMDPI AG
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2021
dc.subjectDG-HMM
dc.subjectDigital twinning
dc.subjectDisassembly
dc.subjectOptimization
dc.subjectReal-time interaction
dc.subjectWaste forecasting
dc.typeArticle
dc.contributor.departmentDEPT OF INDUSTRIAL SYSTEMS ENGG & MGT
dc.description.doi10.3390/su13105391
dc.description.sourcetitleSustainability (Switzerland)
dc.description.volume13
dc.description.issue10
dc.description.page5391
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